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214 Best Big Data Research Topics for Your Thesis Paper

Finding an ideal big data research topic can take you a long time. Big data, IoT, and robotics have evolved. The future generations will be immersed in major technologies that will make work easier. Work that was done by 10 people will now be done by one person or a machine. This is amazing because, in as much as there will be job loss, more jobs will be created. It is a win-win for everyone.

Big data is a major topic that is being embraced globally. Data science and analytics are helping institutions, governments, and the private sector. We will share with you the best big data research topics.

On top of that, we can offer you the best writing tips to ensure you prosper well in your academics. As students in the university, you need to do proper research to get top grades. Hence, you can consult us if in need of research paper writing services.

Big Data Analytics Research Topics for your Research Project

Are you looking for an ideal big data analytics research topic? Once you choose a topic, consult your professor to evaluate whether it is a great topic. This will help you to get good grades.

Which are the best tools and software for big data processing?
Evaluate the security issues that face big data.
An analysis of large-scale data for social networks globally.
The influence of big data storage systems.
The best platforms for big data computing.
The relation between business intelligence and big data analytics.
The importance of semantics and visualization of big data.
Analysis of big data technologies for businesses.
The common methods used for machine learning in big data.
The difference between self-turning and symmetrical spectral clustering.
The importance of information-based clustering.
Evaluate the hierarchical clustering and density-based clustering application.
How is data mining used to analyze transaction data?
The major importance of dependency modeling.
The influence of probabilistic classification in data mining.

Interesting Big Data Analytics Topics

Who said big data had to be boring? Here are some interesting big data analytics topics that you can try. They are based on how some phenomena are done to make the world a better place.

Discuss the privacy issues in big data.
Evaluate the storage systems of scalable in big data.
The best big data processing software and tools.
Data mining tools and techniques are popularly used.
Evaluate the scalable architectures for parallel data processing.
The major natural language processing methods.
Which are the best big data tools and deployment platforms?
The best algorithms for data visualization.
Analyze the anomaly detection in cloud servers
The scrutiny normally done for the recruitment of big data job profiles.
The malicious user detection in big data collection.
Learning long-term dependencies via the Fourier recurrent units.
Nomadic computing for big data analytics.
The elementary estimators for graphical models.
The memory-efficient kernel approximation.

Big Data Latest Research Topics

Do you know the latest research topics at the moment? These 15 topics will help you to dive into interesting research. You may even build on research done by other scholars.

Evaluate the data mining process.
The influence of the various dimension reduction methods and techniques.
The best data classification methods.
The simple linear regression modeling methods.
Evaluate the logistic regression modeling.
What are the commonly used theorems?
The influence of cluster analysis methods in big data.
The importance of smoothing methods analysis in big data.
How is fraud detection done through AI?
Analyze the use of GIS and spatial data.
How important is artificial intelligence in the modern world?
What is agile data science?
Analyze the behavioral analytics process.
Semantic analytics distribution.
How is domain knowledge important in data analysis?

Big Data Debate Topics

If you want to prosper in the field of big data, you need to try even hard topics. These big data debate topics are interesting and will help you to get a better understanding.

The difference between big data analytics and traditional data analytics methods.
Why do you think the organization should think beyond the Hadoop hype?
Does the size of the data matter more than how recent the data is?
Is it true that bigger data are not always better?
The debate of privacy and personalization in maintaining ethics in big data.
The relation between data science and privacy.
Do you think data science is a rebranding of statistics?
Who delivers better results between data scientists and domain experts?
According to your view, is data science dead?
Do you think analytics teams need to be centralized or decentralized?
The best methods to resource an analytics team.
The best business case for investing in analytics.
The societal implications of the use of predictive analytics within Education.
Is there a need for greater control to prevent experimentation on social media users without their consent?
How is the government using big data; for the improvement of public statistics or to control the population?

University Dissertation Topics on Big Data

Are you doing your Masters or Ph.D. and wondering the best dissertation topic or thesis to do? Why not try any of these? They are interesting and based on various phenomena. While doing the research ensure you relate the phenomenon with the current modern society.

The machine learning algorithms are used for fall recognition.
The divergence and convergence of the internet of things.
The reliable data movements using bandwidth provision strategies.
How is big data analytics using artificial neural networks in cloud gaming?
How is Twitter accounts classification done using network-based features?
How is online anomaly detection done in the cloud collaborative environment?
Evaluate the public transportation insights provided by big data.
Evaluate the paradigm for cancer patients using the nursing EHR to predict the outcome.
Discuss the current data lossless compression in the smart grid.
How does online advertising traffic prediction helps in boosting businesses?
How is the hyperspectral classification done using the multiple kernel learning paradigm?
The analysis of large data sets downloaded from websites.
How does social media data help advertising companies globally?
Which are the systems recognizing and enforcing ownership of data records?
The alternate possibilities emerging for edge computing.

The Best Big Data Analysis Research Topics and Essays

There are a lot of issues that are associated with big data. Here are some of the research topics that you can use in your essays. These topics are ideal whether in high school or college.

The various errors and uncertainty in making data decisions.
The application of big data on tourism.
The automation innovation with big data or related technology
The business models of big data ecosystems.
Privacy awareness in the era of big data and machine learning.
The data privacy for big automotive data.
How is traffic managed in defined data center networks?
Big data analytics for fault detection.
The need for machine learning with big data.
The innovative big data processing used in health care institutions.
The money normalization and extraction from texts.
How is text categorization done in AI?
The opportunistic development of data-driven interactive applications.
The use of data science and big data towards personalized medicine.
The programming and optimization of big data applications.

The Latest Big Data Research Topics for your Research Proposal

Doing a research proposal can be hard at first unless you choose an ideal topic. If you are just diving into the big data field, you can use any of these topics to get a deeper understanding.

The data-centric network of things.
Big data management using artificial intelligence supply chain.
The big data analytics for maintenance.
The high confidence network predictions for big biological data.
The performance optimization techniques and tools for data-intensive computation platforms.
The predictive modeling in the legal context.
Analysis of large data sets in life sciences.
How to understand the mobility and transport modal disparities sing emerging data sources?
How do you think data analytics can support asset management decisions?
An analysis of travel patterns for cellular network data.
The data-driven strategic planning for citywide building retrofitting.
How is money normalization done in data analytics?
Major techniques used in data mining.
The big data adaptation and analytics of cloud computing.
The predictive data maintenance for fault diagnosis.

Interesting Research Topics on A/B Testing In Big Data

A/B testing topics are different from the normal big data topics. However, you use an almost similar methodology to find the reasons behind the issues. These topics are interesting and will help you to get a deeper understanding.

How is ultra-targeted marketing done?
The transition of A/B testing from digital to offline.
How can big data and A/B testing be done to win an election?
Evaluate the use of A/B testing on big data
Evaluate A/B testing as a randomized control experiment.
How does A/B testing work?
The mistakes to avoid while conducting the A/B testing.
The most ideal time to use A/B testing.
The best way to interpret results for an A/B test.
The major principles of A/B tests.
Evaluate the cluster randomization in big data
The best way to analyze A/B test results and the statistical significance.
How is A/B testing used in boosting businesses?
The importance of data analysis in conversion research
The importance of A/B testing in data science.

Amazing Research Topics on Big Data and Local Governments

Governments are now using big data to make the lives of the citizens better. This is in the government and the various institutions. They are based on real-life experiences and making the world better.

Assess the benefits and barriers of big data in the public sector.
The best approach to smart city data ecosystems.
The big analytics used for policymaking.
Evaluate the smart technology and emergence algorithm bureaucracy.
Evaluate the use of citizen scoring in public services.
An analysis of the government administrative data globally.
The public values are found in the era of big data.
Public engagement on local government data use.
Data analytics use in policymaking.
How are algorithms used in public sector decision-making?
The democratic governance in the big data era.
The best business model innovation to be used in sustainable organizations.
How does the government use the collected data from various sources?
The role of big data for smart cities.
How does big data play a role in policymaking?

Easy Research Topics on Big Data

Who said big data topics had to be hard? Here are some of the easiest research topics. They are based on data management, research, and data retention. Pick one and try it!

Who uses big data analytics?
Evaluate structure machine learning.
Explain the whole deep learning process.
Which are the best ways to manage platforms for enterprise analytics?
Which are the new technologies used in data management?
What is the importance of data retention?
The best way to work with images is when doing research.
The best way to promote research outreach is through data management.
The best way to source and manage external data.
Does machine learning improve the quality of data?
Describe the security technologies that can be used in data protection.
Evaluate token-based authentication and its importance.
How can poor data security lead to the loss of information?
How to determine secure data.
What is the importance of centralized key management?

Unique IoT and Big Data Research Topics

Internet of Things has evolved and many devices are now using it. There are smart devices, smart cities, smart locks, and much more. Things can now be controlled by the touch of a button.

Evaluate the 5G networks and IoT.
Analyze the use of Artificial intelligence in the modern world.
How do ultra-power IoT technologies work?
Evaluate the adaptive systems and models at runtime.
How have smart cities and smart environments improved the living space?
The importance of the IoT-based supply chains.
How does smart agriculture influence water management?
The internet applications naming and identifiers.
How does the smart grid influence energy management?
Which are the best design principles for IoT application development?
The best human-device interactions for the Internet of Things.
The relation between urban dynamics and crowdsourcing services.
The best wireless sensor network for IoT security.
The best intrusion detection in IoT.
The importance of big data on the Internet of Things.

Big Data Database Research Topics You Should Try

Big data is broad and interesting. These big data database research topics will put you in a better place in your research. You also get to evaluate the roles of various phenomena.

The best cloud computing platforms for big data analytics.
The parallel programming techniques for big data processing.
The importance of big data models and algorithms in research.
Evaluate the role of big data analytics for smart healthcare.
How is big data analytics used in business intelligence?
The best machine learning methods for big data.
Evaluate the Hadoop programming in big data analytics.
What is privacy-preserving to big data analytics?
The best tools for massive big data processing
IoT deployment in Governments and Internet service providers.
How will IoT be used for future internet architectures?
How does big data close the gap between research and implementation?
What are the cross-layer attacks in IoT?
The influence of big data and smart city planning in society.
Why do you think user access control is important?

Big Data Scala Research Topics

Scala is a programming language that is used in data management. It is closely related to other data programming languages. Here are some of the best scala questions that you can research.

Which are the most used languages in big data?
How is scala used in big data research?
Is scala better than Java in big data?
How is scala a concise programming language?
How does the scala language stream process in real-time?
Which are the various libraries for data science and data analysis?
How does scala allow imperative programming in data collection?
Evaluate how scala includes a useful REPL for interaction.
Evaluate scala’s IDE support.
The data catalog reference model.
Evaluate the basics of data management and its influence on research.
Discuss the behavioral analytics process.
What can you term as the experience economy?
The difference between agile data science and scala language.
Explain the graph analytics process.

Independent Research Topics for Big Data

These independent research topics for big data are based on the various technologies and how they are related. Big data will greatly be important for modern society.

The biggest investment is in big data analysis.
How are multi-cloud and hybrid settings deep roots?
Why do you think machine learning will be in focus for a long while?
Discuss in-memory computing.
What is the difference between edge computing and in-memory computing?
The relation between the Internet of things and big data.
How will digital transformation make the world a better place?
How does data analysis help in social network optimization?
How will complex big data be essential for future enterprises?
Compare the various big data frameworks.
The best way to gather and monitor traffic information using the CCTV images
Evaluate the hierarchical structure of groups and clusters in the decision tree.
Which are the 3D mapping techniques for live streaming data.
How does machine learning help to improve data analysis?
Evaluate DataStream management in task allocation.
How is big data provisioned through edge computing?
The model-based clustering of texts.
The best ways to manage big data.
The use of machine learning in big data.

Is Your Big Data Thesis Giving You Problems?

These are some of the best topics that you can use to prosper in your studies. Not only are they easy to research but also reflect on real-time issues. Whether in University or college, you need to put enough effort into your studies to prosper. However, if you have time constraints, we can provide professional writing help. Are you looking for online expert writers? Look no further, we will provide quality work at a cheap price.

Research Topics & Ideas: Data Science

PS – This is just the start…

We know it’s exciting to run through a list of research topics, but please keep in mind that this list is just a starting point . These topic ideas provided here are intentionally broad and generic , so keep in mind that you will need to develop them further. Nevertheless, they should inspire some ideas for your project.

Research topics and ideas about data science and big data analytics

Data Science-Related Research Topics

Developing machine learning models for real-time fraud detection in online transactions.
The use of big data analytics in predicting and managing urban traffic flow.
Investigating the effectiveness of data mining techniques in identifying early signs of mental health issues from social media usage.
The application of predictive analytics in personalizing cancer treatment plans.
Analyzing consumer behavior through big data to enhance retail marketing strategies.
The role of data science in optimizing renewable energy generation from wind farms.
Developing natural language processing algorithms for real-time news aggregation and summarization.
The application of big data in monitoring and predicting epidemic outbreaks.
Investigating the use of machine learning in automating credit scoring for microfinance.
The role of data analytics in improving patient care in telemedicine.
Developing AI-driven models for predictive maintenance in the manufacturing industry.
The use of big data analytics in enhancing cybersecurity threat intelligence.
Investigating the impact of sentiment analysis on brand reputation management.
The application of data science in optimizing logistics and supply chain operations.
Developing deep learning techniques for image recognition in medical diagnostics.
The role of big data in analyzing climate change impacts on agricultural productivity.
Investigating the use of data analytics in optimizing energy consumption in smart buildings.
The application of machine learning in detecting plagiarism in academic works.
Analyzing social media data for trends in political opinion and electoral predictions.
The role of big data in enhancing sports performance analytics.
Developing data-driven strategies for effective water resource management.
The use of big data in improving customer experience in the banking sector.
Investigating the application of data science in fraud detection in insurance claims.
The role of predictive analytics in financial market risk assessment.
Developing AI models for early detection of network vulnerabilities.

Data Science Research Ideas (Continued)

The application of big data in public transportation systems for route optimization.
Investigating the impact of big data analytics on e-commerce recommendation systems.
The use of data mining techniques in understanding consumer preferences in the entertainment industry.
Developing predictive models for real estate pricing and market trends.
The role of big data in tracking and managing environmental pollution.
Investigating the use of data analytics in improving airline operational efficiency.
The application of machine learning in optimizing pharmaceutical drug discovery.
Analyzing online customer reviews to inform product development in the tech industry.
The role of data science in crime prediction and prevention strategies.
Developing models for analyzing financial time series data for investment strategies.
The use of big data in assessing the impact of educational policies on student performance.
Investigating the effectiveness of data visualization techniques in business reporting.
The application of data analytics in human resource management and talent acquisition.
Developing algorithms for anomaly detection in network traffic data.
The role of machine learning in enhancing personalized online learning experiences.
Investigating the use of big data in urban planning and smart city development.
The application of predictive analytics in weather forecasting and disaster management.
Analyzing consumer data to drive innovations in the automotive industry.
The role of data science in optimizing content delivery networks for streaming services.
Developing machine learning models for automated text classification in legal documents.
The use of big data in tracking global supply chain disruptions.
Investigating the application of data analytics in personalized nutrition and fitness.
The role of big data in enhancing the accuracy of geological surveying for natural resource exploration.
Developing predictive models for customer churn in the telecommunications industry.
The application of data science in optimizing advertisement placement and reach.

Recent Data Science-Related Studies

Below, we’ve included a selection of recent studies to help refine your thinking. These are actual studies, so they can provide some useful insight as to what a research topic looks like in practice.

Data Science in Healthcare: COVID-19 and Beyond (Hulsen, 2022)
Auto-ML Web-application for Automated Machine Learning Algorithm Training and evaluation (Mukherjee & Rao, 2022)
Survey on Statistics and ML in Data Science and Effect in Businesses (Reddy et al., 2022)
Visualization in Data Science VDS @ KDD 2022 (Plant et al., 2022)
An Essay on How Data Science Can Strengthen Business (Santos, 2023)
A Deep study of Data science related problems, application and machine learning algorithms utilized in Data science (Ranjani et al., 2022)
You Teach WHAT in Your Data Science Course?!? (Posner & Kerby-Helm, 2022)
Statistical Analysis for the Traffic Police Activity: Nashville, Tennessee, USA (Tufail & Gul, 2022)
Data Management and Visual Information Processing in Financial Organization using Machine Learning (Balamurugan et al., 2022)
A Proposal of an Interactive Web Application Tool QuickViz: To Automate Exploratory Data Analysis (Pitroda, 2022)
Applications of Data Science in Respective Engineering Domains (Rasool & Chaudhary, 2022)
Jupyter Notebooks for Introducing Data Science to Novice Users (Fruchart et al., 2022)
Towards a Systematic Review of Data Science Programs: Themes, Courses, and Ethics (Nellore & Zimmer, 2022)
Application of data science and bioinformatics in healthcare technologies (Veeranki & Varshney, 2022)
TAPS Responsibility Matrix: A tool for responsible data science by design (Urovi et al., 2023)
Data Detectives: A Data Science Program for Middle Grade Learners (Thompson & Irgens, 2022)
MACHINE LEARNING FOR NON-MAJORS: A WHITE BOX APPROACH (Mike & Hazzan, 2022)
COMPONENTS OF DATA SCIENCE AND ITS APPLICATIONS (Paul et al., 2022)
Analysis on the Application of Data Science in Business Analytics (Wang, 2022)

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37 Research Topics In Data Science To Stay On Top Of

February 22, 2024

As a data scientist, staying on top of the latest research in your field is essential.

The data science landscape changes rapidly, and new techniques and tools are constantly being developed.

To keep up with the competition, you need to be aware of the latest trends and topics in data science research.

In this article, we will provide an overview of 37 hot research topics in data science.

We will discuss each topic in detail, including its significance and potential applications.

These topics could be an idea for a thesis or simply topics you can research independently.

Stay tuned – this is one blog post you don’t want to miss!

37 Research Topics in Data Science

1.) predictive modeling.

Predictive modeling is a significant portion of data science and a topic you must be aware of.

Simply put, it is the process of using historical data to build models that can predict future outcomes.

Predictive modeling has many applications, from marketing and sales to financial forecasting and risk management.

As businesses increasingly rely on data to make decisions, predictive modeling is becoming more and more important.

While it can be complex, predictive modeling is a powerful tool that gives businesses a competitive advantage.

2.) Big Data Analytics

These days, it seems like everyone is talking about big data.

And with good reason – organizations of all sizes are sitting on mountains of data, and they’re increasingly turning to data scientists to help them make sense of it all.

But what exactly is big data? And what does it mean for data science?

Simply put, big data is a term used to describe datasets that are too large and complex for traditional data processing techniques.

Big data typically refers to datasets of a few terabytes or more.

But size isn’t the only defining characteristic – big data is also characterized by its high Velocity (the speed at which data is generated), Variety (the different types of data), and Volume (the amount of the information).

Given the enormity of big data, it’s not surprising that organizations are struggling to make sense of it all.

That’s where data science comes in.

Data scientists use various methods to wrangle big data, including distributed computing and other decentralized technologies.

With the help of data science, organizations are beginning to unlock the hidden value in their big data.

By harnessing the power of big data analytics, they can improve their decision-making, better understand their customers, and develop new products and services.

3.) Auto Machine Learning

Auto machine learning is a research topic in data science concerned with developing algorithms that can automatically learn from data without intervention.

This area of research is vital because it allows data scientists to automate the process of writing code for every dataset.

This allows us to focus on other tasks, such as model selection and validation.

Auto machine learning algorithms can learn from data in a hands-off way for the data scientist – while still providing incredible insights.

This makes them a valuable tool for data scientists who either don’t have the skills to do their own analysis or are struggling.

4.) Text Mining

Text mining is a research topic in data science that deals with text data extraction.

This area of research is important because it allows us to get as much information as possible from the vast amount of text data available today.

Text mining techniques can extract information from text data, such as keywords, sentiments, and relationships.

This information can be used for various purposes, such as model building and predictive analytics.

5.) Natural Language Processing

Natural language processing is a data science research topic that analyzes human language data.

This area of research is important because it allows us to understand and make sense of the vast amount of text data available today.

Natural language processing techniques can build predictive and interactive models from any language data.

Natural Language processing is pretty broad, and recent advances like GPT-3 have pushed this topic to the forefront.

6.) Recommender Systems

Recommender systems are an exciting topic in data science because they allow us to make better products, services, and content recommendations.

Businesses can better understand their customers and their needs by using recommender systems.

This, in turn, allows them to develop better products and services that meet the needs of their customers.

Recommender systems are also used to recommend content to users.

This can be done on an individual level or at a group level.

Think about Netflix, for example, always knowing what you want to watch!

Recommender systems are a valuable tool for businesses and users alike.

7.) Deep Learning

Deep learning is a research topic in data science that deals with artificial neural networks.

These networks are composed of multiple layers, and each layer is formed from various nodes.

Deep learning networks can learn from data similarly to how humans learn, irrespective of the data distribution.

This makes them a valuable tool for data scientists looking to build models that can learn from data independently.

The deep learning network has become very popular in recent years because of its ability to achieve state-of-the-art results on various tasks.

There seems to be a new SOTA deep learning algorithm research paper on https://arxiv.org/ every single day!

8.) Reinforcement Learning

Reinforcement learning is a research topic in data science that deals with algorithms that can learn on multiple levels from interactions with their environment.

This area of research is essential because it allows us to develop algorithms that can learn non-greedy approaches to decision-making, allowing businesses and companies to win in the long term compared to the short.

9.) Data Visualization

Data visualization is an excellent research topic in data science because it allows us to see our data in a way that is easy to understand.

Data visualization techniques can be used to create charts, graphs, and other visual representations of data.

This allows us to see the patterns and trends hidden in our data.

Data visualization is also used to communicate results to others.

This allows us to share our findings with others in a way that is easy to understand.

There are many ways to contribute to and learn about data visualization.

Some ways include attending conferences, reading papers, and contributing to open-source projects.

10.) Predictive Maintenance

Predictive maintenance is a hot topic in data science because it allows us to prevent failures before they happen.

This is done using data analytics to predict when a failure will occur.

This allows us to take corrective action before the failure actually happens.

While this sounds simple, avoiding false positives while keeping recall is challenging and an area wide open for advancement.

11.) Financial Analysis

Financial analysis is an older topic that has been around for a while but is still a great field where contributions can be felt.

Current researchers are focused on analyzing macroeconomic data to make better financial decisions.

This is done by analyzing the data to identify trends and patterns.

Financial analysts can use this information to make informed decisions about where to invest their money.

Financial analysis is also used to predict future economic trends.

This allows businesses and individuals to prepare for potential financial hardships and enable companies to be cash-heavy during good economic conditions.

Overall, financial analysis is a valuable tool for anyone looking to make better financial decisions.

12.) Image Recognition

Image recognition is one of the hottest topics in data science because it allows us to identify objects in images.

This is done using artificial intelligence algorithms that can learn from data and understand what objects you’re looking for.

This allows us to build models that can accurately recognize objects in images and video.

This is a valuable tool for businesses and individuals who want to be able to identify objects in images.

Think about security, identification, routing, traffic, etc.

Image Recognition has gained a ton of momentum recently – for a good reason.

13.) Fraud Detection

Fraud detection is a great topic in data science because it allows us to identify fraudulent activity before it happens.

This is done by analyzing data to look for patterns and trends that may be associated with the fraud.

Once our machine learning model recognizes some of these patterns in real time, it immediately detects fraud.

This allows us to take corrective action before the fraud actually happens.

Fraud detection is a valuable tool for anyone who wants to protect themselves from potential fraudulent activity.

14.) Web Scraping

Web scraping is a controversial topic in data science because it allows us to collect data from the web, which is usually data you do not own.

This is done by extracting data from websites using scraping tools that are usually custom-programmed.

This allows us to collect data that would otherwise be inaccessible.

For obvious reasons, web scraping is a unique tool – giving you data your competitors would have no chance of getting.

I think there is an excellent opportunity to create new and innovative ways to make scraping accessible for everyone, not just those who understand Selenium and Beautiful Soup.

15.) Social Media Analysis

Social media analysis is not new; many people have already created exciting and innovative algorithms to study this.

However, it is still a great data science research topic because it allows us to understand how people interact on social media.

This is done by analyzing data from social media platforms to look for insights, bots, and recent societal trends.

Once we understand these practices, we can use this information to improve our marketing efforts.

For example, if we know that a particular demographic prefers a specific type of content, we can create more content that appeals to them.

Social media analysis is also used to understand how people interact with brands on social media.

This allows businesses to understand better what their customers want and need.

Overall, social media analysis is valuable for anyone who wants to improve their marketing efforts or understand how customers interact with brands.

16.) GPU Computing

GPU computing is a fun new research topic in data science because it allows us to process data much faster than traditional CPUs .

Due to how GPUs are made, they’re incredibly proficient at intense matrix operations, outperforming traditional CPUs by very high margins.

While the computation is fast, the coding is still tricky.

There is an excellent research opportunity to bring these innovations to non-traditional modules, allowing data science to take advantage of GPU computing outside of deep learning.

17.) Quantum Computing

Quantum computing is a new research topic in data science and physics because it allows us to process data much faster than traditional computers.

It also opens the door to new types of data.

There are just some problems that can’t be solved utilizing outside of the classical computer.

For example, if you wanted to understand how a single atom moved around, a classical computer couldn’t handle this problem.

You’ll need to utilize a quantum computer to handle quantum mechanics problems.

This may be the “hottest” research topic on the planet right now, with some of the top researchers in computer science and physics worldwide working on it.

You could be too.

18.) Genomics

Genomics may be the only research topic that can compete with quantum computing regarding the “number of top researchers working on it.”

Genomics is a fantastic intersection of data science because it allows us to understand how genes work.

This is done by sequencing the DNA of different organisms to look for insights into our and other species.

Once we understand these patterns, we can use this information to improve our understanding of diseases and create new and innovative treatments for them.

Genomics is also used to study the evolution of different species.

Genomics is the future and a field begging for new and exciting research professionals to take it to the next step.

19.) Location-based services

Location-based services are an old and time-tested research topic in data science.

Since GPS and 4g cell phone reception became a thing, we’ve been trying to stay informed about how humans interact with their environment.

This is done by analyzing data from GPS tracking devices, cell phone towers, and Wi-Fi routers to look for insights into how humans interact.

Once we understand these practices, we can use this information to improve our geotargeting efforts, improve maps, find faster routes, and improve cohesion throughout a community.

Location-based services are used to understand the user, something every business could always use a little bit more of.

While a seemingly “stale” field, location-based services have seen a revival period with self-driving cars.

20.) Smart City Applications

Smart city applications are all the rage in data science research right now.

By harnessing the power of data, cities can become more efficient and sustainable.

But what exactly are smart city applications?

In short, they are systems that use data to improve city infrastructure and services.

This can include anything from traffic management and energy use to waste management and public safety.

Data is collected from various sources, including sensors, cameras, and social media.

It is then analyzed to identify tendencies and habits.

This information can make predictions about future needs and optimize city resources.

As more and more cities strive to become “smart,” the demand for data scientists with expertise in smart city applications is only growing.

21.) Internet Of Things (IoT)

The Internet of Things, or IoT, is exciting and new data science and sustainability research topic.

IoT is a network of physical objects embedded with sensors and connected to the internet.

These objects can include everything from alarm clocks to refrigerators; they’re all connected to the internet.

That means that they can share data with computers.

And that’s where data science comes in.

Data scientists are using IoT data to learn everything from how people use energy to how traffic flows through a city.

They’re also using IoT data to predict when an appliance will break down or when a road will be congested.

Really, the possibilities are endless.

With such a wide-open field, it’s easy to see why IoT is being researched by some of the top professionals in the world.

22.) Cybersecurity

Cybersecurity is a relatively new research topic in data science and in general, but it’s already garnering a lot of attention from businesses and organizations.

After all, with the increasing number of cyber attacks in recent years, it’s clear that we need to find better ways to protect our data.

While most of cybersecurity focuses on infrastructure, data scientists can leverage historical events to find potential exploits to protect their companies.

Sometimes, looking at a problem from a different angle helps, and that’s what data science brings to cybersecurity.

Also, data science can help to develop new security technologies and protocols.

As a result, cybersecurity is a crucial data science research area and one that will only become more important in the years to come.

23.) Blockchain

Blockchain is an incredible new research topic in data science for several reasons.

First, it is a distributed database technology that enables secure, transparent, and tamper-proof transactions.

Did someone say transmitting data?

This makes it an ideal platform for tracking data and transactions in various industries.

Second, blockchain is powered by cryptography, which not only makes it highly secure – but is a familiar foe for data scientists.

Finally, blockchain is still in its early stages of development, so there is much room for research and innovation.

As a result, blockchain is a great new research topic in data science that vows to revolutionize how we store, transmit and manage data.

24.) Sustainability

Sustainability is a relatively new research topic in data science, but it is gaining traction quickly.

To keep up with this demand, The Wharton School of the University of Pennsylvania has started to offer an MBA in Sustainability .

This demand isn’t shocking, and some of the reasons include the following:

Sustainability is an important issue that is relevant to everyone.

Datasets on sustainability are constantly growing and changing, making it an exciting challenge for data scientists.

There hasn’t been a “set way” to approach sustainability from a data perspective, making it an excellent opportunity for interdisciplinary research.

As data science grows, sustainability will likely become an increasingly important research topic.

25.) Educational Data

Education has always been a great topic for research, and with the advent of big data, educational data has become an even richer source of information.

By studying educational data, researchers can gain insights into how students learn, what motivates them, and what barriers these students may face.

Besides, data science can be used to develop educational interventions tailored to individual students’ needs.

Imagine being the researcher that helps that high schooler pass mathematics; what an incredible feeling.

With the increasing availability of educational data, data science has enormous potential to improve the quality of education.

26.) Politics

As data science continues to evolve, so does the scope of its applications.

Originally used primarily for business intelligence and marketing, data science is now applied to various fields, including politics.

By analyzing large data sets, political scientists (data scientists with a cooler name) can gain valuable insights into voting patterns, campaign strategies, and more.

Further, data science can be used to forecast election results and understand the effects of political events on public opinion.

With the wealth of data available, there is no shortage of research opportunities in this field.

As data science evolves, so does our understanding of politics and its role in our world.

27.) Cloud Technologies

Cloud technologies are a great research topic.

It allows for the outsourcing and sharing of computer resources and applications all over the internet.

This lets organizations save money on hardware and maintenance costs while providing employees access to the latest and greatest software and applications.

I believe there is an argument that AWS could be the greatest and most technologically advanced business ever built (Yes, I know it’s only part of the company).

Besides, cloud technologies can help improve team members’ collaboration by allowing them to share files and work on projects together in real-time.

As more businesses adopt cloud technologies, data scientists must stay up-to-date on the latest trends in this area.

By researching cloud technologies, data scientists can help organizations to make the most of this new and exciting technology.

28.) Robotics

Robotics has recently become a household name, and it’s for a good reason.

First, robotics deals with controlling and planning physical systems, an inherently complex problem.

Second, robotics requires various sensors and actuators to interact with the world, making it an ideal application for machine learning techniques.

Finally, robotics is an interdisciplinary field that draws on various disciplines, such as computer science, mechanical engineering, and electrical engineering.

As a result, robotics is a rich source of research problems for data scientists.

29.) HealthCare

Healthcare is an industry that is ripe for data-driven innovation.

Hospitals, clinics, and health insurance companies generate a tremendous amount of data daily.

This data can be used to improve the quality of care and outcomes for patients.

This is perfect timing, as the healthcare industry is undergoing a significant shift towards value-based care, which means there is a greater need than ever for data-driven decision-making.

As a result, healthcare is an exciting new research topic for data scientists.

There are many different ways in which data can be used to improve healthcare, and there is a ton of room for newcomers to make discoveries.

30.) Remote Work

There’s no doubt that remote work is on the rise.

In today’s global economy, more and more businesses are allowing their employees to work from home or anywhere else they can get a stable internet connection.

But what does this mean for data science? Well, for one thing, it opens up a whole new field of research.

For example, how does remote work impact employee productivity?

What are the best ways to manage and collaborate on data science projects when team members are spread across the globe?

And what are the cybersecurity risks associated with working remotely?

These are just a few of the questions that data scientists will be able to answer with further research.

So if you’re looking for a new topic to sink your teeth into, remote work in data science is a great option.

31.) Data-Driven Journalism

Data-driven journalism is an exciting new field of research that combines the best of both worlds: the rigor of data science with the creativity of journalism.

By applying data analytics to large datasets, journalists can uncover stories that would otherwise be hidden.

And telling these stories compellingly can help people better understand the world around them.

Data-driven journalism is still in its infancy, but it has already had a major impact on how news is reported.

In the future, it will only become more important as data becomes increasingly fluid among journalists.

It is an exciting new topic and research field for data scientists to explore.

32.) Data Engineering

Data engineering is a staple in data science, focusing on efficiently managing data.

Data engineers are responsible for developing and maintaining the systems that collect, process, and store data.

In recent years, there has been an increasing demand for data engineers as the volume of data generated by businesses and organizations has grown exponentially.

Data engineers must be able to design and implement efficient data-processing pipelines and have the skills to optimize and troubleshoot existing systems.

If you are looking for a challenging research topic that would immediately impact you worldwide, then improving or innovating a new approach in data engineering would be a good start.

33.) Data Curation

Data curation has been a hot topic in the data science community for some time now.

Curating data involves organizing, managing, and preserving data so researchers can use it.

Data curation can help to ensure that data is accurate, reliable, and accessible.

It can also help to prevent research duplication and to facilitate the sharing of data between researchers.

Data curation is a vital part of data science. In recent years, there has been an increasing focus on data curation, as it has become clear that it is essential for ensuring data quality.

As a result, data curation is now a major research topic in data science.

There are numerous books and articles on the subject, and many universities offer courses on data curation.

Data curation is an integral part of data science and will only become more important in the future.

34.) Meta-Learning

Meta-learning is gaining a ton of steam in data science. It’s learning how to learn.

So, if you can learn how to learn, you can learn anything much faster.

Meta-learning is mainly used in deep learning, as applications outside of this are generally pretty hard.

In deep learning, many parameters need to be tuned for a good model, and there’s usually a lot of data.

You can save time and effort if you can automatically and quickly do this tuning.

In machine learning, meta-learning can improve models’ performance by sharing knowledge between different models.

For example, if you have a bunch of different models that all solve the same problem, then you can use meta-learning to share the knowledge between them to improve the cluster (groups) overall performance.

I don’t know how anyone looking for a research topic could stay away from this field; it’s what the Terminator warned us about!

35.) Data Warehousing

A data warehouse is a system used for data analysis and reporting.

It is a central data repository created by combining data from multiple sources.

Data warehouses are often used to store historical data, such as sales data, financial data, and customer data.

This data type can be used to create reports and perform statistical analysis.

Data warehouses also store data that the organization is not currently using.

This type of data can be used for future research projects.

Data warehousing is an incredible research topic in data science because it offers a variety of benefits.

Data warehouses help organizations to save time and money by reducing the need for manual data entry.

They also help to improve the accuracy of reports and provide a complete picture of the organization’s performance.

Data warehousing feels like one of the weakest parts of the Data Science Technology Stack; if you want a research topic that could have a monumental impact – data warehousing is an excellent place to look.

36.) Business Intelligence

Business intelligence aims to collect, process, and analyze data to help businesses make better decisions.

Business intelligence can improve marketing, sales, customer service, and operations.

It can also be used to identify new business opportunities and track competition.

BI is business and another tool in your company’s toolbox to continue dominating your area.

Data science is the perfect tool for business intelligence because it combines statistics, computer science, and machine learning.

Data scientists can use business intelligence to answer questions like, “What are our customers buying?” or “What are our competitors doing?” or “How can we increase sales?”

Business intelligence is a great way to improve your business’s bottom line and an excellent opportunity to dive deep into a well-respected research topic.

37.) Crowdsourcing

One of the newest areas of research in data science is crowdsourcing.

Crowdsourcing is a process of sourcing tasks or projects to a large group of people, typically via the internet.

This can be done for various purposes, such as gathering data, developing new algorithms, or even just for fun (think: online quizzes and surveys).

But what makes crowdsourcing so powerful is that it allows businesses and organizations to tap into a vast pool of talent and resources they wouldn’t otherwise have access to.

And with the rise of social media, it’s easier than ever to connect with potential crowdsource workers worldwide.

Imagine if you could effect that, finding innovative ways to improve how people work together.

That would have a huge effect.

Final Thoughts, Are These Research Topics In Data Science For You?

Thirty-seven different research topics in data science are a lot to take in, but we hope you found a research topic that interests you.

If not, don’t worry – there are plenty of other great topics to explore.

The important thing is to get started with your research and find ways to apply what you learn to real-world problems.

We wish you the best of luck as you begin your data science journey!

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Featured Collections on Computationally Intensive Problems in General Math and Engineering

This two-part special issue covers computationally intensive problems in engineering and focuses on mathematical mechanisms of interest for emerging problems such as Partial Difference Equations, Tensor Calculus, Mathematical Logic, and Algorithmic Enhancements based on Artificial Intelligence. Applications of the research highlighted in the collection include, but are not limited to: Earthquake Engineering, Spatial Data Analysis, Geo Computation, Geophysics, Genomics and Simulations for Nature Based Construction, and Aerospace Engineering. Featured lead articles are co-authored by three esteemed Nobel laureates: Jean-Marie Lehn, Konstantin Novoselov, and Dan Shechtman.

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99+ Data Science Research Topics: A Path to Innovation

In today’s rapidly advancing digital age, data science research plays a pivotal role in driving innovation, solving complex problems, and shaping the future of technology. Choosing the right data science research topics is paramount to making a meaningful impact in this field.

In this blog, we will delve into the intricacies of selecting compelling data science research topics, explore a range of intriguing ideas, and discuss the methodologies to conduct meaningful research.

How to Choose Data Science Research Topics?

Table of Contents

Selecting the right research topic is the cornerstone of a successful data science endeavor. Several factors come into play when making this decision.

First and foremost, personal interests and passion are essential. A genuine curiosity about a particular subject can fuel the dedication and enthusiasm needed for in-depth research.
Current trends and challenges in data science provide valuable insights into areas that demand attention.
Additionally, the availability of data and resources, as well as the potential impact and applications of the research, should be carefully considered.

: Tips & Tricks

99+ Data Science Research Topics Ideas: Category Wise

Supervised machine learning.

Predictive modeling for disease outbreak prediction.
Credit scoring using machine learning for financial institutions.
Sentiment analysis for stock market predictions.
Recommender systems for personalized content recommendations.
Customer churn prediction in e-commerce.
Speech recognition for voice assistants.
Handwriting recognition for digitization of historical documents.
Facial recognition for security and surveillance.
Time series forecasting for energy consumption.
Object detection in autonomous vehicles.

Unsupervised Machine Learning

Market basket analysis for retail optimization.
Topic modeling for content recommendation.
Clustering techniques for social network analysis.
Anomaly detection in manufacturing processes.
Customer segmentation for marketing strategies.
Event detection in social media data.
Network traffic anomaly detection for cybersecurity.
Anomaly detection in healthcare data.
Fraud detection in insurance claims.
Outlier detection in environmental monitoring.

Natural Language Processing (NLP)

Abstractive text summarization for news articles.
Multilingual sentiment analysis for global brands.
Named entity recognition for information extraction.
Speech-to-text transcription for accessibility.
Hate speech detection in social media.
Aspect-based sentiment analysis for product reviews.
Text classification for content moderation.
Language translation for low-resource languages.
Chatbot development for customer support.
Emotion detection in text and speech.

Deep Learning

Image super-resolution using convolutional neural networks.
Reinforcement learning for game playing and robotics.
Generative adversarial networks (GANs) for image generation.
Transfer learning for domain adaptation in deep models.
Deep learning for medical image analysis.
Video analysis for action recognition.
Natural language understanding with transformer models.
Speech synthesis using deep neural networks.
AI-powered creative art generation.
Deep reinforcement learning for autonomous vehicles.

Big Data Analytics

Real-time data processing for IoT sensor networks.
Social media data analysis for marketing insights.
Data-driven decision-making in supply chain management.
Customer journey analysis for e-commerce.
Predictive maintenance using sensor data.
Stream processing for financial market data.
Energy consumption optimization in smart grids.
Data analytics for climate change mitigation.
Smart city infrastructure optimization.
Data analytics for personalized healthcare recommendations.

Data Ethics and Privacy

Fairness and bias mitigation in AI algorithms.
Ethical considerations in AI for criminal justice.
Privacy-preserving data sharing techniques.
Algorithmic transparency and interpretability.
Data anonymization for privacy protection.
AI ethics in healthcare decision support.
Ethical considerations in facial recognition technology.
Governance frameworks for AI and data use.
Data protection in the age of IoT.
Ensuring AI accountability and responsibility.

Reinforcement Learning

Autonomous drone navigation for package delivery.
Deep reinforcement learning for game AI.
Optimal resource allocation in cloud computing.
Reinforcement learning for personalized education.
Dynamic pricing strategies using reinforcement learning.
Robot control and manipulation with RL.
Multi-agent reinforcement learning for traffic management.
Reinforcement learning in healthcare for treatment plans.
Learning to optimize supply chain logistics.
Reinforcement learning for inventory management.

Computer Vision

Video-based human activity recognition.
3D object detection and tracking.
Visual question answering for image understanding.
Scene understanding for autonomous robots.
Facial emotion recognition in real-time.
Image deblurring and restoration.
Visual SLAM for augmented reality applications.
Image forensics and deepfake detection.
Object counting and density estimation.
Medical image segmentation and diagnosis.

Time Series Analysis

Time series forecasting for renewable energy generation.
Stock price prediction using LSTM models.
Climate data analysis for weather forecasting.
Anomaly detection in industrial sensor data.
Predictive maintenance for machinery.
Time series analysis of social media trends.
Human behavior modeling with time series data.
Forecasting economic indicators.
Time series analysis of health data for disease prediction.
Traffic flow prediction and optimization.

Graph Analytics

Social network analysis for influence prediction.
Recommender systems with graph-based models.
Community detection in complex networks.
Fraud detection in financial networks.
Disease spread modeling in epidemiology.
Knowledge graph construction and querying.
Link prediction in citation networks.
Graph-based sentiment analysis in social media.
Urban planning with transportation network analysis.
Ontology alignment and data integration in semantic web.

What Is The Right Research Methodology?

Alignment with Objectives: Ensure that the chosen research approach aligns with the specific objectives of your study. This will help you answer the research questions effectively.
Data Collection Methods: Carefully plan and execute data collection methods. Consider using surveys, interviews, data mining, or a combination of these based on the nature of your research and the data availability.
Data Analysis Techniques: Select appropriate data analysis techniques that suit the research questions. This may involve using statistical analysis for quantitative data, machine learning algorithms for predictive modeling, or deep learning models for complex pattern recognition, depending on the research context.
Ethical Considerations: Prioritize ethical considerations in data science research. This includes obtaining informed consent from study participants and ensuring data anonymization to protect privacy. Ethical guidelines should be followed throughout the research process.

Choosing the right research methodology involves a thoughtful and purposeful selection of methods and techniques that best serve the objectives of your data science research.

How to Conduct Data Science Research?

Conducting data science research involves a systematic and structured approach to generate insights or develop solutions using data. Here are the key steps to conduct data science research:

Define Research Objectives

Clearly define the goals and objectives of your research. What specific questions do you want to answer or problems do you want to solve?

Literature Review

Conduct a thorough literature review to understand the current state of research in your chosen area. Identify gaps, challenges, and potential research opportunities.

Data Collection

Gather the relevant data for your research. This may involve data from sources like databases, surveys, APIs, or even creating your datasets.

Data Preprocessing

Clean and preprocess the data to ensure it is in a usable format. This includes handling missing values, outliers, and data transformations.

Exploratory Data Analysis (EDA)

Perform EDA to gain a deeper understanding of the data. Visualizations, summary statistics, and data profiling can help identify patterns and insights.

Hypothesis Formulation (if applicable)

If your research involves hypothesis testing, formulate clear hypotheses based on your data and objectives.

Model Development

Choose the appropriate modeling techniques (e.g., machine learning, statistical models) based on your research objectives. Develop and train models as needed.

Evaluation and Validation

Assess the performance and validity of your models or analytical methods. Use appropriate metrics to measure how well they achieve the research goals.

Interpret Results

Analyze the results and interpret what they mean in the context of your research objectives. Visualizations and clear explanations are important.

Iterate and Refine

If necessary, iterate on your data collection, preprocessing, and modeling steps to improve results. This process may involve adjusting parameters or trying different algorithms.

Ethical Considerations

Ensure that your research complies with ethical guidelines, particularly concerning data privacy and informed consent.

Documentation

Maintain comprehensive documentation of your research process, including data sources, methodologies, and results. This helps in reproducibility and transparency.

Communication

Communicate your findings through reports, presentations, or academic papers. Clearly convey the significance of your research and its implications.

Peer Review and Feedback

If applicable, seek peer review and feedback from experts in the field to validate your research and gain valuable insights.

Publication and Sharing

Consider publishing your research in reputable journals or sharing it with the broader community through conferences, online platforms, or industry events.

Continuous Learning

Stay updated with the latest developments in data science and related fields to refine your research skills and methodologies.

Conducting data science research is a dynamic and iterative process, and each step is essential for generating meaningful insights and contributing to the field. It’s important to approach your research with a critical and systematic mindset, ensuring that your work is rigorous and well-documented.

Challenges and Pitfalls of Data Science Research

Data science research, while promising and impactful, comes with its set of challenges. Common obstacles include data quality issues, lack of domain expertise, algorithmic biases, and ethical dilemmas.

Researchers must be aware of these challenges and devise strategies to overcome them. Collaboration with domain experts, thorough validation of algorithms, and adherence to ethical guidelines are some of the approaches to mitigate potential pitfalls.

Impact and Application

The impact of data science research topics extends far beyond the confines of laboratories and academic institutions. Research outcomes often find applications in real-world scenarios, revolutionizing industries and enhancing the quality of life.

Predictive models in healthcare improve patient care and treatment outcomes. Advanced fraud detection systems safeguard financial transactions. Natural language processing technologies power virtual assistants and language translation services, fostering global communication.

Real-time data processing in IoT applications drives smart cities and connected ecosystems. Ethical considerations and privacy-preserving techniques ensure responsible and respectful use of personal data, building trust between technology and society.

Embarking on a journey in data science research topics is an exciting and rewarding endeavor. By choosing the right research topics, conducting rigorous studies, and addressing challenges ethically and responsibly, researchers can contribute significantly to the ever-evolving field of data science.

As we explore the depths of machine learning, natural language processing, big data analytics, and ethical considerations, we pave the way for innovation, shape the future of technology, and make a positive impact on the world.

Top Reasons For Why Should You Use R for Data Science

In Depth Difference Between Big Data And Cloud Computing

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166 Latest Big Data Research Topics And Fascinating Ideas

Big data refers to a huge volume of data, whether organized or unorganized, whose analysis shapes technologies and methodologies. Big data is so massive and complicated that it cannot be handled using ordinary application software. For instance, some frameworks, such as Hadoop, are built to process large amounts of data. Big data has gained much attention, hence it’s a trendy topic and essay for students and researchers who want to write thesis, projects, and dissertations. Based on this, there are several searchable and interesting topics to explore for undergraduate and master’s theses in big data, same as doctoral degrees. In this article, we have provided every topic you need on big data. Our topics stretch from big data analytics, big data research questions, to IoT and database essays. If you’ve been looking for the latest big data research topics, your search stops here. Read on to see some of the most interesting topics for your thesis.

Interesting Big Data Analytics Research Topics

Data analytics is the lifeblood of the modern IT sector. Big data is one of the strategies and technologies for analyzing large amounts of data. Data analytics is being used by the industry to acquire knowledge of system performance and customer behavior. Here are some of the best big data analytics topics and ideas for academic papers.

The surge of Internet of Things (IoT)
Explain the significance of augmented reality.
What is the significance of artificial intelligence?
Describe the graph analytics procedure.
What is agile data science, and how does it differ from traditional data science?
What role does machine intelligence play in today’s businesses?
What is hyper-personalization, and how does it work?
Describe how behavioral analytics works.
What is the experience economy, and how does it work?
Talk about the science of travel.
Discuss the validation and extraction of knowledge.
What is semantic data management, and how does it work?
Describe the process of deep learning.
Describe software engineering in the context of big data science.
What is structured machine learning, and how does it work?
Describe how to answer a semantic question
What is distributed semantic analytics, and how does it work?
What role does domain knowledge play in data analysis?
Why is data exploration important in data analysis?
Who uses big data analytics?

So, it’s not an easy task to write a paper for a high grade. Sometimes every student need a professional help with research paper writing. Therefore, don’t be afraid to hire a writer to complete your assignment. Just contact us and get your paper done soon.

Most Debatable Big Data Research Topics and Essays

The rapid rise of big data in our current time is not without controversy. There is a myriad of ongoing debates in the discipline that have gone unresolved for quite some time. The list below contains the most common big data debate topics.

Big data and its major vulnerabilities.
What measures are in place to recognize a legit user of big data?
Explain the significance of user-access control.
Investigate the importance of centralized key management.
Identify ways to prevent illegal access of data.
Intrusion-detection system: Which is the best?
Does machine learning enhance data quality?
Which security technology has proven to be the best for big data protection?
What strategies should be used for data governance and who should implement data policies?
Should tech giants regularly update security measures and be transparent about them?
How has poor data security contributed to the loss of historical evidence?
What are the most important big data management tools and strategies?
What is data retention and explain its relevance?
Artificial intelligence will lead to the loss of employment and human interaction.
Enterprise analytics: How to manage platforms?
Can data management foster the promotion of peace and freedom?
Who should be in control of data security: Tech giants or the government?
What are the functions of the government in big data management and security?
Discuss how big data is leading to the end of morals and ethics.
How is big data contributing to the rise of global climate and why tech should pay carbon taxes.

Interesting Dissertation Topics on Big Data

Many research theses and big data topics can be found online for undergraduates, Masters, and Ph.D. students. The list below comprises some dissertation topics on big data.

Privacy and security issues in big data and how to curtail them.
Impacts of storage systems of scalable big data.
The significance of big data processing and data management to industrial development.
Techniques and data mining tools for big data.
The benefits of data analytics and cloud computing to the future of work.
Parallel data processing: effective data architecture and how to go about it.
Impacts of machine learning algorithms on the fashion industry.
Using bandwidth provision, how the world of streaming is changing.
What are the benefits and threats of dedicated networks to governance?
Cloud gaming and impacts on Millennials and Generation Z.
Ways to enhance and maximize spread efficiency using flow authority model.
How divergent and convergent is the Internet of Things (IoT) on manufacturing?
Data mining and environmental impact: The way forward.
Geopolitics and the surge of demographic mapping in big data.
Impacts of travel patterns on big data analytics and data management.
The rise of deep learning in the automotive industry.
The sophistication of big data and its implications on cybersecurity.
Discuss how the big data manufacturing process indicates positive globalization.
Evaluate the future of data mining and the adaptation of humans to big data.
Human and material wastes in big data management.

Interesting Research Topics on A/B Testing in Big Data

The A/B testing is also known as controlled experiments and is used widely by companies and firms to make decisions in product launches. Tech companies use the test to know the acceptability of a certain product by the users. However, below are some key research topics on A/B testing in Big Data

Evaluate the common A/B pitfalls in the automotive industry.
Discuss the benefits of improving library user experience with A/B Testing.
How to design A/B tests in a collaboration network.
Analyze how the future of social network advertising can be improved by A/B testing.
Effectiveness of A/B experiments in MOOCs for better instructional methods.
Strategies of Bayesian A/B testing for business decisions.
A/B testing challenges in large-scale social networks and online controlled experiments.
Illustrate how consumer behaviors and trends are shaped by A/B testing.

List of Research Topics on Big Data and Local Governments

Big data offers tremendous value to grassroots governments with the ability to optimize cost through data-induced decisions that reduce the crime rate, traffic congestion and improve the environment. Below are interesting topics on big data and local governments.

How local governments can measure crime using big data testing.
Big data and algorithmic policy in local government policies.
Application of data science technologies to civil service in the local government.
Combating grassroots crime and corruption through algorithmic government.
Big data in the public sector: how local governments can benefit from the algorithmic policy.

Innovative Research Topics on Big Data and IoT

Big data has a lot in common with the Internet of Things (IoT). Indeed, IoT is an integral part of big data. Below are researchable IoT and big data research topics.

The impacts of big data and the Internet of Things (IoT) on the fourth industrial revolution.
The importance of big data and the Internet of Things (IoT) on public health systems.
Explain how big data and the Internet of Things (IoT) dictate the flow of information in the media sector.
Challenges of big data and the Internet of Things (IoT) on governance and sustainability.
The disruption of big data and its attendant effects on the Internet of Things (IoT).
Illustrate the surge in household smart devices and the role of big data analytics.
An analysis of the disruption of the supply chain of traditional goods through the Internet of Things (IoT).
A comprehensive evaluation of machine and deep learning for IoT-enabled healthcare systems.
The future evaluation of the internet of things and big data analytics in the public infrastructure systems.
Discuss how AI-induced security can guarantee effective data protection.
IoT privacy: what data protection means to households and the impacts of security infringement.
Discuss the role of big data and the integrity of the Internet of Things (IoT).
How do dedicated networks work through the Internet of Things (IoT)?
The threats and benefits of the Internet of Things (IoT) forensic science.
Big data distributed storage and impacts on IoT-enabled industries.

Most Engaging Database Big Data Research Topics

The database category of big data has some interesting data science research topics. Due to the large data, modern companies have to analyze every day, which are difficult to handle, strict managing is essential to make sure of the effective use of data. Check out some intriguing big data database research topics students and researchers can write about.

Explain the most inventive big data information concepts and strategies.
Clarify the most ideal data management strategies and techniques for present-day businesses.
New advancements and AI in information management.
What is information maintenance and for what reason is it significant?
Depict the essentials of information management.
Clarify the use of information management in e-learning.
Information distribution and access by present-day organizations.
Clarify the most common way of investigating and overseeing information for biomedical exploration.
Disclose how to function with 3D pictures during research.
How could an association guarantee secure and classified information management and security?
Information indexes: Describe approaches and their execution as well as their reception.
Talk about the effect of information quality on a business.
Instructions on how to advance medical examination and reach logical effort through information management.
The most effective method to source and oversee external data.
Evaluate the procedures available to organizations in ensuring information security through appropriate administration.
Information catalog reference model and global market study.
What is information valuation and what difference does it make in information management?
How could AI further develop database security?
How might an organization carry out effective data administration?
Database management and the cost of disruptive cybersecurity.

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Compelling Big Data Scala Research Topics

Big Data Scala is the product of algorithmic frameworks in deep and machine learning. Below are listed topics on big data Scala for students and young researchers.

Large information versatility dependent on Scala and Spark Machine Learning Libraries
Analyze versatile large information stockpiling frameworks in deep learning.
Dealing with Data and Model drift for practical applications.
Building generative systems based on conversational frameworks (Chatbot systems).
Adaptable designs for parallel data building.
Dealing with continuous video analytics in cloud computing.
Proficient graph processing at a machine learning scale.
Dimensional reduction approaches for information management.
Compelling anonymization of sensitive fields in computer vision.
Versatile security safeguarding on big data.

List of Independent Research Topics for Big Data

Independent researches are pieces of research that may be considered unorthodox in big data testing and management. These are research studies generated by individual researchers. Here is a list of the most fascinating independent research topics on big data.

Significance of effective data mining tools and procedures.
What is data-driven clustering in deep and machine learning?
How impactful is the graph analytics process to the Internet of Things?
Explain the significance of AI for present-day businesses.
Significance of information investigation in information examination on deep learning.
Evaluate the usefulness of coding in Artificial Intelligence.
Clarify the AI strategies in big data management.
Data security: what it means to computer vision.
Impact of open-source deep learning libraries on developers.
The significance of token-based authentication to data security.
Using big data to identify disinformation and misinformation.
Data management and the fundamental principles of Artificial Intelligence.
Big data analytics and why it should be more user-friendly.
Why business intelligence should focus more on privacy preservation.
Social networks and impact on privacy infringement.

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Big data applications: overview, challenges and future

Open access
Published: 16 September 2024
Volume 57 , article number 290 , ( 2024 )

Cite this article

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Afzal Badshah 1 ,
Ali Daud 2 ,
Riad Alharbey 3 ,
Ameen Banjar 3 ,
Amal Bukhari 3 &
Bader Alshemaimri 4

Big Data (i.e., social big data, vehicular big data, healthcare big data etc) points to massive and complex data, that require special technologies and approaches for storage, processing, and analysis. Similarly, big data applications are software and systems utilizing large and complex datasets to extract insights, support decision-making, and address diverse business and societal challenges. Recently, the significance of big data applications has grown immensely for organizations across diverse sectors as they increasingly rely on insights derived from data. The increasing reliance on data insights has rendered traditional technologies and platforms inefficient due to scalability limitations and performance issues. This study contributes by identifying key domains impacted by big data, examining its effect on decision-making, addressing inherent complexities and opportunities, exploring core technologies, and offering solutions for potential concerns. Additionally, it conducts a comparative analysis to demonstrate the superiority of this research. These contributions provide valuable insights into the evolving landscape shaped by big data applications.

Explore related subjects

Artificial Intelligence
Medical Ethics

Avoid common mistakes on your manuscript.

1 Introduction

In the present digital era, big data has emerged as a transformative force, transforming how organizations collect, store, and analyze extensive datasets. The significant increase in data produced from various origins such as social media, autonomous vehicles, and sensors has made Big Data Analytics (BDA) essential for businesses and industries globally. Big data, as defined by the 3 Vs, encompasses large volumes of diverse and rapidly arriving data with potential uncertainties about its quality and availability (Laney 2001 ). The three Vs comprise volume (referring to large datasets), variety (involving diverse data formats), and velocity (indicating the rapid generation of data) (Badshah et al. 2024 ).

Big data ecosystem

The Big Data Ecosystem comprises six key tools essential for efficient large-scale data management (shown in Fig. 1 ). Data Technologies , including Apache Hadoop and Apache Spark, analyze and process Big Data beyond traditional capabilities. Analytics and Visualization tools , such as Tableau and SAS, uncover patterns, while Business Intelligence tools like Cognos transform raw data for business analysis. Cloud Service Providers , like AWS and GCP, offer fundamental infrastructure. NoSQL Databases , including MongoDB and Cassandra, handle Big Data processing, and Programming Tools like R and Python perform analytical tasks and operationalize Big Data, completing this vital ecosystem (Coursera 2023 ).

The applications of big data are diverse and far-reaching, spanning healthcare, supply chain and logistics, marketing and advertising, smart cities, media and entertainment, cybersecurity, climate & earth science, industry, and education. The primary objective of big data lies in its analysis for diverse purposes. Harnessing the capabilities of BDA enables organizations to discover important insights, recognize patterns, and make informed, data-driven decisions. These decisions, in turn, enhance operational efficiency, drive innovation, and improve customer experiences. From personalized healthcare treatments to predictive maintenance in manufacturing, big data is transforming industries and shaping the future of how we live and work (Himeur et al. 2023 ; Talaoui et al. 2023 ).

In the current technological research landscape, big data plays a pivotal role, focusing on the analysis, processing, and extraction of valuable information from extensive and intricate datasets. The foundation of BDA is intricately linked with advanced technologies, specifically Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and the Internet of Things (IoT). Figure 2 illustrates the processing stages of big data. For a better understanding of this article, Table 1 outlines the terminologies used in the study.

Structure of the big data cycle

Devices, data and revenue forecast from 2017 to 2025

The big data market is expected to have remarkable growth globally, with revenue projections ranging to USD 473.6 Billion by 2030, reflecting a growth rate of 12.7% from 2022 to 2030 (Research and Consulting 2023 ). This substantial growth underscores the increasing recognition of big data’s critical role across industries and sectors. Simultaneously, current estimates indicate a massive increase in data generation, with the world expected to produce 175 zettabytes of data by 2025 (Statista 2023 ), as shown in Fig. 3 . This exponential increase highlights the expanding scope and importance of big data as a critical tool for managing, analyzing, and deriving insights from this colossal volume of information.

The massive utilization of big data is propelled by the exponential surge in data volume, the extensive utilization of cloud computing, global digital transformation, increasing internet and smartphone usage, and accelerated adoption due to the impact of the COVID-19 pandemic. Leading companies, such as Google, Amazon, and other tech giants play a crucial role in the big data ecosystem, significantly contributing to the development and advancement of big data technologies, influencing trends, and shaping the future trajectory of this dynamic field.

Numerous comprehensive literature survey papers have extensively explored big data applications. Focusing on healthcare, (Hong et al. 2018 ; Abouelmehdi et al. 2018 ; Rajabion et al. 2019 ; Galetsi et al. 2019 ) conducted a thorough review of big data’s impact in the healthcare sector, often termed Healthcare Big Data (HBD) (HBD). Vehicular Big Data (VBD), referring to big data in vehicles, has received significant attention with comprehensive reviews by researchers in Nguyen et al. ( 2018 ), Torre-Bastida et al. ( 2018 ), Ghofrani et al. ( 2018 ), Mishra et al. ( 2018 ). Concurrently, Urban Big Data (UBD), associated with smart cities, has been deeply explored by authors in Allam and Dhunny ( 2019 ), Karimi et al. ( 2021 ), Mohammadi and Al ( 2018 ), Huang et al. ( 2021 ). Exploring the intersection of big data and cybersecurity, Alani ( 2021 ), Ullah and Babar ( 2019 ), Srivastava and Jaiswal ( 2019 ) provide a comprehensive review. The industrial sector, often referred to as Industrial Big Data (IBD), underwent scrutiny in Qi ( 2020 ), Misra et al. ( 2020 ), Mosavi et al. ( 2018 ), while the education sector, explored under the umbrella of big data, is thoroughly reviewed in Luan et al. ( 2020 ), Baig et al. ( 2020 ), Li and Jiang ( 2021 ). Notably, authors in Akter and Wamba ( 2019 ), Amani et al. ( 2020 ), Huang et al. ( 2018 ), and Akter and Wamba ( 2019 ) have extensively explored the utilization of big data in earth sciences and disaster management, usually referred to as Earth Big Data (EBD). This collective exploration paints a comprehensive picture of the diverse applications and impacts of big data across various domains.

After an in-depth analysis of the available literature, it becomes apparent that individual literature reviews have been conducted across various domains, such as big data in healthcare, vehicles, finance, agriculture, education, etc. However, a wide gap exists in the collective analysis of big data applications. In bridging this gap, it is crucial to undertake a comprehensive assessment of how big data substantially contributes to diverse fields, discern the challenges it presents, delve into ethical concerns, and illuminate emerging applications. Therefore, this article aims to make the following contributions:

This research systematically identifies and analyzes key domains profoundly influenced by big data applications, providing a comprehensive understanding through the exploration of prominent use cases.

The study examines the transformation of decision-making processes in these domains due to big data, emphasizing how data-driven insights contribute to informed decision-making and enrich the existing knowledge on the subject.

This research addresses limitations and potentials in diverse fields’ big data applications, emphasizing inherent complexities and opportunities.

This research delves into the core technologies employed for storing, processing, and analyzing large datasets, elucidating their significance in big data applications.

This research systematically identifies and addresses potential concerns within big data, offering viable solutions and mitigation strategies.

The study conducts a comparative analysis of this research survey with related surveys to demonstrate the unique contributions and superiority of this study.

The subsequent sections encompass the following: In Sect. 2 , the research methodology employed for conducting this study is explained, covering details on the research string, as well as the inclusion and exclusion criteria. Section 3 undertakes the classification of literature concerning big data applications and conducts a thorough analysis of this body of work. Section 10 explored the technologies used to process and store big data. Exploring concerns associated with the utilization of big data in various domains, Sect. 5 delves into potential concerns with big data and introduces potential solutions that can be applied to address the aforementioned issues discussed earlier. Section 6 carried out a detailed comparison of this study with the related literature to show the uniqueness of this paper. Finally, section 7 concludes the study.

2 Research methodology

For the big data applications, we exclusively considered articles published from 2018 to 2023. Our search encompassed Google Scholar, Scopus, IEEE Xplore, and Science Direct to identify pertinent papers. Google Scholar offers access to papers published in any journal, whereas research libraries provide access to a more limited but high-quality selection of papers published in affiliated journals and by specific publishers.

In exploring the electronic world, a pivotal element is the search string, defining the search’s quality. The search string incorporates keywords that encapsulate the population, methodology, and outcomes. The methodology of this research paper is organized into three stages: (i) the Planning phase, (ii) the Conducting phase, and (iii) the Reporting phase. The following section of this part examines these phases.

2.1 Planning the review

In the initial stage, we precisely designed the review’s framework, which encompassed the formulation of the study protocol, identification of relevant journals and research papers, the establishment of including and excluding criteria, and defining our reporting strategy. The planning phase serves two fundamental purposes: (i) emphasizing the significance and necessity of this study, setting it apart from similar research endeavours; and (ii) formulating a robust protocol for conducting a comprehensive search of relevant studies while establishing clear criteria for their inclusion and exclusion.

Creating a well-defined review protocol is of utmost importance. A suitable protocol guides us towards a comprehensive review, while an invalid one may divert authors from the main focus. Therefore, this stage encompasses the examination and determination of developing research queries, exploration approaches, and criteria for selection.

2.2 Conducting the review

During this stage, the research is executed following the protocol delineated in Phase 1. The primary emphasis lies in identifying pertinent research studies, and subjecting them to scrutiny based on three pivotal criteria.

2.2.1 Population

To start, we define the population of the study. For instance, this research encompasses various domains impacted by big data applications, so the population of this review includes diverse fields where big data plays a significant role. The primary keyword is ’Big Data’. This overarching term encompasses various facets, including ’Earth Big Data (EBD)’, ’Vehicular Big Data (VBD)’, ’Healthcare Big Data (HBD)’, ’Urban Big Data (UBD)’, ’Industrial Big Data (IBD)’, and ’Education Big Data (EdBD)’. The inclusion of these synonyms ensures a comprehensive exploration of diverse fields impacted by big data applications.

2.2.2 Methodology or technique

The second criterion involves the methodology or technique employed to achieve the intended outcomes. In this context, big data applications serve as the fundamental technique for obtaining desired results across different domains. The methodology or technique employed in the research is centred around the domain of Data Processing Techniques. The keywords utilized for this area include ’Data Analytics’, ’Machine Learning’, and ’Artificial Intelligence’. These keywords are chosen to encapsulate the fundamental techniques employed to achieve intended outcomes across different domains.

2.2.3 Outcome

The third and final check revolves around the outcomes achieved in each research study. In the case of this review, the outcomes pertain to the applications and impacts of big data within the specified domains. The keywords employed in this context encompass ’Healthcare’, ’Supply-chain’, ’Logistics’, ’Marketing’, ’Advertisement’, ’Smart Cities’, ’Media’, ’Cybersecurity’, ’Climate’, ’Industry’, and ’Education’. These keywords are strategically chosen to align with specific domains, ensuring a focused investigation into the applications and impacts of big data within each specified area.

Therefore, the following research statement is used to search the paper on different platforms. Table 2 shows the details of the research string.

(Big Data) AND (Data Analytics OR Machine Learning OR Artificial Intelligence) AND (Healthcare OR Supply Chain OR Transport OR Marketing OR Advertisement OR Smart Cities OR Social Media OR Climate OR Earth Science OR Industry)

Pursuing these criteria, the subsequent critical step involves formulating research questions. In the context of this review, the investigation questions are as follows:

Which key domains are profoundly influenced by big data applications, and what are the prominent use cases within these domains?

How has big data transformed decision-making processes in these key domains, and how do data-driven insights contribute to informed decision-making?

What are the limitations and potentials associated with big data applications in diverse fields, and what inherent complexities and opportunities do they present?

What core technologies are employed for storing, processing, and analyzing large datasets in big data applications, and what is their significance?

What potential concerns exist within big data applications, and what viable solutions and mitigation strategies can address these concerns?

These research questions guide the review process, providing a structured framework for analyzing the selected research studies and synthesizing their findings. The questions address various aspects of big data applications, from their impact on different domains to the challenges, ethical considerations, and emerging trends associated with their use.

2.3 Quality assessment

The evaluation of this study’s quality depends on various essential parameters. To ensure the robustness and relevance of the papers included in this review, the following parameters have been established.

Inclusion criteria:

Relevant to big data applications : Selected papers must address topics related to big data applications, ensuring the content aligns with the central theme of this review.

methodology and results presentation : Included research articles should present their methodology and results in a clear and organized manner, enhancing the comprehensibility of their findings.

citation threshold : Research articles under consideration must have a minimum of 10 citations, reflecting their impact and recognition within the academic community. However, to include the recent articles, this count is reduced to 5 for 2023 publications.

Publication year: Selected research articles should have been published since 2018 to ensure the incorporation of recent developments in the field.

Exclusion criteria:

Irrelevance to big data role : Research papers that do not discuss the position of big data in any domain will be excluded from the review, as they fall outside the scope of this study.

Inadequate presentation of results and methodology : Papers that do not adequately present the results and methodology used to achieve desired outcomes will be excluded to maintain the quality and rigour of the review.

Insufficient citations : Research papers that have failed to garner at least 10 citations may be excluded to ensure the inclusion of well-recognized and influential works.

Publication year : Research articles not published between 2018 and 2023 will be excluded to focus on more recent and relevant literature.

2.4 Reporting the review

In the final stage, this study extracts and presents papers that are relevant to the keywords and research questions. The impact of the review depends on how the final assessment is presented in the paper.

We conducted a Google search that yielded a total of 22,080 results across various categories, including; Conferences (17,582), Journals (3485), Books (443), Magazines (328), Early Access Articles (228), Standards (12) and Courses (2).

We used the above-mentioned inclusion and exclusion criteria to filter the content. After careful consideration, we selected only 125 papers which were fulfilling the inclusion criteria.

Table 3 shows the number of publications yearwise and categorywise from 2018 to 2023, while Table 13 displays the category-wise publications since 2018.

Big data applications classification

3 Big data application classification

Big data applications have become ubiquitous, establishing as a fundamental technology with a pervasive role across various fields, just like to computers. This section systematically categorizes these applications, delving into their impact, challenges, and prospects. The primary domains of big data applications include Healthcare, Supply Chain and Transport, Market and Transport, Smart Cities, Media, Cyber Security, Earth Science, Industry, Education, and others. Figure 4 visually presents the classification of these diverse big data applications.

3.1 Big data in healthcare

Big data plays a central role in Health 4.0, reshaping healthcare through data-driven research. Analyzing biomedical omics and clinical data offers both challenges and opportunities for healthcare improvement (Ahmed et al. 2023 ). The healthcare industry generates vast data, including hospital records, medical exams, and research, necessitating proper management for meaningful insights (Philip et al. 2022 ). Healthcare’s BDA can enable personalized medicine, clinical risk management, and forecasting, alongside standardizing medical terminology and patient registration (Tohka and Van Gils 2021 ; Masood et al. 2018b ). Table 4 summarizes the big data applications and Fig. 5 shows the HBD categorization.

Big data in healthcare

Integrating biomedical and healthcare data empowers modern organizations to revolutionize medical therapies and personalize treatment (Mehta and Pandit 2018 ). Big data and e-business complement modern hospital management, transforming fragmented systems into comprehensive, omnidirectional healthcare management (Dash et al. 2019 ). Health analytics using big data aids in developing effective medical policies, improving healthcare services, and enhancing disease prediction, drug recommendations, and treatment outcomes (Zhang et al. 2023 ).

A robust BDA platform at Xiangya Hospital’s Gastroenterology Department, China, facilitates comprehensive digestive medicine analysis. The platform combines electronic medical records and colonoscopy data, offering insights for optimal colorectal cancer screening ages and improving healthcare management (Yan et al. 2019 ). Leveraging prescription big data can enhance dosage prediction in pediatric medication. Traditional clinical decision support systems often lack accurate pediatric data. In Wu et al. ( 2019 ), the authors propose a data-driven approach for precise pediatric medication dosage predictions. Authors in Zhou et al. ( 2021 ) introduce a track-able patient health data search system for smart city hospital management, ensuring data privacy and efficient analysis. In Makkie et al. ( 2018 ), authors discuss challenges in analyzing MRI big data and introduce a distributed computing platform using Hadoop and Spark for fMRI data processing.

In telemedicine, an innovative big-data visualization methodology is proposed (Galletta et al. 2018 ). This graphical tool allows remote monitoring of patient health using coloured circles to represent various health data, adhering to the geoJSON standard for data classification. Additionally, authors in Hong et al. ( 2019 ) suggest a medical-history-based algorithm for predicting potential diseases accurately. This algorithm utilizes HBD and DL technology, providing references for targeted medical examinations and reducing delays in treatment due to unclear symptoms or limited professional knowledge. Similarly, authors in Yadav and Jadhav ( 2019 ) employ medical big data in disease recognition.

While health big data is vital for disease detection, migrating to the cloud faces challenges like data standards and sensitivity. Authors endorse a cloud-native healthcare data ingestion service in Wu et al. ( 2019 ) to address these challenges and establish best practices. Similarly, authors in Zhou et al. ( 2020 ) present a scalable system that securely stores and analyzes healthcare data from IoT devices using big data systems and blockchain architecture.

In the future, healthcare organizations will increasingly embrace big data for success. The use of HBD will enhance marketing strategies, especially with the growing popularity of wearable technology and the IoT. The integration of constant patient monitoring data from these sources will provide valuable insights, enabling healthcare marketers to identify and engage patients more effectively.

However, several concerns are associated with the utilization of HBD. One of the primary challenges involves network congestion and delays. The massive data generation, particularly during peak hours, congests the network. Real-time healthcare applications running during these times are significantly affected (Adeghe et al. 2024 ). This is the main reason why healthcare real-time applications do not trust the network. Furthermore, HBD is directly linked to lives. Therefore, it will take time and maturity to build trust in this technology. The health care data is also used for several tasks, such as research and treatment, however, no consent is taken in this regard (Al Teneiji et al. 2024 ).

3.2 Big data in logistics and transport

The integration of big data in logistics and transport has gained significant attention (Yadav and Jadhav 2019 ). Researchers have delved into BDA within SCM, identifying its potential to rectify deficiencies, enhance efficiency, and reduce costs (Lwin et al. 2019 ; Jahani et al. 2023 ). Particularly, in the context of the COVID-19 pandemic, logistics firms harnessed big data and supply chain integration (SCI) to optimize supply chain performance (Ved and B 2019 ; Fosso Wamba et al. 2018 ). Table 5 summarizes the big data applications and Fig. 6 shows the logistic and transport big data categorization.

Big data in supply chain and logistics

Moreover, the synergy between big data analytics technology capability (BDATC) and SCI has been observed to bolster supply chain performance by fostering proactive and reactive capabilities, as well as resource reconfiguration (Leng et al. 2020 ). Blockchain technology has also made inroads in logistics and supply chain systems, lending technical support and mitigating risks (Chen et al. 2022b ). In tandem, AI and big data analysis are utilized to scrutinize logistics service supply chain models, augmenting customer satisfaction and optimizing logistics operations (Farchi et al. 2023 ).

Significantly, the assessment of service capability in maritime logistics enterprises relies heavily on the extensive big data resources derived from the IoT supply chain system. This evaluation is crucial due to the numerous factors influencing maritime logistics, including overseas transportation routes. In Zhu and Du ( 2022 ), the authors suggest an approach for evaluating the service capabilities of maritime logistics enterprises by leveraging big data from the IoT supply chain system.

Moreover, an advanced cloud blockchain and Internet of Everything (IoE) enabled quality control platform seeks to improve quality management and bolster consumer confidence in perishable supply chain logistics, as discussed in Yang et al. ( 2022 ). This platform enables swift sensor data acquisition, ensuring authentication and transparency within cold supply chain logistics. In Jiang ( 2019 ), the authors endorse an intelligent supply chain model based on the IoT and big data. The objective of this model is to enhance information collaboration efficiency while mitigating the risks of supply chain disruption.

In the context of internet supply chain finance, compressed sensing proves to be a valuable method for conducting risk assessments within big data. Authors in Lyu and Zhao ( 2019 ) investigated the development of a risk assessment system for Internet supply chain finance, harnessing the power of compressed sensing and big data analysis. Furthermore, blockchain technology emerges as a robust solution to address security challenges in ITS and big data integration (Zhili et al. 2021 ). By using blockchain, data trustworthiness, transparency, and integrity are assured, surpassing the security standards of centralized databases.

The future holds significant promise for integrating big data in logistics and transportation. As highlighted by a research study (Insider 2023 ), last-mile delivery, a substantial portion of total shipping expenses, faces challenges such as carrier collaboration, manual processes, driver retention, fuel costs, WISMO ("Where is my order?") calls, and return costs. These challenges provide opportunities for optimization through the effective application of big data solutions.

While the integration of big data in logistics and transport is essential, there are associated concerns that need consideration. The primary concern is the privacy of drivers’ locations, which may be misused. Similarly, the utilization of big data in logistics may also jeopardize customer privacy. Therefore, it is necessary to address all these concerns when planning the future of big data in logistics and transport (Albqowr et al. 2024 ).

3.3 Big data in marketing and advertising

Big Data has a substantial influence on marketing and advertising, enabling organizations to collect and scrutinize vast data reservoirs for informed decision-making (Craig and Ludloff 2011 ). It empowers precise targeting and customization of advertising messages, guided by consumer behaviours and preferences (Chen 2022 ; Cockcroft and Russell 2018 ). Real behaviours data marketing entails the collection of internet-driven behavioural data for in-depth analysis of advertising content, timing, and format. This, in turn, fosters more effective customer relationship management and enhances customer retention (Del Vecchio et al. 2022 ; Beauvisage et al. 2023 ). Table 6 summarizes the big data applications and Fig. 7 shows the marketing and advertising big data categorization.

Big data in marketing and advertising

In the financial sector, Big Data has ascended to prominence, with companies leveraging its capabilities for market analysis, customer insights, and informed decision-making. Authors in Hassani et al. ( 2018 ) explore into the pertinence of Big Data approaches in the financial realm, particularly within corporate banking, highlighting opportunities for technological advancements.

Furthermore, in the context of telecom big data, authors in Jia et al. ( 2019 ) propose a meticulous user classification scheme based on decision trees, aimed at amplifying marketing efficiency and effectiveness. The advent of Big Data technology has ushered in a paradigm shift in online advertising delivery, seamlessly integrating data, users, platforms, and businesses. Authors in Jieyu ( 2020 ) investigated the development of a precise online delivery system hinged on Big Data technology.

Cloud computing and Big Data technology have found extensive applications in the world of e-commerce advertising promotion, elevating the core competitiveness of enterprises within this industry. The authors in Zhang ( 2022 ) investigate the utilization of Big Data and cloud computing technology to enhance e-commerce advertising. They propose a distributed system built on Hadoop for this purpose. Similarly, authors in Ducange et al. ( 2018 ) furnish an in-depth analysis of SBD and its application in shaping marketing strategies, encompassing a comprehensive methodology and classification of contemporary use cases.

E-commerce and advertising cannot survive without big data. Nowadays, the action plans of e-commerce and advertising agencies rely heavily on big data analysis. This technology enhances targeted advertising, enabling businesses to reach potential customers more effectively. Therefore, it can be stated that the e-commerce and advertising domains represent significant applications of big data.

However, the deployment of Big Data in marketing and advertising gives rise to substantial concerns regarding privacy and the potential for government surveillance, as discussed in Tang et al. ( 2022 ). Despite its advantages, Big Data in marketing and advertising presents challenges such as the crucial need for consent and the complexities surrounding transparency, identity, power dynamics, and inclusivity (Yin et al. 2021 ). Therefore, it is necessary to prioritize customer data privacy when planning the integration of big data in commerce and advertising.

3.4 Big data in smart cities

The combination of the IoT and BDA technologies holds the potential to be a game-changer in the construction of smart cities (Bibri 2019 ). These technologies provide opportunities for efficient disaster management activities, analysis, and the acquisition of valuable information for decision-making (Shah et al. 2019 ; Ding et al. 2023 ). Table 7 summarizes the big data applications and Fig. 8 shows the UBD categorization.

Big data in smart cities

A plethora of devices connected to the internet in smart cities continuously generates vast amounts of data. Addressing this data deluge, researchers in Wang et al. ( 2018 ) propose enhanced multi-order distributed algorithms to efficiently process this big data in the realm of smart city services. Similarly, authors in Alahakoon et al. ( 2020 ) advocate for a comprehensive framework designed to handle the substantial data inflow from sources such as sensors, IoT devices, and social networks within smart cities. This framework encompasses data processing workflows, ML algorithms, and statistical techniques aimed at extracting meaningful insights from the data.

The evaluation of smart cities has resulted in the generation of massive quantities of data. Unfortunately, a significant portion of this data often goes to waste due to the absence of established mechanisms and standards for extracting valuable information. Authors in Chang ( 2021 ) discuss the issues and approaches linked with leveraging big data and ML to enable cognitive smart cities, thereby enhancing the utilization of this data.

In alignment with this, authors in Wu et al. ( 2018 ) present a framework designed to efficiently process the large amounts of data generated by sensors in smart cities. This architectural model comprises various layers and components for data processing and analysis. ML techniques are integral to this framework, ensuring the acquisition of accurate data and the delivery of precise information to end-users, ultimately resulting in an elevated Quality of Experience (QoE) performance.

In anticipation of the growing prevalence of cameras in smart cities, video surveillance is becoming a key component of data collection. This evolution necessitates the development of efficient techniques for processing substantial volumes of video data. Several papers in the field look into this topic. Tian et al. ( 2018 ) propose a block-level background modelling (BBM) algorithm for efficient video coding, complemented by a rate-distortion optimization algorithm designed to enhance compression performance.

The part of big data in the implementation of smart cities is crucial, as it enables the analysis of extensive data volumes to extract valuable insights. In He et al. ( 2018 ), the authors utilize special technologies for municipal governance and planning in smart cities. Similarly, in Kandt and Batty ( 2021 ), authors delve into the value of big data in shaping long-term urban planning. They emphasize how urban analytics can inform these long-term urban policies within smart cities.

The perspective of big data in smart cities promises transformative advancements. The integration of big data and the IoT is set to revolutionize urban living. Expect more sophisticated data analytics, real-time insights for resource management, improved infrastructure planning, and AI-driven solutions to address urban challenges. This evolution aims to create proactive, sustainable, and resilient smart cities.

However, the widespread use of big data in smart cities brings critical concerns. Security and privacy issues surrounding the vast data generated by IoT devices and sensors need careful attention. Protecting data from unauthorized access and ensuring citizen privacy requires robust security measures and regulatory frameworks. Ethical considerations in data collection, storage, and usage demand scrutiny to prevent misuse. Striking a moderation between reaping the benefits of big data in urban development and safeguarding individual privacy is crucial for fostering trust and ensuring sustainable and inclusive smart city growth (Thilagavathi et al. 2019 ; Elhoseny et al. 2018 ).

3.5 Big data in media

The intersection of big data and entertainment is a dynamic field with vast potential for insights, innovation, and, at the same time, several challenges to navigate Abbasi et al. ( 2018 ); Daud et al. ( 2013 ). Table 8 summarizes the SBD applications and Fig. 9 shows the media big data categorization.

Big data in media

Social media platforms are prolific producers of what’s referred to as SBD (Badshah et al. 2022b ). This treasure trove of data is a window into user behaviour, trends, and interactions, offering valuable insights (Esfahani et al. 2019 ). Companies recognize the power of this data and utilize it to personalize marketing strategies, pinpoint specific demographics, and boost sales (Ghani et al. 2019 ; Rahman and Reza 2022 ). Social media also serves as a powerful platform for businesses to engage with their customer base, foster loyalty, and even function as online retail spaces (Liu et al. 2021 ; Hayat et al. 2019 ).

However, the employing of SBD raises significant concerns related to privacy and the potential misuse of personal information, as highlighted in Bansal et al. ( 2018 ). Thus, the combination of big data and social media presents a dual landscape, offering opportunities for innovation, effective marketing, and improved decision-making. However, it is laden with challenges and ethical considerations. Similarly, authors in Mani and Chouk ( 2022 ) and Vargo et al. ( 2018 ) discussed privacy and security issues in media big data.

To investigate the role of social media big data, the authors in Jimenez-Marquez et al. ( 2019 ) propose a comprehensive two-stage framework tailored for the big data era. The first stage emphasizes data preparation and the selection of a ML model, while the second stage utilizes established layers of big data architectures to extract insights from the data. This versatile framework accommodates both large and small datasets and is illustrated through a case study focused on analyzing reviews of hotel-related businesses. Similarly, in the study (Zhang et al. 2022 ), the authors introduce the Big Data-assisted Social Media Analytics for Business (BD-SMAB) model to enhance decision-making in marketing strategies and competitive analysis.

Social media is a focal point for marketing, especially for business-to-business (B2B) organizations aiming to sustain and expand through strategic operations and marketing activities, as explained by authors in Sivarajah et al. ( 2020 ).

The potential of SBD is also recognized in the realm of urban sustainability research and practice. Its unique advantages, including vast scale and near-real-time observation, offer insights into human behaviour within urban environments. Authors in Ilieva and McPhearson ( 2018 ) delve into the potential and issues associated with harnessing social media data for urban sustainability research and practice, shedding light on a promising avenue for urban development.

The integration of big data in entertainment and social media is currently revolutionizing user experiences, content creation, and industry dynamics. With ongoing technological advancements, big data is driving personalized content recommendations, offering predictive insights, enhancing user engagement, enabling targeted advertising, optimizing content distribution, and facilitating real-time trend analysis (Hariri et al. 2019 ). Emphasizing data security and privacy measures, these developments are transforming the industry, providing tailored and immersive experiences, improving content relevance, and ensuring efficiency in advertising and content distribution. To remain competitive in these evolving sectors, a seamless integration of BDA is essential to meet the dynamic expectations of users in today’s rapidly advancing technological landscape (Amalina et al. 2019 ).

Despite its benefits, social media big data faces challenges such as misinformation and limited data, making it difficult to distinguish the truth. Current solutions struggle with scalability in large-scale events (Zhang et al. 2018 ). Furthermore, this big data is wrongly used by companies, as they share it with commercial entities. These companies enforce their narratives through advertising. Therefore, it is necessary to address these concerns while working on SBD, especially in entertainment, particularly on social media.

3.6 Big data in cyber security

Playing a crucial role in cybersecurity, big data is especially significant in domains such as intrusion detection, anomaly detection, spamming and spoofing detection, malware and ransomware detection, code security, and cloud security (Walters and Novak 2021 ). The integration of BDA with ML can effectively address unknown risks and insider threats, providing advanced threat analytics (Saravanan and Prakash 2021 ). It enables the discovery of irregularities and suspicious activities, leading to the deployment of effective intrusion detection systems (França et al. 2021 ). Additionally, BDA can enhance data security and privacy, mitigating cybersecurity breaches and supporting secure information sharing (Rassam et al. 2017 ). The application of BDA in cybersecurity is an emerging trend, presenting potential future directions for research and development (Wang and Jones 2021 ). Table 9 summarizes the applications and Fig. 10 shows the cybersecurity big data categorization.

By leveraging big data and advanced analytics techniques, organizations can improve their operational intelligence and security capabilities, staying ahead of evolving cyber threats. Authors in Kantarcioglu and Xi ( 2016 ) discussed security issues faced in the big data environment, particularly in the context of cloud computing.

Big data in cyber security

Surveilling the security of the IoT through multidimensional streaming big data encounters various challenges, including substantial data volumes, redundancy, and scalability issues. To tackle these obstacles, the authors in Ullah et al. ( 2022 ) present an algorithm called ODIS. This algorithm extracts vital information from data across distributed sensor nodes, considering the spatial and temporal dependence structure of the data. ODIS establishes a precise data structure model to understand IoT system behaviours and employs testing methods to quantify the uncertainty linked with monitoring tasks. Adversarial data mining is an emerging field that combines BDA with cybersecurity. Authors in Li et al. ( 2019 ) used adversarial data mining techniques to handle malicious adversaries in cyber security applications.

In Tao et al. ( 2019 ), the authors introduced a parameter-wise adaptation that autonomously initiates the tuning process. This system adjusts the configuration parameters of the framework for various security datasets and subsequently executes the BDCA system with the adapted configuration. Similarly, Rawat et al. ( 2019 ) explores the economic aspects of safeguarding big data security and privacy, encompassing investment decisions and cyber insurance.

To tackle the challenges posed by cyber threats in the cloud, the authors in Subroto and Apriyana ( 2019 ) have devised a cloud computing-based system for cybersecurity management. This system aims to streamline the analysis process of extensive network data. The constructed system is built on the MapReduce framework and encompasses end-user devices, cloud infrastructure, and a monitoring center.

Big data is advancing cybersecurity, making it more intelligent for the future. This increased intelligence will enable systems to promptly counter cyber attacks. Consequently, cybersecurity experts are acquiring additional skills in both big data and cybersecurity, driven by the recognition of the crucial role played by these combined capabilities (Zhang and Ghorbani 2021 ).

Big data in cybersecurity offers potent advantages but introduces challenges, including privacy concerns, security issues, data accuracy, scalability, and cost management. Successfully navigating these hurdles requires a comprehensive strategy addressing legal compliance, robust security measures, data quality assurance, and cost-effective implementation (Rao and Lakshmanan 2024 ).

3.7 Big data in earth science

An extensive array of data about our planet, which is usually also referred to as Earth Big Data (EBD) is generated from Earth observation systems on diverse platforms, such as satellites, aeroplanes, and ground-based setups. This includes geoscience, statistical, and social data (Yang et al. 2019 ). Integrating Earth observation data with other forms within a geographic context offers the potential to model Earth systems more accurately, linking human activities with their impacts on Earth processes (EOS 2023 ). Table 10 summarizes the applications and Fig. 11 shows the earth’s big data categorization.

Big data in earth science

Big data applications in climate and earth studies have gained increasing importance in recent years. These applications involve the utilization of large volumes of data generated from climate and weather modelling (Huang et al. 2018 ). The analysis of this big climate data has led to advancements in understanding climate change, assessing environmental conditions, and predicting future climate trends. Leveraging BDA, including data mining techniques and the integration of heterogeneous data sources, has empowered researchers to study climate change in a more comprehensive and interdisciplinary manner. Open data resources, like Google Earth Engine, have been used to evaluate environmental conditions and assess vulnerability to climate change in specific regions (Amani et al. 2020 ). Overall, big data tools and techniques have provided valuable insights into climate-related issues and have the potential to contribute to sustainability and resilience-building efforts.

Big data on climate and earth is used for several purposes. The foremost use is the monitoring. Authors in Hassani et al. ( 2019 ) designed BDA to enhance seasonal change monitoring and understanding of climate change. The second big use of big data is to predict the climate and conditions. Authors in Knüsel et al. ( 2019 ) used Big data techniques in rainfall prediction, helping farmers make wise decisions on crop yield and studying the timing of floods or droughts. Similar concepts are discussed and proposed by authors in Sebestyén et al. ( 2021 ) and use the big data collected by different sensors for climate monitoring and prediction. Authors in Silva et al. ( 2018 ) discussed in detail the studies, which investigated big data climate monitoring and prediction.

Along with climate monitoring and prediction, big data is used for Sustainable Urban Planning and Infrastructure. Authors in Leung et al. ( 2019 ); Ameer and Shah ( 2018 ) used big data and its analytics tools in urban planning and smart city decision management. Similarly, authors in Sarker et al. ( 2020 ) used BDA for smart cities’ air pollution prediction. They introduced a spark-based architecture for smart urban planning that utilizes BDA to classify air quality. This architecture is implemented on a dataset of vehicle pollution in Aarhus City, Denmark.

Disaster management has become a significant concern, and Big Data is being utilized for natural disaster management. Authors in Yu et al. ( 2018 ), utilized big data for disaster management derived from remote sensing imagery, social media data, crowdsourced data, GIS, and mobile metadata. Similarly, in Sarker et al. ( 2020b ), the authors investigated several studies exploring the use of big data in disaster management.

The main challenge associated with the Earth’s big data is its continuous growth. Every country deploys satellites, balloons, aeroplanes, and other tools that consistently gather data. However, reaping benefits from this data is contingent upon having appropriate tools. Regarding the sheer volume of Earth’s big data, our current tools are not advanced enough to thoroughly analyze it Sudmanns et al. ( 2019 ).

The foremost concern regarding Earth Big Data is individual privacy. This data is constantly generated without regard for the privacy of specific locations, making it accessible to anyone for various purposes. The data finds application in numerous fields, including science, weather prediction, and defence. The issue of precisely identifying the responsible party or owner of this data remains unresolved (Farley et al. 2018 ). Therefore, there is a need to explore whether it is feasible to collect this data with individual consent and whether regulations can be established to govern this vast dataset.

3.8 Big data in industry

Big data is being applied in various industries, including construction, sports, tourism, and the legal field. In the construction industry, big data is utilized to enhance construction efficiency, reduce material waste and expenses, improve planning and decision-making processes, and enhance construction site safety (Nguyen et al. 2020 ). In the sports industry, big data analysis and AI are used to analyze player performance, broadcast events, and improve sports marketing strategies (Patel et al. 2020 ). In tourism, big data is used for revenue management, marketing strategies, customer experience, and market research, aiding in the development and recovery of the industry (Li et al. 2022 ). In the legal industry, BDA tools are used for tasks such as billing, marketing, and identifying trends in cases (Bhure and Desai 2023 ). Table 11 summarizes the applications and Fig. 12 shows the industry big data categorization.

Big data in industry

Authors in Lies ( 2019 ) covered big data’s transformative role in automotive marketing, emphasizing precision marketing and data-driven consumer insights. A similar theme is explored in Liu and You ( 2021 ), where big data correlates with a 2.895% increase in new energy vehicle technology innovation, advocating its integration with the industry for national benefits.

Classification benefits from big data too, as seen in Li et al. ( 2019 ), where cellular company customer records are categorized to enhance marketing efficiency. In Chen et al. ( 2022a ), big data analysis is used to create tailored data packages. The chemical industry harnesses big data for intelligent manufacturing, evaluating strengths, weaknesses, and future trends (Jiyang et al. 2020 ). Similarly, Huabei Oilfield adopts big data with a "seven-step method" system and a data mining for oil production engineering, enhancing data-driven processes (Mohammadpoor and Torabi 2020 ).

Big data is reshaping industries, particularly production, in alignment with market analysis. The expanding realm of big data is certain to amplify its influence on the industry. Utilizing big data analysis will enhance customer-centric production strategies, ultimately leading to improved revenue outcomes (Vassakis et al. 2018 ).

Big data utilized for market analysis is collected from various sources, raising significant concerns about the privacy and security of this data. Therefore, it is imperative to ensure that the data collection and analysis do not compromise someone’s privacy and security (Del Vecchio et al. 2018 ).

3.9 Big data in education

Big data has the potential to enhance teaching and learning, improve educational research, and advance education governance (Fischer et al. 2020 ). Although the utilization of big data in education is not a new concept, recent technological advancements have spurred increased research in this area (Ray and Saeed 2018 ; Amjad et al. 2018 ). There is an interest in leveraging big data to analyze student behavior and performance, enhance the educational system, and integrate big data into the curriculum (Baig et al. 2020 ). Popular tools and techniques for working with big data in the education industry include educational data mining and learning analytics (Qian et al. 2022 ). The convergence of the ability to collect, store, manage, and process data, along with data from online educational platforms, presents unprecedented opportunities for educational institutions, learners, educators, and researchers. Table 12 provides a summary of the applications, and Fig. 13 illustrates the categorization of big data in education.

Big data in education

In educational technology, the most investigated these days is personalized learning. With the help of personalized learning, the personalized content or subjects are recommended to the learners and they can learn in their own space (Munshi and Alhindi 2021 ). Authors in Yuwen et al. ( 2018 ) carried out some experiences to appropriately suggest the courses to the learners using BDA. Their results show that their accuracy for the course recommendation is much better than the already working algorithms. Similarly, authors in Kanth et al. ( 2018 ) highlighted the challenges of identifying student misconceptions, predicting dropouts, and improving educational quality, with a focus on leveraging data and advanced technologies. The authors aim to enhance personalized learning and propose various supervised learning methods as solutions.

Student management and discipline represent significant challenges in educational institutions. The authors in Zhang et al. ( 2021 ) addressed this issue by leveraging big data. Through the analysis of students’ daily routines, learning styles, and behavior, they obtained insights to aid in student management. In Liang ( 2020 ), the authors present an education management model utilizing big data, demonstrating improved information levels and a broader application of big data in educational management. Similarly, authors in Badshah ( 2023a , 2023b ) utilize similar concepts for student management and enhancing their productive engagement.

The big data is also changing the way of teaching. Flipped classrooms (Hao 2021 ) and homeschooling (Inayatulloh et al. 2022 ) are the leading examples. Authors in Hu et al. ( 2022 ) explored the same by proposing the hybrid teaching method. Their investigation shows that students were more actively engaged in the learning concerning the normal classes.

Education is intricately linked with big data as both a producer and consumer. Millions of individuals, whether learners, teachers, or administrators, are actively engaged in this dynamic field. The demand for virtual classes has surged during the COVID-19 pandemic, further emphasizing the role of big data in meeting these evolving educational needs. Concepts like personalized learning and home-based schooling are gaining prominence, relying entirely on the insights and capabilities provided by big data. In this interconnected landscape, the symbiotic relationship between education and big data continues to shape the future of learning.

While there is a considerable list of advantages, the use of big data in education also raises several concerns. Foremost among them is the risk of misuse, as the data of thousands of learners, including institutional geography and learner locations, may be mishandled. Additionally, concerns about data bias and algorithmic bias pose potential challenges that need careful consideration to ensure fair and equitable outcomes (Lin et al. 2024 ).

4 Key technologies

In big data, several key enabling technologies play pivotal roles in facilitating the storage, processing, and analysis of extensive and intricate datasets. These technologies serve as the backbone for the vast potential of big data applications. Here are some of the key enabling technologies discussed.

At the forefront, Hadoop stands as a distributed storage and processing framework that enables parallelized handling of large datasets. Its architecture allows for efficient and scalable data processing, making it a cornerstone in the big data ecosystem (Apache 2023a ).

4.2 Apache spark

Complementing Hadoop, Spark emerges as an in-memory data processing engine that significantly enhances the speed and efficiency of BDA. It excels in iterative computations and ML algorithms, contributing to improved data processing capabilities, as discussed in Apache ( 2023c ).

4.3 NoSQL databases

In the era of diverse data types, NoSQL databases like MongoDB (Apache 2023d ) and Cassandra (Cassandra 2023 ) play a vital role. These non-relational databases accommodate unstructured and varied data, providing flexibility and scalability crucial for managing the complexities of modern data.

4.4 Data warehousing

Technologies such as Amazon Redshift (Amazone 2023 ) and Google BigQuery (Google 2023 ) exemplify the capacity to store and retrieve large volumes of structured data. These solutions for data warehousing enable organizations to effectively handle and retrieve their data for analytical purposes.

4.5 Machine learning

The integration of ML algorithms and frameworks, including TensorFlow (Tensor 2023 ) and scikit-learn Learning ( 2023 ), empowers data scientists to derive actionable insights and predictions from vast datasets. ML becomes an invaluable tool in uncovering patterns and trends within the data.

4.6 Data integration tools

Apache NiFi Apache ( 2023b ) and Talend ( 2023 ) exemplify the significance of data integration tools. These platforms facilitate the seamless integration of diverse data sources, ensuring a unified and coherent dataset ready for comprehensive analysis.

4.7 Data visualization tools

Platforms like Tableau ( 2023 ) and Power BI Microsoft ( 2023 ) add a layer of accessibility to big data insights. These visualization tools transform complex datasets into digestible visualizations, enabling stakeholders to interpret and understand data-driven narratives.

4.8 Blockchain technology

Highlighting security and transparency, blockchain technology contributes to safeguarding the integrity of transactions and data sharing in the realm of big data. The decentralized nature of blockchain enhances both trust and data immutability (Badshah 2023c ).

4.9 Edge computing

To fulfil the demand for real-time analytics, edge computing facilitates data handling near the data source. This minimizes latency and improves the efficiency of analytics for applications such as the IoT (Amjad et al. 2018 ).

4.10 Cloud computing

Services offered by AWS, Azure, and Google provide a scalable and flexible infrastructure for big data storage and processing. Cloud computing has become a cornerstone, furnishing organizations with the resources required to manage the continuously expanding volumes of data (Badshah 2023a ).

5 Potential concerns and solutions

As we have explored the expansive landscape of employing big data across various applications, it becomes imperative to acknowledge and address potential challenges and concerns associated with its widespread utilization (Ajah and Nweke 2019 ). These concerns, spanning privacy, security, biases, and misuse, highlight the need for understanding the implications and risks inherent in BDA (Ikegwu et al. 2024 ). In this section, we delve into these concerns, acknowledging the multifaceted nature of navigating complexities when harnessing vast datasets. Additionally, we present solutions to tackle these challenges, offering a roadmap for a more secure and responsible digital environment. This section provides a detailed examination of proactive measures, carefully crafted to address distinct aspects of concern.

5.1 Privacy

A substantial concern associated with the utilization of big data across various applications is the issue of privacy. Almost every field grapples with this concern due to the underdeveloped nature of regulations on data security and privacy. The existing rules lack maturity, posing a challenge in adequately protecting user data (Amaithi Rajan and V 2023 ; Masood et al. 2018a ).

To address privacy issues linked with big data, it is crucial to advocate for the development and implementation of robust regulations governing data security and privacy. Collaborating with regulatory bodies and policymakers to create comprehensive and mature frameworks will enhance the protection of user data (Price and Cohen 2019 ).

5.2 Security

The lack of data privacy raises security concerns, not just for the data itself but also for individual security. Organizational data exposure or the public availability of individual locations can lead to notable security problems. The connection between data vulnerability and individual security issues exacerbates the overall concern (Khan and Ahmad 2023 ).

Mitigating security risks involves reinforcing data privacy measures. Implementing encryption, access controls, and regular security audits can fortify the protection of organizational and individual data. Additionally, fostering awareness about cybersecurity practices among users is essential for minimizing vulnerabilities (Ikegwu et al. 2022 ).

Algorithmic bias, especially in IoT devices, is a common problem nowadays. Similarly, BDA may also exhibit biases in their calculations, disrupting decision-making processes (Rehman et al. 2022 ).

Addressing algorithmic biases in BDA requires continuous monitoring and evaluation of algorithms. Implementing diversity in datasets and adopting ethical guidelines for algorithm development can help mitigate biases, ensuring fair and unbiased decision-making processes (Favaretto et al. 2019 ; Amjad et al. 2012 ).

Misuse of big data is a major concern, with companies often utilizing this data without considering the welfare of customers. Many individuals are unaware of how their data is being used for the benefit of companies. Mitigating potential misuse requires increased transparency and ethical considerations (Stegenga et al. 2023 ).

Preventing the misuse of big data involves enhancing transparency in data usage and fostering ethical considerations. Implementing clear data usage policies, obtaining explicit consent from users, and educating individuals about how their data is utilized contribute to responsible and ethical data practices (Bag et al. 2023 ).

5.5 Different cyber laws

The internet has transformed the world into a global village, however, the issue is that cyber rules and regulations vary widely. Every country has different rules, leading to conflicts on the internet. An action may be a crime in some countries and not in others, highlighting the need for harmonizing international cyber regulations (Rawat et al. 2023 ).

When the big data concerns are collectively looked at, it is noticed that all these concerns are linked with international cyber laws. Due to this gap, the digital world has these issues. It is, therefore, important and need of the day to go ahead toward international cyber rules, which will equally work in all countries (Favaretto et al. 2019 ).

5.6 Doubted accuracy

Despite its advantages, social media big data faces challenges such as misinformation and limited data, making it difficult to distinguish the truth. Therefore, it is not always guaranteed that the big data used for decision-making is correct (Badshah et al. 2022b ).

Ensuring the accuracy of big data used for decision-making requires implementing rigorous data validation processes. Incorporating fact-checking mechanisms, promoting data transparency, and investing in data quality assurance measures contribute to the reliability of information derived from big data (Khan et al. 2016 ).

5.7 Reason for network congestion

The rapid growth in data generation leads to network congestion, slowing internet speed and impeding real-time communication. This poses challenges, particularly in critical applications like hospitals, where trust in the network’s reliability is compromised. Addressing congestion is crucial for ensuring seamless real-time interactions and maintaining the dependability of data-driven systems (Anitha et al. 2023 ).

Addressing network congestion involves optimizing data transmission protocols, investing in network infrastructure, and implementing load-balancing techniques. Prioritizing network reliability in critical sectors like healthcare ensures that real-time communication remains unaffected even during periods of high data traffic (Al-Jumaili et al. 2023 ).

5.8 Special hardware and software

In big data, concerns emerge regarding the need for specialized hardware and software. Access and compatibility challenges risk obstructing positive outcomes. Processing vast data volumes demands specialized tools, resist by limitations in hardware or software and the complexity of multiple data formats. The substantial processing needs also contribute to higher costs, necessitating careful cost management for optimal resource utilization (Badshah et al. 2022a ).

To overcome challenges related to specialized hardware and software, organizations should invest in versatile and scalable technologies. Collaborating with technology providers to develop solutions that enhance accessibility and compatibility can facilitate positive outcomes without compromising on processing efficiency (Selmy et al. 2023 ).

5.9 Dependency on tech experts

One limitation of big data lies in its dependency on technology experts for its collection, filtering, and processing. This reliance poses a challenge in ensuring that the necessary expertise is consistently available for the effective utilization of big data resources (Badshah 2023b ).

Reducing the dependency on tech experts requires investing in user-friendly interfaces and tools. Implementing training programs for non-experts and promoting the development of intuitive big data platforms can empower a wider range of professionals to harness the power of big data resources effectively (Selmy et al. 2023 ).

6 Comparative analysis

This section compares the current literature study with related surveys. Scholars have extensively studied and investigated big data and its applications. However, existing reviews often focus on a single application of big data, failing to explore it comprehensively. Big data has potential and challenges in every domain, necessitating a thorough investigation. Additionally, no studies have categorized big data applications or comprehensively discussed their future potentials and concerns.

To evaluate and compare our study with similar ones, we applied the criteria outlined in Table 14 . The criteria included examining challenges (C1), future potentials (C2), domain categorization (C3), privacy concerns (C4), and specific domains such as healthcare (C5), supply chain and logistics (C6), marketing and advertising (C7), smart cities (C8), media and entertainment (C9), cybersecurity (C10), climate and earth science (C11), industry (C12), and education (C13). Table 15 shows the overall comparison of the related surveys literature.

Big data applications in healthcare have been extensively reviewed, focusing on the benefits and challenges in this domain. The study in Hong et al. ( 2018 ) offers a comprehensive overview of big data in healthcare, addressing challenges (C1) and exploring applications (C5). The authors emphasize the importance of privacy (C4) and regulatory frameworks. Subsequently, a study (Abouelmehdi et al. 2018 ) investigate the transformative potential of big data within the healthcare domain (C2), highlighting privacy concerns (C4). This study provides valuable insights into disease prediction and cost reduction (C5). Furthermore, authors in Rajabion et al. ( 2019 ) contribute to understanding data processing mechanisms in healthcare (C3). Lastly, study in Galetsi et al. ( 2019 ) emphasizes the value of personalized services in healthcare (C5), acknowledging privacy concerns (C4).

Big data applications within the supply chain and logistics domain have shown significant potential for optimization and efficiency improvements. The study in Torre-Bastida et al. ( 2018 ) offers a comprehensive overview of big data applications within the transportation industry, addressing challenges (C1) and exploring opportunities in routing, planning, monitoring, and network design (C6). Building upon this, authors in Nguyen et al. ( 2018 ) extend the analysis to the broader supply chain management domain (C6), proposing a classification framework and identifying research gaps (C2). Focusing on the railway sector, study (Ghofrani et al. 2018 ) contributes to the understanding of big data applications in operations, maintenance, and safety, leveraging Mayring’s framework (C6). A broader perspective is offered by Mishra et al. ( 2018 ), which provides a bibliometric analysis of big data in supply chain management (C6), identifying key research clusters and managerial insights.

The application of big data in marketing and advertising has been explored to understand its impact on digital marketing strategies and customer engagement. The study by Miklosik and Evans ( 2020 ) delves into the application of big data and ML in the realm of digital marketing (C7), uncovering unexplored avenues for future research. Subsequently, authors in Anshari et al. ( 2019 ) explore the integration of big data into CRM, emphasizing its role in personalized marketing strategies (C7). While survey papers like Kushwaha et al. ( 2021 ); Sestino et al. ( 2020 ), and Lee et al. ( 2023 ) offer comprehensive overviews of big data in marketing and advertising (C7).

Big data plays a crucial role in the development and management of smart cities, enhancing sustainability and livability. The study (Karimi et al. 2021 ) delves into the urban potential of AI within Smart Cities, emphasizing the integration of culture, metabolism, and governance for sustainability and livability (C8). It prioritizes the livability of the urban fabric alongside economic growth, showcasing the potential of AI and Big Data integration. In alignment with this perspective, authors in Mohammadi and Al ( 2018 ) conduct a comprehensive review of big data handling in smart cities, categorizing techniques and exploring key ideas (C3). The study introduces crucial factors such as scalability, time, availability, and accuracy, contributing to the understanding of big data’s role in smart city development (C8). Similarly, the study in Huang et al. ( 2021 ) addresses the underutilized data in smart cities by proposing a three-level framework employing semi-supervised deep reinforcement learning to optimize control policies (C8). The interconnected studies collectively contribute to a more holistic understanding and advancement of AI and big data applications in the context of smart cities.

The media and entertainment industry has been significantly impacted by big data, especially through social media platforms. Big data is revolutionizing the media and entertainment industry. A significant portion of this data is generated by social media platforms. The study in Abkenar et al. ( 2021 ) explores the types of SBD, laying the groundwork for understanding its potential applications in this domain (C9). While studies (Sebei et al. 2018 ) and Muhammad et al. ( 2018 ) contribute to the growing body of knowledge in this area (C9).

The application of BDA in cybersecurity is critical for enhancing security measures and protecting against cyber threats. The study in Alani ( 2021 ) surveys the applications of BDA in cybersecurity, covering areas such as intrusion detection, spamming detection, and cloud security (C10). It highlights the rapid increase in data generation due to the growing number of internet users. Building on this foundation, authors in Ullah and Babar ( 2019 ) and Srivastava and Jaiswal ( 2019 ) further explore the role of big data in cybersecurity, expanding the knowledge base in this domain (C10).

Big data has significant applications in earth sciences and disaster management, aiding in visualization, analysis, and prediction. The study by Akter and Wamba ( 2019 ) examines the application of big data in natural disaster management (C11), emphasizing visualization, analysis, and prediction. It highlights the role of emerging technologies in enhancing disaster response and recovery strategies. Expanding on this, Amani et al. ( 2020 ) delves into the utilization of Google Earth Engine (GEE) in various domains, including land classification, hydrology, and climate analysis (C11). Shifting focus to agriculture, authors in Huang et al. ( 2018 ) explore the application of big data in precision agriculture, addressing challenges and proposing a management framework. A comprehensive overview of big data in disaster management is presented in Akter and Wamba ( 2019 ), providing valuable insights into research trends, challenges, and future directions (C11).

BDA is revolutionizing industries, offering advanced analytics, optimization, decision-making, modelling, and predictions. BDA is revolutionizing industries, offering advanced analytics, optimization, decision-making, modelling, and predictions. The study in Mosavi et al. ( 2018 ) explores the adoption of big data technologies in the engineering domain, highlighting its role in enhancing competitiveness. It reviews academic literature on big data applications within the engineering field (C12). Expanding the focus to industry-specific challenges, Qi ( 2020 ) delves into the mining industry, addressing hurdles in implementing big data management (BDM) (C12). The study outlines data sources, challenges, and future prospects for the mining industry (C1, C2). Furthermore, Misra et al. ( 2020 ) explores the impact of IoT, big data, and AI on agri-food systems (C12). It covers applications across the supply chain, from agriculture to food quality assessment, emphasizing commercialization and translational research outcomes.

The exploration of big data applications in education reveals a growing body of research. Authors in Luan et al. ( 2020 ) delve into challenges and trends (C1, C2), advocating for a balanced approach to technology integration (C13). A study in Baig et al. ( 2020 ) contributes by analyzing 40 studies, focusing on learner behaviour and performance (C13), while the investigation in Li and Jiang ( 2021 ) examines the impact of COVID-19 on educational big data, highlighting the role of educational psychology (C13).

In the context of these contributions and the existing literature, this study represents a pioneering investigation that deeply probes big data applications, categorization, challenges, and potential futures. This collective exploration paints a comprehensive picture of the diverse applications and impacts of big data across various domains.

7 Conclusion

This research explored the dynamic landscape of Big Data applications, unveiling their profound impact across diverse domains. The literature is meticulously categorized into distinct segments: healthcare, supply chain and logistics, marketing and advertising, smart cities, media and entertainment, cybersecurity, climate and earth science, industry, and education. Furthermore, it examined the transformative effects on decision-making processes, emphasizing the role of data-driven insights in various domains. Challenges and issues related to Big Data are thoroughly investigated, and recommendations are presented to overcome these hurdles. Additionally, core technologies for storing, processing, and analyzing large datasets are explored. The study also identifies and addresses potential concerns within Big Data, offering robust solutions and effective mitigation strategies.Through a comprehensive comparative analysis with related surveys, this research highlights its unique contributions and superiority. These contributions collectively bridge the existing gap in collective analysis, providing a holistic perspective on multifaceted Big Data applications.

Data availability

No datasets were generated or analysed during the current study.

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Department of Software Engineering, University of Sargodha, Sargodha, Pakistan

Afzal Badshah

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Afzal and Ali have written a major part of the paper under the supervision of Riad and Ameen. Ameen, Bader, and Riad have helped design and improve the methodology and wrote the paper initial draft with Afzal and Ali. Ameen and Amal have helped in improving the paper sections, such as, review methodology, datasets, and challenges and future directions. Amal, Bader and Ameen have improved the technical writing of paper. All authors are involved in revising the manuscript critically and have approved the final version of the manuscript.

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140 Excellent Big Data Research Topics to Consider

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Are you a computer science student searching for recent big data research topics for your final year project? Do you want to write a top-quality big data research paper but are confused about what topic to choose? If yes, then this blog post is for you.

Big Data is one of the recently emerging technologies that have gained a lot of attraction among professionals, especially computer science engineers and information technologists. In the latest internet world, we are surrounded by data and information. Particularly, after the advent of digital systems, data is considered to be precious. In order to process, store, and analyze a large volume of data, the concept of Big Data came into existence.

To write an excellent computer science thesis on big data, you must have a valid research topic. As big data is a broad subject, choosing a new trending research topic is a challenging task. So, to help you, here, in this blog post, we have listed the top interesting big data topics for you to consider for research or academic writing.

List of Outstanding Big Data Research Topics

When it comes to writing research papers and essays, it is necessary to choose trendy research topics to get an A+ grade. As far as big data is concerned, you can conduct research on any interesting data science topics, data mining topics, data analysis topics, or data security topics.

Listed below are a few top-notch big data research topic ideas. You can go through the complete list and identify the best big data research topic of your desire.

Simple Big Data Research Ideas

Maturity model of big data.
How far is data science relevant as a master’s thesis and research in today’s date?
How can big data develop organizational operations and enhance its competitive advantage in the current competitive market?
Briefly describe the Hadoop Ecosystem
Describe the use of NoSQL Database and R Programming
Evaluation of SQL-based Technologies
Describe the application of Predictive Analytics
Comparative analysis of the application of Apache Spark and Elasticsearch
Describe the difference between Tensor Flow, Beam, and Apache Airflow
Compare and contrast Docker and Kubernetes
How does the use of data analytics bring positive social impact?
Discuss the use of Big Data in therapies and genomics
Describe the three major components of big data
What are the major challenges of big data?
Discuss the impact of Big Data on bioinformatics

Big Data Analysis Research Topics

Who uses big data analytics?
Why is domain knowledge important in data analysis?
What is distributed semantic analytics?
Why is data exploration important in data analysis?
Define semantic questions answering
What is structured machine learning?
What is semantic data management ?
The Internet of Things
How important is artificial intelligence?
Describe the importance of augmented reality.
What is agile data science?
Explain the knowledge validation and extraction.
Explain the deep learning process.
Significance of machine learning for modern business.
What is hyper-personalization?
Experience economy and its relevance.
Analyzing large-scale data for social networks
Discuss the behavioral analytics process.
Explain journey sciences.
Discuss the graph analytics process.
Explore the problems associated with big data.
Analyze the use of GIS and spatial data.
How far is big data for storage and transfer
How can big data be used for efficiently modeling uncertainty?
Explore the use of Quantum computing for big data Analytics
Describe the five latest Big Data trends in 2022
Discuss DataOps and data stewardship
What are the essential practices related to big data analytics for manufacturing businesses?
Discuss the best way to preserve and Assess Big Data, Video Integrity, and Images using AI
Evaluate the Use of Big Data in Healthcare
Evaluation of the effectiveness of healthcare diagnoses and using deep learning
Synergies of machine learning and data management: methods, problems, and future directions
Describe the usefulness of Big Data analysis

Data Mining Research Topics

Big data mining techniques and tools
The role of data mining in analyzing transaction data in a supermarket.
Parallel spectral clustering within a distributed system
Explain the Association Rule Learning regarding data mining
Describe the concept of data spectroscopic clustering
Describe asymmetrical spectral clustering
What is information-based clustering?
Self-turning spectral clustering
Discuss the K-Means clustering from an online spherical perspective.
Discuss the K-Means algorithms in data clustering.
Symmetrical spectral clustering
Discuss the performance of representative-based clustering.
Discuss the package of MATLAB spectral clustering.
How can the effectiveness of nonlinear and linear regression analysis be improved?
Discuss the hierarchical clustering application.
Explain the performance of dependency modeling.
Explain the importance of probabilistic classification in data mining.
Model-based clustering of texts
Explain the need for density-based clustering.
Discuss the importance of subject-based data mining in minimizing terrorism.
Explore how data mining can be used in automatic content generation.
The use of data mining in evaluating employee performance.
Discuss about Parallel Spectral Clustering in Distributed System
What are K-Means Algorithms for Data Clustering and how it gets applied in Data Mining?
Why Data mining is called an iterative process?
How does Data mining go through the phases laid down by the Cross Industry Standard Process for Data Mining (CRISP-DM) process model?
Compare and contrast Data Mining and Web Mining
Discuss the differences between Oracle Data Mining and Test Mining
Analyze Data Mining as a Service(DMaaS)
What is called Domain Driven Data Mining and Opinion Mining?
How Predictive Analytics is Used in Data Mining?
Discuss the benefits and drawbacks of using Web mining for businesses that depend on the web

Read more: Innovative Technology Research Topics To Explore and Write About

Data Security Research Topics

Why should big data owners update security measures regularly?
How does changing the data from Terabytes to Petabytes affect its security?
What are the major vulnerabilities of big data?
The security technologies that can be used to protect big data
How does Hadoop integrate with modern security tools?
Token-based authentication
How do data encryption tools work?
How can poor data security lead to the loss of important information?
Why is user access control important?
How to prevent illegitimate data access?
How to identify a legit data user?
The importance of centralized key management
How to implement attribute-access or role-based access control?
How do intrusion prevention and detection systems work?
The best intrusion detection system
Which tool or algorithm can be used for data owner and user authentication?
What are the most effective physical systems for securing data?
The implementation of attribute-access or role-based access control.
Explain how you can determine the amount of secure data.
The best encryption tools for protecting transit data.

Recent Trending Big Data Research Topics

Data retention and its importance.
Describe data catalog approaches, implementations, and adoption.
Describe some of the most innovative bid data management concepts.
Analytics for Big Data in the Smart Healthcare Systems
New technologies and AI in data management
Explain the best data management strategies for modern enterprises.
How to manage platforms for enterprise analytics
The impact of data quality on business
How can a company implement data governance?
How can machine learning improve the data quality?
Anomaly detection in large-scale data systems
The process of analyzing and managing data for reproducible research.
Data catalog reference model and market study
The role of data valuation in data management.
Explain software engineering for big data science.
How to ensure effective data protection through proper management
Big data analytics and privacy preservation
Data publishing and access by modern companies
How to work with images during research?
How to promote research and scientific outreach through data management?

Read more: Interesting Cybercrime Research Topics To Deal With

Unique Big Data Research Topics

Evaluate the logistic regression modeling.
Explain the malicious user detection in big data collection.
Evaluate data stream management in task allocation.
Explain how to gather and monitor traffic information using CCTV images
What is the difference between edge computing and in-memory computing?
Explain the difference between agile data science and Scala language.
Evaluate how Scala includes a useful REPL for interaction.
Discuss the influence of big data and smart city planning in society.
Evaluate the adaptive systems and models at runtime.
Explain the relation between urban dynamics and crowdsourcing services.

From the list of 100+ ideas suggested above, choose any topic that matches your university requirements and compose a brilliant big data research paper. In case, you are not satisfied with the topics recommended here, contact us immediately.

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Top 25 Big Data Projects in 2024 [With Source Code]

Home Blog Big Data Top 25 Big Data Projects in 2024 [With Source Code]

Big data and Artificial Intelligence have been thriving in recent years, and the emphasis on these technologies will propel them to new heights. Companies have realized the value of big data, and various opportunities are knocking on your door. It is the ideal moment to begin working on your big data project if you are a big data student in your final year. Current suggestions for your next big data project are provided in this article. You can check out the best Big Data courses to have an in-depth idea about big data tools and technologies to prepare for a job in the domain. This article will provide big data project examples, big data projects for final year students , data mini projects with source code and some big data sample projects. The article will also discuss some big data projects using Hadoop and big data projects using Spark .

Let's check some big data analytics projects and big data analytics projects with source code . The top big data projects that you shouldn't miss are listed below.

List of Big Data Projects [Based on Levels]

Applying what you've learned will be necessary. Working on big data projects will allow you to exercise your big data skills. The chance to put your skills to the test is greatly enhanced by projects. Additionally, they look fantastic on resumes. In this article, we'll talk about some fantastic big data project ideas you may work on to show off your expertise in the field. Let’s check some big data projects with source code.


Traffic control using Big Data	Big Data Cybersecurity	Anomaly detection in Cloud Servers
Search Engine	Crime Detection	Smart cities using Big Data
Medical insurance fraud detection	Disease prediction based on symptom	Tourist behavior analysis
Data warehouse design for an E-Commerce site	Recommendation System	Web Server Log analysis

Big Data Project Projects for Beginners

The following is a list of some of the best big data projects for beginners:

1. Traffic control using Big Data

This project is a Lambda Architecture program that tracks Chicago's streets' traffic conditions, including congestion and safety. On 1,250 roadway segments inside the city limits, it shows current traffic crashes, red-light, and speed camera offenses, as well as traffic trends.

Source Code: Traffic Control

2. Search Engine

Search engines must manage trillions of network objects and keep track of billions of users' online activities in order to understand what people are searching for. Search engines transform website content into quantitative data. This is an intriguing big data Hadoop project for newcomers who wish to learn the fundamentals of running data queries and analytics using Apache Hive. For obtaining data from various Hadoop-integrated databases and file systems, Hive has a SQL-like interface. If you are familiar with SQL, you should have no trouble completing this project.

Source Code: Search Engine

3. Medical insurance fraud detection

Medical Insurance Fraud Detection is a special data science approach for predicting fraud in the medical insurance market that makes use of real-time analysis and classification algorithms. The government can use this tool to help patients, pharmacies, and physicians, ultimately boosting sector trust, addressing the problem of rising healthcare costs, and reducing the effects of fraud. With the help of data scientists and workers with AI backgrounds, this project uses data analytics in a special way to uncover connections between healthcare professionals.

Source Code: Medical Insurance Fraud Detection

4. Data warehouse design for an E-Commerce site

In this big data project, you will be building a data warehouse for a retail establishment. However, it focuses on providing answers to a few specific questions on the design and implementation of pricing optimization and inventory allocation. You'll be attempting to respond to the following two questions in this hive project:

Were the more expensive products more common in some markets?
Should inventory be redistributed, or should prices be changed in accordance with location?

Source Code: Data Warehouse Design for an E-Commerce Site

Intermediate Big Data Projects

The following is a list of some of the best intermediate big data projects:

1. Big Data Cybersecurity

It is among the important big data machine learning projects . By obtaining login credentials from any of the company's users and then getting into the network, cyber attackers may choose to target a particular company. It is very challenging for ordinary antivirus software to detect this, given that the user credentials are genuine and that a cyberattack may occur without anyone being aware of it. Your user behavior modeling system will be built using big data algorithms.

The main goal of this Big Data project is to use sophisticated multivariate time series data to manipulate vulnerability disclosure trends in current cybersecurity issues. The system's machine learning and automation engines are integrated with outliers and detect suspicious technologies based on Hadoop, Spark, and Storm, allowing for real-time detection of fraud and prevention of threats in forensics.

Source Code: Big Data Cybersecurity

2. Crime Detection

It is among the important Apache big data projects. This intriguing big data study looks for trends to anticipate and identify connections in a dynamic criminal network. Since the criminal network is a dynamic social graph, this study uses a stream processing technique to extract pertinent information as soon as data is generated. Additionally, it offers three brand-new social network similarity indicators for the detection and forecasting of criminal links. The following phase entails creating a flexible data stream analysis application with the Apache Flink framework, allowing for the deployment and assessment of both newly proposed and existing metrics.

Source Code: Crime Detection

3. Disease prediction based on symptom

There's a phrase that goes, "Health is wealth." To be fair, wealth cannot exist unless one is well enough to engage in worldly pleasures. Risk factors for many diseases can be genetic, environmental, or nutritional, more prevalent in a certain age group or sex, and more prevalent in various races or regions. They can also be environmental or genetic.

The presence of additional risk variables can be used to calculate the likelihood that a certain disease would manifest by compiling datasets of this information that are pertinent for specific conditions, such as diabetes, Parkinson's disease, and breast cancer. When the risk variables are unknown, the datasets can be analyzed to find patterns of risk factors and, as a result, forecast the likelihood of onset appropriately.

Source Code: Disease Prediction Based on Symptoms

4. Recommendation System

Online services often provide access to thousands, millions, or even billions of items, including goods, advertisements, video clips, movies, music, blog entries, and so forth. Big data makes it possible for recommendation systems to give accurate and pertinent recommendations by providing a wealth of user data, including past purchases, browsing history, and opinions. Our recommendation system for mini-movies is powered by big data. This project aims to compare how different recommendation models function on the Hadoop Framework.

Source Code: Recommendation System

Advanced Big Data Projects

The following is a list of some of the advanced-level Big Data projects :

1. Anomaly detection in Cloud Servers

As cloud computing has grown in popularity, many people and businesses have turned to cloud storage solutions. This approach is prompted by benefits like shared storage, computing, and transparent service among a large number of users. However, maintaining sophisticated, large-scale systems with essentially inescapable runtime issues brought on by hardware and software errors is necessary for cloud computing systems. A crucial strategy for handling such complicated cloud resources is automatic anomaly detection.

Source Code: Anomaly Detection

2. Smart cities using Big Data

Smart cities are technologically advanced urban centers that gather data through the use of various digital means, voice activation methods, and sensors. The knowledge gained from the data is used to manage resources, services, and assets effectively; in turn, the data is used to enhance operations across the city.

Source Code: Smart Cities

3. Tourist behavior analysis

A nation's economy might be negatively impacted by the enormous industry of tourism, which supports the livelihoods of many people. This behavior can be examined in terms of decision-making, perception, destination preference, and level of satisfaction to ensure that both visitors and residents have a positive experience. One of the more sophisticated project concepts in the Big Data space is behavior analysis, which is similar to sentiment analysis.

Source Code: Behavior Analysis

4. Web Server Log analysis

Web server log analysis can be used to acquire a feel of the overall user experience. Any business that depends heavily on its website for customer service or revenue production can benefit from this type of processing.

Source Code: Log Analysis

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More Big Data Project Ideas & Topics

We will explore some Big Data projects with source code that you could explore and do as well to include in your data science portfolio. We will cover Big Data projects for beginners, intermediate and advanced levels so that you can choose the one that is right for you.

1. Beginners Level

Hadoop Project for Beginners-SQL Analytics with Hive
Tough engineering choices with large datasets in Hive Part - 1
Finding Unique URL's using Hadoop Hive
AWS Project - Build an ETL Data Pipeline on AWS EMR Cluster
Yelp Data Processing Using Spark And Hive Part 1
Yelp Data Processing using Spark and Hive Part 2

2. Intermediate Level

Analyzing Big Data with Twitter Sentiments using Spark Streaming
PySpark Tutorial - Learn to use Apache Spark with Python
Tough engineering choices with large datasets in Hive Part - 2
Event Data Analysis using AWS ELK Stack

3. Advanced Level

Build a Time Series Analysis Dashboard with Spark and Grafana
GCP Data Ingestion with SQL using Google Cloud Dataflow
Deploying auto-reply Twitter handle with Kafka, Spark, and LSTM
Dealing with Slowly Changing Dimensions using Snowflake

What Problems You Might Face in Doing Big Data Projects?

A data analyst might come across quite a few challenges while executing Big Data projects, especially the Big Data live projects or some real time projects on Big Data. These are:

1. Inadequate Monitoring: While working with Big Data real-time projects, monitoring real-time environments could be a problem as not many solutions are available for this.

2. Latency Problems: Output latency during data virtualization is a common problem faced during data analysis due to the tools requiring high-level performance leading to latency in output generation.

3. Data Privacy: While dealing with data, data privacy and the governance policy of the company needs to be adhered to as any privacy breach to it might be fatal to the project.

4. Demanding Scripts/ Tools: A Big Data analytics project might require a higher-level of scripting or the use of tools that you are not familiar with.

Why Are Big Data Projects So Important?

A big data project is a data analysis program that bases its analysis on a very sizable data set. Big data is any collection of data that is larger than one terabyte.

Traditional data analysis methods are combined with others that are specifically designed to manage high data volumes in big data initiatives. Big data engineers frequently use deep learning, machine learning, and computer vision as part of their analytical process.

Because of the limitations of conventional techniques, software engineers could not truly analyze very large volumes of data before the development of the big data area. The future of project big data is bright, and here are some of the examples that tell us why big data is important:

Big data is utilized in the energy sector by oil and gas companies to track pipeline traffic and by utilities to monitor power grids and potential drilling locations.
Manufacturing and transportation companies use big data to manage their supply networks and enhance delivery routes.
Other government applications include disaster response, crime prevention, and smart city programs.

Thus, the article provides a concise big data projects list and various big data-related projects. Big data is already enormous, but it is predicted to increase rapidly as new technologies enter the picture, like the increasingly prevalent IoT devices, drones, and wearables. You can enroll in the KnowledgeHut best Big Data courses to learn important concepts and aspects of big data from industry experts to launch a successful career in Big Data.

Frequently Asked Questions (FAQs)

Data Projects are initiatives to people whose goal is to deliver something useful that can be used. This could involve developing and writing reports, using machine learning models, and other activities.

A big data project is a data management project that bases its analysis on a very large data set.

Having a good project plan is the first and most important stage in starting any project endeavor. A well-defined procedure should always be followed while developing a large data project.

A big data project's objective is to be able to mine data and analyze it to find hidden patterns. Big data is used by today's data-driven businesses to better understand their customers and inform corporate strategy, such as those in the banking and e-commerce industries.

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Dr. Manish Kumar Jain is an accomplished author, international corporate trainer, and technical consultant with 20+ years of industry experience. He specializes in cutting-edge technologies such as ChatGPT, OpenAI, generative AI, prompt engineering, Industry 4.0, web 3.0, blockchain, RPA, IoT, ML, data science, big data, AI, cloud computing, Hadoop, and deep learning. With expertise in fintech, IIoT, and blockchain, he possesses in-depth knowledge of diverse sectors including finance, aerospace, retail, logistics, energy, banking, telecom, healthcare, manufacturing, education, and oil and gas. Holding a PhD in deep learning and image processing, Dr. Jain's extensive certifications and professional achievements demonstrate his commitment to delivering exceptional training and consultancy services globally while staying at the forefront of technology.

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List of Best Research and Thesis Topic Ideas for Data Science in 2022

In an era driven by digital and technological transformation, businesses actively seek skilled and talented data science potentials capable of leveraging data insights to enhance business productivity and achieve organizational objectives. In keeping with an increasing demand for data science professionals, universities offer various data science and big data courses to prepare students for the tech industry. Research projects are a crucial part of these programs and a well- executed data science project can make your CV appear more robust and compelling. A broad range of data science topics exist that offer exciting possibilities for research but choosing data science research topics can be a real challenge for students . After all, a good research project relies first and foremost on data analytics research topics that draw upon both mono-disciplinary and multi-disciplinary research to explore endless possibilities for real –world applications.

As one of the top-most masters and PhD online dissertation writing services , we are geared to assist students in the entire research process right from the initial conception to the final execution to ensure that you have a truly fulfilling and enriching research experience. These resources are also helpful for those students who are taking online classes .

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Data science thesis topics

We have compiled a list of data science research topics for students studying data science that can be utilized in data science projects in 2022. our team of professional data experts have brought together master or MBA thesis topics in data science that cater to core areas driving the field of data science and big data that will relieve all your research anxieties and provide a solid grounding for an interesting research projects . The article will feature data science thesis ideas that can be immensely beneficial for students as they cover a broad research agenda for future data science . These ideas have been drawn from the 8 v’s of big data namely Volume, Value, Veracity, Visualization, Variety, Velocity, Viscosity, and Virility that provide interesting and challenging research areas for prospective researches in their masters or PhD thesis . Overall, the general big data research topics can be divided into distinct categories to facilitate the research topic selection process.

Security and privacy issues
Cloud Computing Platforms for Big Data Adoption and Analytics
Real-time data analytics for processing of image , video and text
Modeling uncertainty

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DATA SCIENCE PHD RESEARCH TOPICS

The article will also guide students engaged in doctoral research by introducing them to an outstanding list of data science thesis topics that can lead to major real-time applications of big data analytics in your research projects.

Intelligent traffic control ; Gathering and monitoring traffic information using CCTV images.
Asymmetric protected storage methodology over multi-cloud service providers in Big data.
Leveraging disseminated data over big data analytics environment.
Internet of Things.
Large-scale data system and anomaly detection.

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Now, we’ll proceed to cover specific research problems encompassing both data analytics research topics and big data thesis topics that have applications across multiple domains.

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Multi-modal Transfer Learning for Cross-Modal Information Retrieval

Aim and objectives.

The research aims to examine and explore the use of CMR approach in bringing about a flexible retrieval experience by combining data across different modalities to ensure abundant multimedia data.

Develop methods to enable learning across different modalities in shared cross modal spaces comprising texts and images as well as consider the limitations of existing cross –modal retrieval algorithms.
Investigate the presence and effects of bias in cross modal transfer learning and suggesting strategies for bias detection and mitigation.
Develop a tool with query expansion and relevance feedback capabilities to facilitate search and retrieval of multi-modal data.
Investigate the methods of multi modal learning and elaborate on the importance of multi-modal deep learning to provide a comprehensive learning experience.

The Role of Machine Learning in Facilitating the Implication of the Scientific Computing and Software Engineering

Evaluate how machine learning leads to improvements in computational APA reference generator tools and thus aids in the implementation of scientific computing
Evaluating the effectiveness of machine learning in solving complex problems and improving the efficiency of scientific computing and software engineering processes.
Assessing the potential benefits and challenges of using machine learning in these fields, including factors such as cost, accuracy, and scalability.
Examining the ethical and social implications of using machine learning in scientific computing and software engineering, such as issues related to bias, transparency, and accountability.

Trustworthy AI

The research aims to explore the crucial role of data science in advancing scientific goals and solving problems as well as the implications involved in use of AI systems especially with respect to ethical concerns.

Investigate the value of digital infrastructures available through open data in aiding sharing and inter linking of data for enhanced global collaborative research efforts
Provide explanations of the outcomes of a machine learning model for a meaningful interpretation to build trust among users about the reliability and authenticity of data
Investigate how formal models can be used to verify and establish the efficacy of the results derived from probabilistic model.
Review the concept of Trustworthy computing as a relevant framework for addressing the ethical concerns associated with AI systems.

The Implementation of Data Science and their impact on the management environment and sustainability

The aim of the research is to demonstrate how data science and analytics can be leveraged in achieving sustainable development.

To examine the implementation of data science using data-driven decision-making tools
To evaluate the impact of modern information technology on management environment and sustainability.
To examine the use of data science in achieving more effective and efficient environment management
Explore how data science and analytics can be used to achieve sustainability goals across three dimensions of economic, social and environmental.

Big data analytics in healthcare systems

The aim of the research is to examine the application of creating smart healthcare systems and how it can lead to more efficient, accessible and cost –effective health care.

Identify the potential Areas or opportunities in big data to transform the healthcare system such as for diagnosis, treatment planning, or drug development.
Assessing the potential benefits and challenges of using AI and deep learning in healthcare, including factors such as cost, efficiency, and accessibility
Evaluating the effectiveness of AI and deep learning in improving patient outcomes, such as reducing morbidity and mortality rates, improving accuracy and speed of diagnoses, or reducing medical errors
Examining the ethical and social implications of using AI and deep learning in healthcare, such as issues related to bias, privacy, and autonomy.

Large-Scale Data-Driven Financial Risk Assessment

The research aims to explore the possibility offered by big data in a consistent and real time assessment of financial risks.

Investigate how the use of big data can help to identify and forecast risks that can harm a business.
Categories the types of financial risks faced by companies.
Describe the importance of financial risk management for companies in business terms.
Train a machine learning model to classify transactions as fraudulent or genuine.

Scalable Architectures for Parallel Data Processing

Big data has exposed us to an ever –growing volume of data which cannot be handled through traditional data management and analysis systems. This has given rise to the use of scalable system architectures to efficiently process big data and exploit its true value. The research aims to analyses the current state of practice in scalable architectures and identify common patterns and techniques to design scalable architectures for parallel data processing.

To design and implement a prototype scalable architecture for parallel data processing
To evaluate the performance and scalability of the prototype architecture using benchmarks and real-world datasets
To compare the prototype architecture with existing solutions and identify its strengths and weaknesses
To evaluate the trade-offs and limitations of different scalable architectures for parallel data processing
To provide recommendations for the use of the prototype architecture in different scenarios, such as batch processing, stream processing, and interactive querying

Robotic manipulation modelling

The aim of this research is to develop and validate a model-based control approach for robotic manipulation of small, precise objects.

Develop a mathematical model of the robotic system that captures the dynamics of the manipulator and the grasped object.
Design a control algorithm that uses the developed model to achieve stable and accurate grasping of the object.
Test the proposed approach in simulation and validate the results through experiments with a physical robotic system.
Evaluate the performance of the proposed approach in terms of stability, accuracy, and robustness to uncertainties and perturbations.
Identify potential applications and areas for future work in the field of robotic manipulation for precision tasks.

Big data analytics and its impacts on marketing strategy

The aim of this research is to investigate the impact of big data analytics on marketing strategy and to identify best practices for leveraging this technology to inform decision-making.

Review the literature on big data analytics and marketing strategy to identify key trends and challenges
Conduct a case study analysis of companies that have successfully integrated big data analytics into their marketing strategies
Identify the key factors that contribute to the effectiveness of big data analytics in marketing decision-making
Develop a framework for integrating big data analytics into marketing strategy.
Investigate the ethical implications of big data analytics in marketing and suggest best practices for responsible use of this technology.

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Platforms for large scale data computing: big data analysis and acceptance

To investigate the performance and scalability of different large-scale data computing platforms.

To compare the features and capabilities of different platforms and determine which is most suitable for a given use case.
To identify best practices for using these platforms, including considerations for data management, security, and cost.
To explore the potential for integrating these platforms with other technologies and tools for data analysis and visualization.
To develop case studies or practical examples of how these platforms have been used to solve real-world data analysis challenges.

Distributed data clustering

Distributed data clustering can be a useful approach for analyzing and understanding complex datasets, as it allows for the identification of patterns and relationships that may not be immediately apparent.

To develop and evaluate new algorithms for distributed data clustering that is efficient and scalable.

To compare the performance and accuracy of different distributed data clustering algorithms on a variety of datasets.
To investigate the impact of different parameters and settings on the performance of distributed data clustering algorithms.
To explore the potential for integrating distributed data clustering with other machine learning and data analysis techniques.
To apply distributed data clustering to real-world problems and evaluate its effectiveness.

Analyzing and predicting urbanization patterns using GIS and data mining techniques".

The aim of this project is to use GIS and data mining techniques to analyze and predict urbanization patterns in a specific region.

To collect and process relevant data on urbanization patterns, including population density, land use, and infrastructure development, using GIS tools.
To apply data mining techniques, such as clustering and regression analysis, to identify trends and patterns in the data.
To use the results of the data analysis to develop a predictive model for urbanization patterns in the region.
To present the results of the analysis and the predictive model in a clear and visually appealing way, using GIS maps and other visualization techniques.

Use of big data and IOT in the media industry

Big data and the Internet of Things (IoT) are emerging technologies that are transforming the way that information is collected, analyzed, and disseminated in the media sector. The aim of the research is to understand how big data and IoT re used to dictate information flow in the media industry

Identifying the key ways in which big data and IoT are being used in the media sector, such as for content creation, audience engagement, or advertising.
Analyzing the benefits and challenges of using big data and IoT in the media industry, including factors such as cost, efficiency, and effectiveness.
Examining the ethical and social implications of using big data and IoT in the media sector, including issues such as privacy, security, and bias.
Determining the potential impact of big data and IoT on the media landscape and the role of traditional media in an increasingly digital world.

Exigency computer systems for meteorology and disaster prevention

The research aims to explore the role of exigency computer systems to detect weather and other hazards for disaster prevention and response

Identifying the key components and features of exigency computer systems for meteorology and disaster prevention, such as data sources, analytics tools, and communication channels.
Evaluating the effectiveness of exigency computer systems in providing accurate and timely information about weather and other hazards.
Assessing the impact of exigency computer systems on the ability of decision makers to prepare for and respond to disasters.
Examining the challenges and limitations of using exigency computer systems, such as the need for reliable data sources, the complexity of the systems, or the potential for human error.

Network security and cryptography

Overall, the goal of research is to improve our understanding of how to protect communication and information in the digital age, and to develop practical solutions for addressing the complex and evolving security challenges faced by individuals, organizations, and societies.

Developing new algorithms and protocols for securing communication over networks, such as for data confidentiality, data integrity, and authentication
Investigating the security of existing cryptographic primitives, such as encryption and hashing algorithms, and identifying vulnerabilities that could be exploited by attackers.
Evaluating the effectiveness of different network security technologies and protocols, such as firewalls, intrusion detection systems, and virtual private networks (VPNs), in protecting against different types of attacks.
Exploring the use of cryptography in emerging areas, such as cloud computing, the Internet of Things (IoT), and blockchain, and identifying the unique security challenges and opportunities presented by these domains.
Investigating the trade-offs between security and other factors, such as performance, usability, and cost, and developing strategies for balancing these conflicting priorities.

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Top 15 Big Data Projects (With Source Code)

Introduction, big data project ideas, projects for beginners, intermediate big data projects, advanced projects, big data projects: why are they so important, frequently asked questions, additional resources.

Almost 6,500 million linked gadgets communicate data via the Internet nowadays. This figure will climb to 20,000 million by 2025. This “sea of data” is analyzed by big data to translate it into the information that is reshaping our world. Big data refers to massive data volumes – both organized and unstructured – that bombard enterprises daily. But it’s not simply the type or quantity of data that matters; it’s also what businesses do with it. Big data may be evaluated for insights that help people make better decisions and feel more confident about making key business decisions. Big data refers to vast, diversified amounts of data that are growing at an exponential rate. The volume of data, the velocity or speed with which it is created and collected, and the variety or scope of the data points covered (known as the “three v’s” of big data) are all factors to consider. Big data is frequently derived by data mining and is available in a variety of formats.

Unstructured and structured big data are two types of big data. For large data, the term structured data refers to data that has a set length and format. Numbers, dates, and strings, which are collections of words and numbers, are examples of organized data. Unstructured data is unorganized data that does not fit into a predetermined model or format. It includes information gleaned from social media sources that aid organizations in gathering information on customer demands.

Key Takeaway

Confused about your next job?

Big data is a large amount of diversified information that is arriving in ever-increasing volumes and at ever-increasing speeds.
Big data can be structured (typically numerical, readily formatted, to and saved) or unstructured (often non-numerical, difficult to format and store) (more free-form, less quantifiable).
Big data analysis may benefit nearly every function in a company, but dealing with the clutter and noise can be difficult.
Big data can be gathered willingly through personal devices and applications, through questionnaires, product purchases, and electronic check-ins, as well as publicly published remarks on social networks and websites.
Big data is frequently kept in computer databases and examined with software intended to deal with huge, complicated data sets.

Just knowing the theory of big data isn’t going to get you very far. You’ll need to put what you’ve learned into practice. You may put your big data talents to the test by working on big data projects. Projects are an excellent opportunity to put your abilities to the test. They’re also great for your resume. In this article, we are going to discuss some great Big Data projects that you can work on to showcase your big data skills.

1. Traffic control using Big Data

Big Data initiatives that simulate and predict traffic in real-time have a wide range of applications and advantages. The field of real-time traffic simulation has been modeled successfully. However, anticipating route traffic has long been a challenge. This is because developing predictive models for real-time traffic prediction is a difficult endeavor that involves a lot of latency, large amounts of data, and ever-increasing expenses.

The following project is a Lambda Architecture application that monitors the traffic safety and congestion of each street in Chicago. It depicts current traffic collisions, red light, and speed camera infractions, as well as traffic patterns on 1,250 street segments within the city borders.

These datasets have been taken from the City of Chicago’s open data portal:

Traffic Crashes shows each crash that occurred within city streets as reported in the electronic crash reporting system (E-Crash) at CPD. Citywide data are available starting September 2017.
Red Light Camera Violations reflect the daily number of red light camera violations recorded by the City of Chicago Red Light Program for each camera since 2014.
Speed Camera Violations reflect the daily number of speed camera violations recorded by each camera in Children’s Safety Zones since 2014.
Historical Traffic Congestion Estimates estimates traffic congestion on Chicago’s arterial streets in real-time by monitoring and analyzing GPS traces received from Chicago Transit Authority (CTA) buses.
Current Traffic Congestion Estimate shows current estimated speed for street segments covering 300 miles of arterial roads. Congestion estimates are produced every ten minutes.

The project implements the three layers of the Lambda Architecture:

Batch layer – manages the master dataset (the source of truth), which is an immutable, append-only set of raw data. It pre-computes batch views from the master dataset.
Serving layer – responds to ad-hoc queries by returning pre-computed views (from the batch layer) or building views from the processed data.
Speed layer – deals with up-to-date data only to compensate for the high latency of the batch layer

Source Code – Traffic Control

2. Search Engine

To comprehend what people are looking for, search engines must deal with trillions of network objects and monitor the online behavior of billions of people. Website material is converted into quantifiable data by search engines. The given project is a full-featured search engine built on top of a 75-gigabyte In this project, we will use several datasets like stopwords.txt (A text file containing all the stop words in the current directory of the code) and wiki_dump.xml (The XML file containing the full data of Wikipedia). Wikipedia corpus with sub-second search latency. The results show wiki pages sorted by TF/IDF (stands for Term Frequency — Inverse Document Frequency) relevance based on the search term/s entered. This project addresses latency, indexing, and huge data concerns with an efficient code and the K-Way merge sort method.

Source Code – Search Engine

3. Medical Insurance Fraud Detection

A unique data science model that uses real-time analysis and classification algorithms to assist predict fraud in the medical insurance market. This instrument can be utilized by the government to benefit patients, pharmacies, and doctors, ultimately assisting in improving industry confidence, addressing rising healthcare expenses, and addressing the impact of fraud. Medical services deception is a major problem that costs Medicare/Medicaid and the insurance business a lot of money.

4 different Big Datasets have been joined in this project to get a single table for final data analysis. The datasets collected are:

Part D prescriber services- data such as name of doctor, addres of doctor, disease, symptoms etc.
List of Excluded Individuals and Entities (LEIE) database: This database contains a rundown of people and substances that are prohibited from taking an interest in governmentally financed social insurance programs (for example Medicare) because of past medicinal services extortion.
Payments Received by Physician from Pharmaceuticals
CMS part D dataset- data by Center of Medicare and Medicaid Services

It has been developed by taking consideration of different key features with applying different Machine Learning Algorithms to see which one performs better. The ML algorithms used have been trained to detect any irregularities in the dataset so that the authorities can be alerted.

Source Code – Medical Insurance Fraud

4. Data Warehouse Design for an E-Commerce Site

A data warehouse is essentially a vast collection of data for a company that assists the company in making educated decisions based on data analysis. The data warehouse designed in this project is a central repository for an e-commerce site, containing unified data ranging from searches to purchases made by site visitors. The site can manage supply based on demand (inventory management), logistics, the price for maximum profitability, and advertisements based on searches and things purchased by establishing such a data warehouse. Recommendations can also be made based on tendencies in a certain area, as well as age groups, sex, and other shared interests. This is a data warehouse implementation for an e-commerce website “Infibeam” which sells digital and consumer electronics.

Source Code – Data Warehouse Design

5. Text Mining Project

You will be required to perform text analysis and visualization of the delivered documents as part of this project. For beginners, this is one of the best deep learning project ideas. Text mining is in high demand, and it can help you demonstrate your abilities as a data scientist . You can deploy Natural Language Process Techniques to gain some useful information from the link provided below. The link contains a collection of NLP tools and resources for various languages.

Source Code – Text Mining

6. Big Data Cybersecurity

The major goal of this Big Data project is to use complex multivariate time series data to exploit vulnerability disclosure trends in real-world cybersecurity concerns. This project consists of outlier and anomaly detection technologies based on Hadoop, Spark, and Storm are interwoven with the system’s machine learning and automation engine for real-time fraud detection and intrusion detection to forensics.

For independent Big Data Multi-Inspection / Forensics of high-level risks or volume datasets exceeding local resources, it uses the Ophidia Analytics Framework. Ophidia Analytics Framework is an open-source big data analytics framework that contains cluster-aware parallel operators for data analysis and mining (subsetting, reduction, metadata processing, and so on). The framework is completely connected with Ophidia Server: it takes commands from the server and responds with alerts, allowing processes to run smoothly.

Lumify, an open-source big data analysis, and visualization platform are also included in the Cyber Security System to provide big data analysis and visualization of each instance of fraud or intrusion events into temporary, compartmentalized virtual machines, which creates a full snapshot of the network infrastructure and infected device, allowing for in-depth analytics, forensic review, and providing a transportable threat analysis for Executive level next-steps.

Lumify, a big data analysis and visualization tool developed by Cyberitis is launched using both local and cloud resources (customizable per environment and user). Only the backend servers (Hadoop, Accumulo, Elasticsearch, RabbitMQ, Zookeeper) are included in the Open Source Lumify Dev Virtual Machine. This VM allows developers to get up and running quickly without having to install the entire stack on their development workstations.

Source Code – Big Data Cybersecurity

7. Crime Detection

The following project is a Multi-class classification model for predicting the types of crimes in Toronto city. The developer of the project, using big data ( The dataset collected includes every major crime committed from 2014-2017* in the city of Toronto, with detailed information about the location and time of the offense), has constructed a multi-class classification model using a Random Forest classifier to predict the type of major crime committed based on time of day, neighborhood, division, year, month, etc. using data sourced from Toronto Police.

The use of big data analytics here is to discover crime tendencies automatically. If analysts are given automated, data-driven tools to discover crime patterns, these tools can help police better comprehend crime patterns, allowing for more precise estimates of past crimes and increasing suspicion of suspects.

Source Code – Crime Detection

8. Disease Prediction Based on Symptoms

With the rapid advancement of technology and data, the healthcare domain is one of the most significant study fields in the contemporary era. The enormous amount of patient data is tough to manage. Big Data Analytics makes it easier to manage this information (Electronic Health Records are one of the biggest examples of the application of big data in healthcare). Knowledge derived from big data analysis gives healthcare specialists insights that were not available before. In healthcare, big data is used at every stage of the process, from medical research to patient experience and outcomes. There are numerous ways of treating various ailments throughout the world. Machine Learning and Big Data are new approaches that aid in disease prediction and diagnosis. This research explored how machine learning algorithms can be used to forecast diseases based on symptoms. The following algorithms have been explored in code:

Naive Bayes
Decision Tree
Random Forest
Gradient Boosting

Source Code – Disease Prediction

9. Yelp Review Analysis

Yelp is a forum for users to submit reviews and rate businesses with a star rating. According to studies, an increase of one star resulted in a 59 percent rise in income for independent businesses. As a result, we believe the Yelp dataset has a lot of potential as a powerful insight source. Customer reviews of Yelp is a gold mine waiting to be discovered.

This project’s main goal is to conduct in-depth analyses of seven different cuisine types of restaurants: Korean, Japanese, Chinese, Vietnamese, Thai, French, and Italian, to determine what makes a good restaurant and what concerns customers, and then make recommendations for future improvement and profit growth. We will mostly evaluate customer evaluations to determine why customers like or dislike the business. We can turn the unstructured data (reviews) into actionable insights using big data, allowing businesses to better understand how and why customers prefer their products or services and make business improvements as rapidly as feasible.

Source Code – Review Analysis

10. Recommendation System

Thousands, millions, or even billions of objects, such as merchandise, video clips, movies, music, news, articles, blog entries, advertising, and so on, are typically available through online services. The Google Play Store, for example, has millions of apps and YouTube has billions of videos. Netflix Recommendation Engine, their most effective algorithm, is made up of algorithms that select material based on each user profile. Big data provides plenty of user data such as past purchases, browsing history, and comments for Recommendation systems to deliver relevant and effective recommendations. In a nutshell, without massive data, even the most advanced Recommenders will be ineffective. Big data is the driving force behind our mini-movie recommendation system. Over 3,000 titles are filtered at a time by the engine, which uses 1,300 suggestion clusters depending on user preferences. It’s so accurate that customized recommendations from the engine drive 80 percent of Netflix viewer activity. The goal of this project is to compare the performance of various recommendation models on the Hadoop Framework.

Source Code – Recommendation System

11. Anomaly Detection in Cloud Servers

Anomaly detection is a useful tool for cloud platform managers who want to keep track of and analyze cloud behavior in order to improve cloud reliability. It assists cloud platform managers in detecting unexpected system activity so that preventative actions can be taken before a system crash or service failure occurs.

This project provides a reference implementation of a Cloud Dataflow streaming pipeline that integrates with BigQuery ML, Cloud AI Platform to perform anomaly detection. A key component of the implementation leverages Dataflow for feature extraction & real-time outlier identification which has been tested to analyze over 20TB of data.

Source Code – Anomaly Detection

12. Smart Cities Using Big Data

A smart city is a technologically advanced metropolitan region that collects data using various electronic technologies, voice activation methods, and sensors. The information gleaned from the data is utilized to efficiently manage assets, resources, and services; in turn, the data is used to improve operations throughout the city. Data is collected from citizens, devices, buildings, and assets, which is then processed and analyzed to monitor and manage traffic and transportation systems, power plants, utilities, water supply networks, waste, crime detection, information systems, schools, libraries, hospitals, and other community services. Big data obtains this information and with the help of advanced algorithms, smart network infrastructures and various analytics platforms can implement the sophisticated features of a smart city. This smart city reference pipeline shows how to integrate various media building blocks, with analytics powered by the OpenVINO Toolkit, for traffic or stadium sensing, analytics, and management tasks.

Source Code – Smart Cities

13. Tourist Behavior Analysis

This is one of the most innovative big data project concepts. This Big Data project aims to study visitor behavior to discover travelers’ preferences and most frequented destinations, as well as forecast future tourism demand.

What is the role of big data in the project? Because visitors utilize the internet and other technologies while on vacation, they leave digital traces that Big Data can readily collect and distribute – the majority of the data comes from external sources such as social media sites. The sheer volume of data is simply too much for a standard database to handle, necessitating the use of big data analytics. All the information from these sources can be used to help firms in the aviation, hotel, and tourist industries find new customers and advertise their services. It can also assist tourism organizations in visualizing and forecasting current and future trends.

Source Code – Tourist Behavior Analysis

14. Web Server Log Analysis

A web server log keeps track of page requests as well as the actions it has taken. To further examine the data, web servers can be used to store, analyze, and mine the data. Page advertising can be determined and SEO (search engine optimization) can be performed in this manner. Web-server log analysis can be used to get a sense of the overall user experience. This type of processing is advantageous to any company that relies largely on its website for revenue generation or client communication. This interesting big data project demonstrates parsing (including incorrectly formatted strings) and analysis of web server log data.

Source Code – Web Server Log Analysis

15. Image Caption Generator

Because of the rise of social media and the importance of digital marketing, businesses must now upload engaging content. Visuals that are appealing to the eye are essential, but subtitles that describe the images are also required. The usage of hashtags and attention-getting subtitles might help you reach out to the right people even more. Large datasets with correlated photos and captions must be managed. Image processing and deep learning are used to comprehend the image, and artificial intelligence is used to provide captions that are both relevant and appealing. Big Data source code can be written in Python. The creation of image captions isn’t a beginner-level Big Data project proposal and is indeed challenging. The project given below uses a neural network to generate captions for an image using CNN (Convolution Neural Network) and RNN (Recurrent Neural Network) with BEAM Search (Beam search is a heuristic search algorithm that examines a graph by extending the most promising node in a small collection.

There are currently rich and colorful datasets in the image description generating work, such as MSCOCO, Flickr8k, Flickr30k, PASCAL 1K, AI Challenger Dataset, and STAIR Captions, which are progressively becoming a trend of discussion. The given project utilizes state-of-the-art ML and big data algorithms to build an effective image caption generator.

Source Code – Image Caption Generator

Big Data is a fascinating topic. It helps in the discovery of patterns and outcomes that might otherwise go unnoticed. Big Data is being used by businesses to learn what their customers want, who their best customers are, and why people choose different products. The more information a business has about its customers, the more competitive it is.

It can be combined with Machine Learning to create market strategies based on customer predictions. Companies that use big data become more customer-centric.

This expertise is in high demand and learning it will help you progress your career swiftly. As a result, if you’re new to big data, the greatest thing you can do is brainstorm some big data project ideas.

We’ve examined some of the best big data project ideas in this article. We began with some simple projects that you can complete quickly. After you’ve completed these beginner tasks, I recommend going back to understand a few additional principles before moving on to the intermediate projects. After you’ve gained confidence, you can go on to more advanced projects.

What are the 3 types of big data? Big data is classified into three main types:

Unstructured
Semi-structured

What can big data be used for? Some important use cases of big data are:

Improving Science and research
Improving governance
Smart cities
Understanding and targeting customers
Understanding and Optimizing Business Processes
Improving Healthcare and Public Health
Financial Trading
Optimizing Machine and Device Performance

What industries use big data? Big data finds its application in various domains. Some fields where big data can be used efficiently are:

Travel and tourism
Financial and banking sector
Telecommunication and media
Banking Sector
Government and Military
Social Media
Big Data Tools
Big Data Engineer
Applications of Big Data
Big Data Interview Questions
Big Data Projects

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Our latest analysis shows that the estimated share of adults who live in middle-income households varies widely across the 254 metropolitan areas we examined, from 42% in San Jose-Sunnyvale-Santa Clara, California, to 66% in Olympia-Lacey-Tumwater, Washington. The share of adults who live in lower-income households ranges from 16% in Bismarck, North Dakota, to 46% in Laredo, Texas. The share living in upper-income households is smallest in Muskegon-Norton Shores, Michigan (8%), and greatest in San Jose-Sunnyvale-Santa Clara, California (41%).

How the income calculator works

The calculator takes your household income and adjusts it for the size of your household. The income is revised upward for households that are below average in size and downward for those of above-average size. This way, each household’s income is made equivalent to the income of a three-person household. (Three is the whole number nearest to the average size of a U.S. household , which was 2.5 people in 2023.)

Pew Research Center does not store or share any of the information you enter.

We use your size-adjusted household income and the cost of living in your area to determine your income tier. Middle-income households – those with an income that is two-thirds to double the U.S. median household income – had incomes ranging from about $56,600 to $169,800 in 2022. Lower-income households had incomes less than $56,600, and upper-income households had incomes greater than $169,800. (All figures are computed for three-person households, adjusted for the cost of living in a metropolitan area, and expressed in 2022 dollars.)

The following example illustrates how cost-of-living adjustment for a given area was calculated: Jackson, Tennessee, is a relatively inexpensive area, with a price level in 2022 that was 13.0% less than the national average. The San Francisco-Oakland-Berkeley metropolitan area in California is one of the most expensive, with a price level that was 17.9% higher than the national average. Thus, to step over the national middle-class threshold of $56,600, a household in Jackson needs an income of only about $49,200, or 13.0% less than the national threshold. But a household in the San Francisco area needs an income of about $66,700, or 17.9% more than the U.S. threshold, to be considered middle class.

The income calculator encompasses 254 of 387 metropolitan areas in the United States, as defined by the Office of Management and Budget . If you live outside of one of these 254 areas, the calculator reports the estimates for your state.

The second part of our calculator asks about your education, age, race or ethnicity, and marital status. This allows you to see how other adults who are similar to you demographically are distributed across lower-, middle- and upper-income tiers in the U.S. overall. It does not recompute your economic tier.

Note: This post and interactive calculator were originally published Dec. 9, 2015, and have been updated to reflect the Center’s new analysis. Former Senior Researcher Rakesh Kochhar and former Research Analyst Jesse Bennett also contributed to this analysis.

The Center recently published an analysis of the distribution of the American population across income tiers . In that analysis, the estimates of the overall shares in each income tier are slightly different, because it relies on a separate government data source and includes children as well as adults.

Pew Research Center designed this calculator as a way for users to find out, based on our analysis, where they appear in the distribution of U.S. adults by income tier, as well as how they compare with others who match their demographic profile.

The data underlying the calculator come from the 2022 American Community Survey (ACS). The ACS contains approximately 3 million records, or about 1% of the U.S. population.

In our analysis, “middle-income” Americans are adults whose annual household income is two-thirds to double the national median, after incomes have been adjusted for household size. Lower-income households have incomes less than two-thirds of the median, and upper-income households have incomes more than double the median. American adults refers to those ages 18 and older who reside in a household (as opposed to group quarters).

In 2022, the national middle-income range was about $56,600 to $169,800 annually for a household of three. Lower-income households had incomes less than $56,600, and upper-income households had incomes greater than $169,800. (Incomes are calculated in 2022 dollars.) The median adjusted household income used to derive this middle-income range is based on household heads, regardless of their age.

These income ranges vary with the cost of living in metropolitan areas and with household size. A household in a metropolitan area with a higher-than-average cost of living, or one with more than three people, needs more than $56,600 to be included in the middle-income tier. Households in less expensive areas or with fewer than three people need less than $56,600 to be considered middle income. Additional details on the methodology are available in our earlier analyses .

Income & Wages
Middle Class

Richard Fry is a senior researcher focusing on economics and education at Pew Research Center .

Income inequality is greater among Chinese Americans than any other Asian origin group in the U.S.

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ABOUT PEW RESEARCH CENTER Pew Research Center is a nonpartisan, nonadvocacy fact tank that informs the public about the issues, attitudes and trends shaping the world. It does not take policy positions. The Center conducts public opinion polling, demographic research, computational social science research and other data-driven research. Pew Research Center is a subsidiary of The Pew Charitable Trusts , its primary funder.

Amendment 48: A.5 Carbon Cycle Science Final Text and Due Dates.

A.5 Carbon Cycle Science (CCS) solicits proposals for research focused on the improved understanding of carbon stocks and fluxes between and within ecosystems, and their exchange with the atmosphere. It also targets improved understanding of carbon cycle processes and feedbacks in critical ecosystems, as highlighted by the research topics below:

Carbon Fluxes within the ocean
Carbon Cycle of Critical Ecosystems
Vulnerability of Dryland Ecosystems
Vulnerability of Tropical Forests
Carbon Fluxes between Terrestrial Ecosystems and the Atmosphere

Substantive use of remote sensing and/or airborne data is required in all studies.

ROSES-2024 Amendment 48 releases final text and due dates for A.5 CCS . NOIs are requested by October 17, 2024, and proposals are due February 3, 2025. Proposals submitted to this program will be evaluated using a dual-anonymous review process. See Section 5 and in the associated "Guidelines for Anonymous Proposals" document under "Other Documents" on the NSPIRES page for this program element.

On or about September 16, 2024, this Amendment to the NASA Research Announcement "Research Opportunities in Space and Earth Sciences (ROSES) 2024" (NNH24ZDA001N) will be posted on the NASA research opportunity homepage at https://solicitation.nasaprs.com/ROSES2024

Questions concerning A.5 CCS may be directed to Laura Lorenzoni at [email protected] and/or Ryan Pavlick at [email protected] .

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A.5 Carbon Cycle Science (CCS) solicits proposals for research focused on the improved understanding of carbon stocks and fluxes between and within ecosystems, and their exchange with the atmosphere. It also targets improved understanding of carbon cycle processes and feedbacks in critical ecosystems, as highlighted by the research topics below: Substantive use of remote sensing […]