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214 Best Big Data Research Topics for Your Thesis Paper
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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.
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Research Topics & Ideas: Data Science
50 Topic Ideas To Kickstart Your Research Project
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If you’re just starting out exploring data science-related topics for your dissertation, thesis or research project, you’ve come to the right place. In this post, we’ll help kickstart your research by providing a hearty list of data science and analytics-related research ideas , including examples from recent studies.
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.
To develop a suitable research topic, you’ll need to identify a clear and convincing research gap , and a viable plan to fill that gap. If this sounds foreign to you, check out our free research topic webinar that explores how to find and refine a high-quality research topic, from scratch. Alternatively, consider our 1-on-1 coaching service .
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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.
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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
While the ideas we’ve presented above are a decent starting point for finding a research topic, they are fairly generic and non-specific. So, it helps to look at actual studies in the data science and analytics space to see how this all comes together in practice.
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)
As you can see, these research topics are a lot more focused than the generic topic ideas we presented earlier. So, for you to develop a high-quality research topic, you’ll need to get specific and laser-focused on a specific context with specific variables of interest. In the video below, we explore some other important things you’ll need to consider when crafting your research topic.
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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 .
By taking advantage of our best digital marketing research topics in data science you can be assured of producing an innovative research project that will impress your research professors and make a huge difference in attracting the right employers.
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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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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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- Published: 06 January 2022
The use of Big Data Analytics in healthcare
- Kornelia Batko ORCID: orcid.org/0000-0001-6561-3826 1 &
- Andrzej Ślęzak 2
Journal of Big Data volume 9 , Article number: 3 ( 2022 ) Cite this article
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The introduction of Big Data Analytics (BDA) in healthcare will allow to use new technologies both in treatment of patients and health management. The paper aims at analyzing the possibilities of using Big Data Analytics in healthcare. The research is based on a critical analysis of the literature, as well as the presentation of selected results of direct research on the use of Big Data Analytics in medical facilities. The direct research was carried out based on research questionnaire and conducted on a sample of 217 medical facilities in Poland. Literature studies have shown that the use of Big Data Analytics can bring many benefits to medical facilities, while direct research has shown that medical facilities in Poland are moving towards data-based healthcare because they use structured and unstructured data, reach for analytics in the administrative, business and clinical area. The research positively confirmed that medical facilities are working on both structural data and unstructured data. The following kinds and sources of data can be distinguished: from databases, transaction data, unstructured content of emails and documents, data from devices and sensors. However, the use of data from social media is lower as in their activity they reach for analytics, not only in the administrative and business but also in the clinical area. It clearly shows that the decisions made in medical facilities are highly data-driven. The results of the study confirm what has been analyzed in the literature that medical facilities are moving towards data-based healthcare, together with its benefits.
Introduction
The main contribution of this paper is to present an analytical overview of using structured and unstructured data (Big Data) analytics in medical facilities in Poland. Medical facilities use both structured and unstructured data in their practice. Structured data has a predetermined schema, it is extensive, freeform, and comes in variety of forms [ 27 ]. In contrast, unstructured data, referred to as Big Data (BD), does not fit into the typical data processing format. Big Data is a massive amount of data sets that cannot be stored, processed, or analyzed using traditional tools. It remains stored but not analyzed. Due to the lack of a well-defined schema, it is difficult to search and analyze such data and, therefore, it requires a specific technology and method to transform it into value [ 20 , 68 ]. Integrating data stored in both structured and unstructured formats can add significant value to an organization [ 27 ]. Organizations must approach unstructured data in a different way. Therefore, the potential is seen in Big Data Analytics (BDA). Big Data Analytics are techniques and tools used to analyze and extract information from Big Data. The results of Big Data analysis can be used to predict the future. They also help in creating trends about the past. When it comes to healthcare, it allows to analyze large datasets from thousands of patients, identifying clusters and correlation between datasets, as well as developing predictive models using data mining techniques [ 60 ].
This paper is the first study to consolidate and characterize the use of Big Data from different perspectives. The first part consists of a brief literature review of studies on Big Data (BD) and Big Data Analytics (BDA), while the second part presents results of direct research aimed at diagnosing the use of big data analyses in medical facilities in Poland.
Healthcare is a complex system with varied stakeholders: patients, doctors, hospitals, pharmaceutical companies and healthcare decision-makers. This sector is also limited by strict rules and regulations. However, worldwide one may observe a departure from the traditional doctor-patient approach. The doctor becomes a partner and the patient is involved in the therapeutic process [ 14 ]. Healthcare is no longer focused solely on the treatment of patients. The priority for decision-makers should be to promote proper health attitudes and prevent diseases that can be avoided [ 81 ]. This became visible and important especially during the Covid-19 pandemic [ 44 ].
The next challenges that healthcare will have to face is the growing number of elderly people and a decline in fertility. Fertility rates in the country are found below the reproductive minimum necessary to keep the population stable [ 10 ]. The reflection of both effects, namely the increase in age and lower fertility rates, are demographic load indicators, which is constantly growing. Forecasts show that providing healthcare in the form it is provided today will become impossible in the next 20 years [ 70 ]. It is especially visible now during the Covid-19 pandemic when healthcare faced quite a challenge related to the analysis of huge data amounts and the need to identify trends and predict the spread of the coronavirus. The pandemic showed it even more that patients should have access to information about their health condition, the possibility of digital analysis of this data and access to reliable medical support online. Health monitoring and cooperation with doctors in order to prevent diseases can actually revolutionize the healthcare system. One of the most important aspects of the change necessary in healthcare is putting the patient in the center of the system.
Technology is not enough to achieve these goals. Therefore, changes should be made not only at the technological level but also in the management and design of complete healthcare processes and what is more, they should affect the business models of service providers. The use of Big Data Analytics is becoming more and more common in enterprises [ 17 , 54 ]. However, medical enterprises still cannot keep up with the information needs of patients, clinicians, administrators and the creator’s policy. The adoption of a Big Data approach would allow the implementation of personalized and precise medicine based on personalized information, delivered in real time and tailored to individual patients.
To achieve this goal, it is necessary to implement systems that will be able to learn quickly about the data generated by people within clinical care and everyday life. This will enable data-driven decision making, receiving better personalized predictions about prognosis and responses to treatments; a deeper understanding of the complex factors and their interactions that influence health at the patient level, the health system and society, enhanced approaches to detecting safety problems with drugs and devices, as well as more effective methods of comparing prevention, diagnostic, and treatment options [ 40 ].
In the literature, there is a lot of research showing what opportunities can be offered to companies by big data analysis and what data can be analyzed. However, there are few studies showing how data analysis in the area of healthcare is performed, what data is used by medical facilities and what analyses and in which areas they carry out. This paper aims to fill this gap by presenting the results of research carried out in medical facilities in Poland. The goal is to analyze the possibilities of using Big Data Analytics in healthcare, especially in Polish conditions. In particular, the paper is aimed at determining what data is processed by medical facilities in Poland, what analyses they perform and in what areas, and how they assess their analytical maturity. In order to achieve this goal, a critical analysis of the literature was performed, and the direct research was based on a research questionnaire conducted on a sample of 217 medical facilities in Poland. It was hypothesized that medical facilities in Poland are working on both structured and unstructured data and moving towards data-based healthcare and its benefits. Examining the maturity of healthcare facilities in the use of Big Data and Big Data Analytics is crucial in determining the potential future benefits that the healthcare sector can gain from Big Data Analytics. There is also a pressing need to predicate whether, in the coming years, healthcare will be able to cope with the threats and challenges it faces.
This paper is divided into eight parts. The first is the introduction which provides background and the general problem statement of this research. In the second part, this paper discusses considerations on use of Big Data and Big Data Analytics in Healthcare, and then, in the third part, it moves on to challenges and potential benefits of using Big Data Analytics in healthcare. The next part involves the explanation of the proposed method. The result of direct research and discussion are presented in the fifth part, while the following part of the paper is the conclusion. The seventh part of the paper presents practical implications. The final section of the paper provides limitations and directions for future research.
Considerations on use Big Data and Big Data Analytics in the healthcare
In recent years one can observe a constantly increasing demand for solutions offering effective analytical tools. This trend is also noticeable in the analysis of large volumes of data (Big Data, BD). Organizations are looking for ways to use the power of Big Data to improve their decision making, competitive advantage or business performance [ 7 , 54 ]. Big Data is considered to offer potential solutions to public and private organizations, however, still not much is known about the outcome of the practical use of Big Data in different types of organizations [ 24 ].
As already mentioned, in recent years, healthcare management worldwide has been changed from a disease-centered model to a patient-centered model, even in value-based healthcare delivery model [ 68 ]. In order to meet the requirements of this model and provide effective patient-centered care, it is necessary to manage and analyze healthcare Big Data.
The issue often raised when it comes to the use of data in healthcare is the appropriate use of Big Data. Healthcare has always generated huge amounts of data and nowadays, the introduction of electronic medical records, as well as the huge amount of data sent by various types of sensors or generated by patients in social media causes data streams to constantly grow. Also, the medical industry generates significant amounts of data, including clinical records, medical images, genomic data and health behaviors. Proper use of the data will allow healthcare organizations to support clinical decision-making, disease surveillance, and public health management. The challenge posed by clinical data processing involves not only the quantity of data but also the difficulty in processing it.
In the literature one can find many different definitions of Big Data. This concept has evolved in recent years, however, it is still not clearly understood. Nevertheless, despite the range and differences in definitions, Big Data can be treated as a: large amount of digital data, large data sets, tool, technology or phenomenon (cultural or technological.
Big Data can be considered as massive and continually generated digital datasets that are produced via interactions with online technologies [ 53 ]. Big Data can be defined as datasets that are of such large sizes that they pose challenges in traditional storage and analysis techniques [ 28 ]. A similar opinion about Big Data was presented by Ohlhorst who sees Big Data as extremely large data sets, possible neither to manage nor to analyze with traditional data processing tools [ 57 ]. In his opinion, the bigger the data set, the more difficult it is to gain any value from it.
In turn, Knapp perceived Big Data as tools, processes and procedures that allow an organization to create, manipulate and manage very large data sets and storage facilities [ 38 ]. From this point of view, Big Data is identified as a tool to gather information from different databases and processes, allowing users to manage large amounts of data.
Similar perception of the term ‘Big Data’ is shown by Carter. According to him, Big Data technologies refer to a new generation of technologies and architectures, designed to economically extract value from very large volumes of a wide variety of data by enabling high velocity capture, discovery and/or analysis [ 13 ].
Jordan combines these two approaches by identifying Big Data as a complex system, as it needs data bases for data to be stored in, programs and tools to be managed, as well as expertise and personnel able to retrieve useful information and visualization to be understood [ 37 ].
Following the definition of Laney for Big Data, it can be state that: it is large amount of data generated in very fast motion and it contains a lot of content [ 43 ]. Such data comes from unstructured sources, such as stream of clicks on the web, social networks (Twitter, blogs, Facebook), video recordings from the shops, recording of calls in a call center, real time information from various kinds of sensors, RFID, GPS devices, mobile phones and other devices that identify and monitor something [ 8 ]. Big Data is a powerful digital data silo, raw, collected with all sorts of sources, unstructured and difficult, or even impossible, to analyze using conventional techniques used so far to relational databases.
While describing Big Data, it cannot be overlooked that the term refers more to a phenomenon than to specific technology. Therefore, instead of defining this phenomenon, trying to describe them, more authors are describing Big Data by giving them characteristics included a collection of V’s related to its nature [ 2 , 3 , 23 , 25 , 58 ]:
Volume (refers to the amount of data and is one of the biggest challenges in Big Data Analytics),
Velocity (speed with which new data is generated, the challenge is to be able to manage data effectively and in real time),
Variety (heterogeneity of data, many different types of healthcare data, the challenge is to derive insights by looking at all available heterogenous data in a holistic manner),
Variability (inconsistency of data, the challenge is to correct the interpretation of data that can vary significantly depending on the context),
Veracity (how trustworthy the data is, quality of the data),
Visualization (ability to interpret data and resulting insights, challenging for Big Data due to its other features as described above).
Value (the goal of Big Data Analytics is to discover the hidden knowledge from huge amounts of data).
Big Data is defined as an information asset with high volume, velocity, and variety, which requires specific technology and method for its transformation into value [ 21 , 77 ]. Big Data is also a collection of information about high-volume, high volatility or high diversity, requiring new forms of processing in order to support decision-making, discovering new phenomena and process optimization [ 5 , 7 ]. Big Data is too large for traditional data-processing systems and software tools to capture, store, manage and analyze, therefore it requires new technologies [ 28 , 50 , 61 ] to manage (capture, aggregate, process) its volume, velocity and variety [ 9 ].
Undoubtedly, Big Data differs from the data sources used so far by organizations. Therefore, organizations must approach this type of unstructured data in a different way. First of all, organizations must start to see data as flows and not stocks—this entails the need to implement the so-called streaming analytics [ 48 ]. The mentioned features make it necessary to use new IT tools that allow the fullest use of new data [ 58 ]. The Big Data idea, inseparable from the huge increase in data available to various organizations or individuals, creates opportunities for access to valuable analyses, conclusions and enables making more accurate decisions [ 6 , 11 , 59 ].
The Big Data concept is constantly evolving and currently it does not focus on huge amounts of data, but rather on the process of creating value from this data [ 52 ]. Big Data is collected from various sources that have different data properties and are processed by different organizational units, resulting in creation of a Big Data chain [ 36 ]. The aim of the organizations is to manage, process and analyze Big Data. In the healthcare sector, Big Data streams consist of various types of data, namely [ 8 , 51 ]:
clinical data, i.e. data obtained from electronic medical records, data from hospital information systems, image centers, laboratories, pharmacies and other organizations providing health services, patient generated health data, physician’s free-text notes, genomic data, physiological monitoring data [ 4 ],
biometric data provided from various types of devices that monitor weight, pressure, glucose level, etc.,
financial data, constituting a full record of economic operations reflecting the conducted activity,
data from scientific research activities, i.e. results of research, including drug research, design of medical devices and new methods of treatment,
data provided by patients, including description of preferences, level of satisfaction, information from systems for self-monitoring of their activity: exercises, sleep, meals consumed, etc.
data from social media.
These data are provided not only by patients but also by organizations and institutions, as well as by various types of monitoring devices, sensors or instruments [ 16 ]. Data that has been generated so far in the healthcare sector is stored in both paper and digital form. Thus, the essence and the specificity of the process of Big Data analyses means that organizations need to face new technological and organizational challenges [ 67 ]. The healthcare sector has always generated huge amounts of data and this is connected, among others, with the need to store medical records of patients. However, the problem with Big Data in healthcare is not limited to an overwhelming volume but also an unprecedented diversity in terms of types, data formats and speed with which it should be analyzed in order to provide the necessary information on an ongoing basis [ 3 ]. It is also difficult to apply traditional tools and methods for management of unstructured data [ 67 ]. Due to the diversity and quantity of data sources that are growing all the time, advanced analytical tools and technologies, as well as Big Data analysis methods which can meet and exceed the possibilities of managing healthcare data, are needed [ 3 , 68 ].
Therefore, the potential is seen in Big Data analyses, especially in the aspect of improving the quality of medical care, saving lives or reducing costs [ 30 ]. Extracting from this tangle of given association rules, patterns and trends will allow health service providers and other stakeholders in the healthcare sector to offer more accurate and more insightful diagnoses of patients, personalized treatment, monitoring of the patients, preventive medicine, support of medical research and health population, as well as better quality of medical services and patient care while, at the same time, the ability to reduce costs (Fig. 1 ).
![thesis in big data figure 1](https://media.springernature.com/lw685/springer-static/image/art%3A10.1186%2Fs40537-021-00553-4/MediaObjects/40537_2021_553_Fig1_HTML.png)
(Source: Own elaboration)
Healthcare Big Data Analytics applications
The main challenge with Big Data is how to handle such a large amount of information and use it to make data-driven decisions in plenty of areas [ 64 ]. In the context of healthcare data, another major challenge is to adjust big data storage, analysis, presentation of analysis results and inference basing on them in a clinical setting. Data analytics systems implemented in healthcare are designed to describe, integrate and present complex data in an appropriate way so that it can be understood better (Fig. 2 ). This would improve the efficiency of acquiring, storing, analyzing and visualizing big data from healthcare [ 71 ].
![thesis in big data figure 2](https://media.springernature.com/lw685/springer-static/image/art%3A10.1186%2Fs40537-021-00553-4/MediaObjects/40537_2021_553_Fig2_HTML.png)
Process of Big Data Analytics
The result of data processing with the use of Big Data Analytics is appropriate data storytelling which may contribute to making decisions with both lower risk and data support. This, in turn, can benefit healthcare stakeholders. To take advantage of the potential massive amounts of data in healthcare and to ensure that the right intervention to the right patient is properly timed, personalized, and potentially beneficial to all components of the healthcare system such as the payer, patient, and management, analytics of large datasets must connect communities involved in data analytics and healthcare informatics [ 49 ]. Big Data Analytics can provide insight into clinical data and thus facilitate informed decision-making about the diagnosis and treatment of patients, prevention of diseases or others. Big Data Analytics can also improve the efficiency of healthcare organizations by realizing the data potential [ 3 , 62 ].
Big Data Analytics in medicine and healthcare refers to the integration and analysis of a large amount of complex heterogeneous data, such as various omics (genomics, epigenomics, transcriptomics, proteomics, metabolomics, interactomics, pharmacogenetics, deasomics), biomedical data, talemedicine data (sensors, medical equipment data) and electronic health records data [ 46 , 65 ].
When analyzing the phenomenon of Big Data in the healthcare sector, it should be noted that it can be considered from the point of view of three areas: epidemiological, clinical and business.
From a clinical point of view, the Big Data analysis aims to improve the health and condition of patients, enable long-term predictions about their health status and implementation of appropriate therapeutic procedures. Ultimately, the use of data analysis in medicine is to allow the adaptation of therapy to a specific patient, that is personalized medicine (precision, personalized medicine).
From an epidemiological point of view, it is desirable to obtain an accurate prognosis of morbidity in order to implement preventive programs in advance.
In the business context, Big Data analysis may enable offering personalized packages of commercial services or determining the probability of individual disease and infection occurrence. It is worth noting that Big Data means not only the collection and processing of data but, most of all, the inference and visualization of data necessary to obtain specific business benefits.
In order to introduce new management methods and new solutions in terms of effectiveness and transparency, it becomes necessary to make data more accessible, digital, searchable, as well as analyzed and visualized.
Erickson and Rothberg state that the information and data do not reveal their full value until insights are drawn from them. Data becomes useful when it enhances decision making and decision making is enhanced only when analytical techniques are used and an element of human interaction is applied [ 22 ].
Thus, healthcare has experienced much progress in usage and analysis of data. A large-scale digitalization and transparency in this sector is a key statement of almost all countries governments policies. For centuries, the treatment of patients was based on the judgment of doctors who made treatment decisions. In recent years, however, Evidence-Based Medicine has become more and more important as a result of it being related to the systematic analysis of clinical data and decision-making treatment based on the best available information [ 42 ]. In the healthcare sector, Big Data Analytics is expected to improve the quality of life and reduce operational costs [ 72 , 82 ]. Big Data Analytics enables organizations to improve and increase their understanding of the information contained in data. It also helps identify data that provides insightful insights for current as well as future decisions [ 28 ].
Big Data Analytics refers to technologies that are grounded mostly in data mining: text mining, web mining, process mining, audio and video analytics, statistical analysis, network analytics, social media analytics and web analytics [ 16 , 25 , 31 ]. Different data mining techniques can be applied on heterogeneous healthcare data sets, such as: anomaly detection, clustering, classification, association rules as well as summarization and visualization of those Big Data sets [ 65 ]. Modern data analytics techniques explore and leverage unique data characteristics even from high-speed data streams and sensor data [ 15 , 16 , 31 , 55 ]. Big Data can be used, for example, for better diagnosis in the context of comprehensive patient data, disease prevention and telemedicine (in particular when using real-time alerts for immediate care), monitoring patients at home, preventing unnecessary hospital visits, integrating medical imaging for a wider diagnosis, creating predictive analytics, reducing fraud and improving data security, better strategic planning and increasing patients’ involvement in their own health.
Big Data Analytics in healthcare can be divided into [ 33 , 73 , 74 ]:
descriptive analytics in healthcare is used to understand past and current healthcare decisions, converting data into useful information for understanding and analyzing healthcare decisions, outcomes and quality, as well as making informed decisions [ 33 ]. It can be used to create reports (i.e. about patients’ hospitalizations, physicians’ performance, utilization management), visualization, customized reports, drill down tables, or running queries on the basis of historical data.
predictive analytics operates on past performance in an effort to predict the future by examining historical or summarized health data, detecting patterns of relationships in these data, and then extrapolating these relationships to forecast. It can be used to i.e. predict the response of different patient groups to different drugs (dosages) or reactions (clinical trials), anticipate risk and find relationships in health data and detect hidden patterns [ 62 ]. In this way, it is possible to predict the epidemic spread, anticipate service contracts and plan healthcare resources. Predictive analytics is used in proper diagnosis and for appropriate treatments to be given to patients suffering from certain diseases [ 39 ].
prescriptive analytics—occurs when health problems involve too many choices or alternatives. It uses health and medical knowledge in addition to data or information. Prescriptive analytics is used in many areas of healthcare, including drug prescriptions and treatment alternatives. Personalized medicine and evidence-based medicine are both supported by prescriptive analytics.
discovery analytics—utilizes knowledge about knowledge to discover new “inventions” like drugs (drug discovery), previously unknown diseases and medical conditions, alternative treatments, etc.
Although the models and tools used in descriptive, predictive, prescriptive, and discovery analytics are different, many applications involve all four of them [ 62 ]. Big Data Analytics in healthcare can help enable personalized medicine by identifying optimal patient-specific treatments. This can influence the improvement of life standards, reduce waste of healthcare resources and save costs of healthcare [ 56 , 63 , 71 ]. The introduction of large data analysis gives new analytical possibilities in terms of scope, flexibility and visualization. Techniques such as data mining (computational pattern discovery process in large data sets) facilitate inductive reasoning and analysis of exploratory data, enabling scientists to identify data patterns that are independent of specific hypotheses. As a result, predictive analysis and real-time analysis becomes possible, making it easier for medical staff to start early treatments and reduce potential morbidity and mortality. In addition, document analysis, statistical modeling, discovering patterns and topics in document collections and data in the EHR, as well as an inductive approach can help identify and discover relationships between health phenomena.
Advanced analytical techniques can be used for a large amount of existing (but not yet analytical) data on patient health and related medical data to achieve a better understanding of the information and results obtained, as well as to design optimal clinical pathways [ 62 ]. Big Data Analytics in healthcare integrates analysis of several scientific areas such as bioinformatics, medical imaging, sensor informatics, medical informatics and health informatics [ 65 ]. Big Data Analytics in healthcare allows to analyze large datasets from thousands of patients, identifying clusters and correlation between datasets, as well as developing predictive models using data mining techniques [ 65 ]. Discussing all the techniques used for Big Data Analytics goes beyond the scope of a single article [ 25 ].
The success of Big Data analysis and its accuracy depend heavily on the tools and techniques used to analyze the ability to provide reliable, up-to-date and meaningful information to various stakeholders [ 12 ]. It is believed that the implementation of big data analytics by healthcare organizations could bring many benefits in the upcoming years, including lowering health care costs, better diagnosis and prediction of diseases and their spread, improving patient care and developing protocols to prevent re-hospitalization, optimizing staff, optimizing equipment, forecasting the need for hospital beds, operating rooms, treatments, and improving the drug supply chain [ 71 ].
Challenges and potential benefits of using Big Data Analytics in healthcare
Modern analytics gives possibilities not only to have insight in historical data, but also to have information necessary to generate insight into what may happen in the future. Even when it comes to prediction of evidence-based actions. The emphasis on reform has prompted payers and suppliers to pursue data analysis to reduce risk, detect fraud, improve efficiency and save lives. Everyone—payers, providers, even patients—are focusing on doing more with fewer resources. Thus, some areas in which enhanced data and analytics can yield the greatest results include various healthcare stakeholders (Table 1 ).
Healthcare organizations see the opportunity to grow through investments in Big Data Analytics. In recent years, by collecting medical data of patients, converting them into Big Data and applying appropriate algorithms, reliable information has been generated that helps patients, physicians and stakeholders in the health sector to identify values and opportunities [ 31 ]. It is worth noting that there are many changes and challenges in the structure of the healthcare sector. Digitization and effective use of Big Data in healthcare can bring benefits to every stakeholder in this sector. A single doctor would benefit the same as the entire healthcare system. Potential opportunities to achieve benefits and effects from Big Data in healthcare can be divided into four groups [ 8 ]:
Improving the quality of healthcare services:
assessment of diagnoses made by doctors and the manner of treatment of diseases indicated by them based on the decision support system working on Big Data collections,
detection of more effective, from a medical point of view, and more cost-effective ways to diagnose and treat patients,
analysis of large volumes of data to reach practical information useful for identifying needs, introducing new health services, preventing and overcoming crises,
prediction of the incidence of diseases,
detecting trends that lead to an improvement in health and lifestyle of the society,
analysis of the human genome for the introduction of personalized treatment.
Supporting the work of medical personnel
doctors’ comparison of current medical cases to cases from the past for better diagnosis and treatment adjustment,
detection of diseases at earlier stages when they can be more easily and quickly cured,
detecting epidemiological risks and improving control of pathogenic spots and reaction rates,
identification of patients who are predicted to have the highest risk of specific, life-threatening diseases by collating data on the history of the most common diseases, in healing people with reports entering insurance companies,
health management of each patient individually (personalized medicine) and health management of the whole society,
capturing and analyzing large amounts of data from hospitals and homes in real time, life monitoring devices to monitor safety and predict adverse events,
analysis of patient profiles to identify people for whom prevention should be applied, lifestyle change or preventive care approach,
the ability to predict the occurrence of specific diseases or worsening of patients’ results,
predicting disease progression and its determinants, estimating the risk of complications,
detecting drug interactions and their side effects.
Supporting scientific and research activity
supporting work on new drugs and clinical trials thanks to the possibility of analyzing “all data” instead of selecting a test sample,
the ability to identify patients with specific, biological features that will take part in specialized clinical trials,
selecting a group of patients for which the tested drug is likely to have the desired effect and no side effects,
using modeling and predictive analysis to design better drugs and devices.
Business and management
reduction of costs and counteracting abuse and counseling practices,
faster and more effective identification of incorrect or unauthorized financial operations in order to prevent abuse and eliminate errors,
increase in profitability by detecting patients generating high costs or identifying doctors whose work, procedures and treatment methods cost the most and offering them solutions that reduce the amount of money spent,
identification of unnecessary medical activities and procedures, e.g. duplicate tests.
According to research conducted by Wang, Kung and Byrd, Big Data Analytics benefits can be classified into five categories: IT infrastructure benefits (reducing system redundancy, avoiding unnecessary IT costs, transferring data quickly among healthcare IT systems, better use of healthcare systems, processing standardization among various healthcare IT systems, reducing IT maintenance costs regarding data storage), operational benefits (improving the quality and accuracy of clinical decisions, processing a large number of health records in seconds, reducing the time of patient travel, immediate access to clinical data to analyze, shortening the time of diagnostic test, reductions in surgery-related hospitalizations, exploring inconceivable new research avenues), organizational benefits (detecting interoperability problems much more quickly than traditional manual methods, improving cross-functional communication and collaboration among administrative staffs, researchers, clinicians and IT staffs, enabling data sharing with other institutions and adding new services, content sources and research partners), managerial benefits (gaining quick insights about changing healthcare trends in the market, providing members of the board and heads of department with sound decision-support information on the daily clinical setting, optimizing business growth-related decisions) and strategic benefits (providing a big picture view of treatment delivery for meeting future need, creating high competitive healthcare services) [ 73 ].
The above specification does not constitute a full list of potential areas of use of Big Data Analysis in healthcare because the possibilities of using analysis are practically unlimited. In addition, advanced analytical tools allow to analyze data from all possible sources and conduct cross-analyses to provide better data insights [ 26 ]. For example, a cross-analysis can refer to a combination of patient characteristics, as well as costs and care results that can help identify the best, in medical terms, and the most cost-effective treatment or treatments and this may allow a better adjustment of the service provider’s offer [ 62 ].
In turn, the analysis of patient profiles (e.g. segmentation and predictive modeling) allows identification of people who should be subject to prophylaxis, prevention or should change their lifestyle [ 8 ]. Shortened list of benefits for Big Data Analytics in healthcare is presented in paper [ 3 ] and consists of: better performance, day-to-day guides, detection of diseases in early stages, making predictive analytics, cost effectiveness, Evidence Based Medicine and effectiveness in patient treatment.
Summarizing, healthcare big data represents a huge potential for the transformation of healthcare: improvement of patients’ results, prediction of outbreaks of epidemics, valuable insights, avoidance of preventable diseases, reduction of the cost of healthcare delivery and improvement of the quality of life in general [ 1 ]. Big Data also generates many challenges such as difficulties in data capture, data storage, data analysis and data visualization [ 15 ]. The main challenges are connected with the issues of: data structure (Big Data should be user-friendly, transparent, and menu-driven but it is fragmented, dispersed, rarely standardized and difficult to aggregate and analyze), security (data security, privacy and sensitivity of healthcare data, there are significant concerns related to confidentiality), data standardization (data is stored in formats that are not compatible with all applications and technologies), storage and transfers (especially costs associated with securing, storing, and transferring unstructured data), managerial skills, such as data governance, lack of appropriate analytical skills and problems with Real-Time Analytics (health care is to be able to utilize Big Data in real time) [ 4 , 34 , 41 ].
The research is based on a critical analysis of the literature, as well as the presentation of selected results of direct research on the use of Big Data Analytics in medical facilities in Poland.
Presented research results are part of a larger questionnaire form on Big Data Analytics. The direct research was based on an interview questionnaire which contained 100 questions with 5-point Likert scale (1—strongly disagree, 2—I rather disagree, 3—I do not agree, nor disagree, 4—I rather agree, 5—I definitely agree) and 4 metrics questions. The study was conducted in December 2018 on a sample of 217 medical facilities (110 private, 107 public). The research was conducted by a specialized market research agency: Center for Research and Expertise of the University of Economics in Katowice.
When it comes to direct research, the selected entities included entities financed from public sources—the National Health Fund (23.5%), and entities operating commercially (11.5%). In the surveyed group of entities, more than a half (64.9%) are hybrid financed, both from public and commercial sources. The diversity of the research sample also applies to the size of the entities, defined by the number of employees. Taking into account proportions of the surveyed entities, it should be noted that in the sector structure, medium-sized (10–50 employees—34% of the sample) and large (51–250 employees—27%) entities dominate. The research was of all-Poland nature, and the entities included in the research sample come from all of the voivodships. The largest group were entities from Łódzkie (32%), Śląskie (18%) and Mazowieckie (18%) voivodships, as these voivodships have the largest number of medical institutions. Other regions of the country were represented by single units. The selection of the research sample was random—layered. As part of medical facilities database, groups of private and public medical facilities have been identified and the ones to which the questionnaire was targeted were drawn from each of these groups. The analyses were performed using the GNU PSPP 0.10.2 software.
The aim of the study was to determine whether medical facilities in Poland use Big Data Analytics and if so, in which areas. Characteristics of the research sample is presented in Table 2 .
The research is non-exhaustive due to the incomplete and uneven regional distribution of the samples, overrepresented in three voivodeships (Łódzkie, Mazowieckie and Śląskie). The size of the research sample (217 entities) allows the authors of the paper to formulate specific conclusions on the use of Big Data in the process of its management.
For the purpose of this paper, the following research hypotheses were formulated: (1) medical facilities in Poland are working on both structured and unstructured data (2) medical facilities in Poland are moving towards data-based healthcare and its benefits.
The paper poses the following research questions and statements that coincide with the selected questions from the research questionnaire:
From what sources do medical facilities obtain data? What types of data are used by the particular organization, whether structured or unstructured, and to what extent?
From what sources do medical facilities obtain data?
In which area organizations are using data and analytical systems (clinical or business)?
Is data analytics performed based on historical data or are predictive analyses also performed?
Determining whether administrative and medical staff receive complete, accurate and reliable data in a timely manner?
Determining whether real-time analyses are performed to support the particular organization’s activities.
Results and discussion
On the basis of the literature analysis and research study, a set of questions and statements related to the researched area was formulated. The results from the surveys show that medical facilities use a variety of data sources in their operations. These sources are both structured and unstructured data (Table 3 ).
According to the data provided by the respondents, considering the first statement made in the questionnaire, almost half of the medical institutions (47.58%) agreed that they rather collect and use structured data (e.g. databases and data warehouses, reports to external entities) and 10.57% entirely agree with this statement. As much as 23.35% of representatives of medical institutions stated “I agree or disagree”. Other medical facilities do not collect and use structured data (7.93%) and 6.17% strongly disagree with the first statement. Also, the median calculated based on the obtained results (median: 4), proves that medical facilities in Poland collect and use structured data (Table 4 ).
In turn, 28.19% of the medical institutions agreed that they rather collect and use unstructured data and as much as 9.25% entirely agree with this statement. The number of representatives of medical institutions that stated “I agree or disagree” was 27.31%. Other medical facilities do not collect and use structured data (17.18%) and 13.66% strongly disagree with the first statement. In the case of unstructured data the median is 3, which means that the collection and use of this type of data by medical facilities in Poland is lower.
In the further part of the analysis, it was checked whether the size of the medical facility and form of ownership have an impact on whether it analyzes unstructured data (Tables 4 and 5 ). In order to find this out, correlation coefficients were calculated.
Based on the calculations, it can be concluded that there is a small statistically monotonic correlation between the size of the medical facility and its collection and use of structured data (p < 0.001; τ = 0.16). This means that the use of structured data is slightly increasing in larger medical facilities. The size of the medical facility is more important according to use of unstructured data (p < 0.001; τ = 0.23) (Table 4 .).
To determine whether the form of medical facility ownership affects data collection, the Mann–Whitney U test was used. The calculations show that the form of ownership does not affect what data the organization collects and uses (Table 5 ).
Detailed information on the sources of from which medical facilities collect and use data is presented in the Table 6 .
The questionnaire results show that medical facilities are especially using information published in databases, reports to external units and transaction data, but they also use unstructured data from e-mails, medical devices, sensors, phone calls, audio and video data (Table 6 ). Data from social media, RFID and geolocation data are used to a small extent. Similar findings are concluded in the literature studies.
From the analysis of the answers given by the respondents, more than half of the medical facilities have integrated hospital system (HIS) implemented. As much as 43.61% use integrated hospital system and 16.30% use it extensively (Table 7 ). 19.38% of exanimated medical facilities do not use it at all. Moreover, most of the examined medical facilities (34.80% use it, 32.16% use extensively) conduct medical documentation in an electronic form, which gives an opportunity to use data analytics. Only 4.85% of medical facilities don’t use it at all.
Other problems that needed to be investigated were: whether medical facilities in Poland use data analytics? If so, in what form and in what areas? (Table 8 ). The analysis of answers given by the respondents about the potential of data analytics in medical facilities shows that a similar number of medical facilities use data analytics in administration and business (31.72% agreed with the statement no. 5 and 12.33% strongly agreed) as in the clinical area (33.04% agreed with the statement no. 6 and 12.33% strongly agreed). When considering decision-making issues, 35.24% agree with the statement "the organization uses data and analytical systems to support business decisions” and 8.37% of respondents strongly agree. Almost 40.09% agree with the statement that “the organization uses data and analytical systems to support clinical decisions (in the field of diagnostics and therapy)” and 15.42% of respondents strongly agree. Exanimated medical facilities use in their activity analytics based both on historical data (33.48% agree with statement 7 and 12.78% strongly agree) and predictive analytics (33.04% agrees with the statement number 8 and 15.86% strongly agree). Detailed results are presented in Table 8 .
Medical facilities focus on development in the field of data processing, as they confirm that they conduct analytical planning processes systematically and analyze new opportunities for strategic use of analytics in business and clinical activities (38.33% rather agree and 10.57% strongly agree with this statement). The situation is different with real-time data analysis, here, the situation is not so optimistic. Only 28.19% rather agree and 14.10% strongly agree with the statement that real-time analyses are performed to support an organization’s activities.
When considering whether a facility’s performance in the clinical area depends on the form of ownership, it can be concluded that taking the average and the Mann–Whitney U test depends. A higher degree of use of analyses in the clinical area can be observed in public institutions.
Whether a medical facility performs a descriptive or predictive analysis do not depend on the form of ownership (p > 0.05). It can be concluded that when analyzing the mean and median, they are higher in public facilities, than in private ones. What is more, the Mann–Whitney U test shows that these variables are dependent from each other (p < 0.05) (Table 9 ).
When considering whether a facility’s performance in the clinical area depends on its size, it can be concluded that taking the Kendall’s Tau (τ) it depends (p < 0.001; τ = 0.22), and the correlation is weak but statistically important. This means that the use of data and analytical systems to support clinical decisions (in the field of diagnostics and therapy) increases with the increase of size of the medical facility. A similar relationship, but even less powerful, can be found in the use of descriptive and predictive analyses (Table 10 ).
Considering the results of research in the area of analytical maturity of medical facilities, 8.81% of medical facilities stated that they are at the first level of maturity, i.e. an organization has developed analytical skills and does not perform analyses. As much as 13.66% of medical facilities confirmed that they have poor analytical skills, while 38.33% of the medical facility has located itself at level 3, meaning that “there is a lot to do in analytics”. On the other hand, 28.19% believe that analytical capabilities are well developed and 6.61% stated that analytics are at the highest level and the analytical capabilities are very well developed. Detailed data is presented in Table 11 . Average amounts to 3.11 and Median to 3.
The results of the research have enabled the formulation of following conclusions. Medical facilities in Poland are working on both structured and unstructured data. This data comes from databases, transactions, unstructured content of emails and documents, devices and sensors. However, the use of data from social media is smaller. In their activity, they reach for analytics in the administrative and business, as well as in the clinical area. Also, the decisions made are largely data-driven.
In summary, analysis of the literature that the benefits that medical facilities can get using Big Data Analytics in their activities relate primarily to patients, physicians and medical facilities. It can be confirmed that: patients will be better informed, will receive treatments that will work for them, will have prescribed medications that work for them and not be given unnecessary medications [ 78 ]. Physician roles will likely change to more of a consultant than decision maker. They will advise, warn, and help individual patients and have more time to form positive and lasting relationships with their patients in order to help people. Medical facilities will see changes as well, for example in fewer unnecessary hospitalizations, resulting initially in less revenue, but after the market adjusts, also the accomplishment [ 78 ]. The use of Big Data Analytics can literally revolutionize the way healthcare is practiced for better health and disease reduction.
The analysis of the latest data reveals that data analytics increase the accuracy of diagnoses. Physicians can use predictive algorithms to help them make more accurate diagnoses [ 45 ]. Moreover, it could be helpful in preventive medicine and public health because with early intervention, many diseases can be prevented or ameliorated [ 29 ]. Predictive analytics also allows to identify risk factors for a given patient, and with this knowledge patients will be able to change their lives what, in turn, may contribute to the fact that population disease patterns may dramatically change, resulting in savings in medical costs. Moreover, personalized medicine is the best solution for an individual patient seeking treatment. It can help doctors decide the exact treatments for those individuals. Better diagnoses and more targeted treatments will naturally lead to increases in good outcomes and fewer resources used, including doctors’ time.
The quantitative analysis of the research carried out and presented in this article made it possible to determine whether medical facilities in Poland use Big Data Analytics and if so, in which areas. Thanks to the results obtained it was possible to formulate the following conclusions. Medical facilities are working on both structured and unstructured data, which comes from databases, transactions, unstructured content of emails and documents, devices and sensors. According to analytics, they reach for analytics in the administrative and business, as well as in the clinical area. It clearly showed that the decisions made are largely data-driven. The results of the study confirm what has been analyzed in the literature. Medical facilities are moving towards data-based healthcare and its benefits.
In conclusion, Big Data Analytics has the potential for positive impact and global implications in healthcare. Future research on the use of Big Data in medical facilities will concern the definition of strategies adopted by medical facilities to promote and implement such solutions, as well as the benefits they gain from the use of Big Data analysis and how the perspectives in this area are seen.
Practical implications
This work sought to narrow the gap that exists in analyzing the possibility of using Big Data Analytics in healthcare. Showing how medical facilities in Poland are doing in this respect is an element that is part of global research carried out in this area, including [ 29 , 32 , 60 ].
Limitations and future directions
The research described in this article does not fully exhaust the questions related to the use of Big Data Analytics in Polish healthcare facilities. Only some of the dimensions characterizing the use of data by medical facilities in Poland have been examined. In order to get the full picture, it would be necessary to examine the results of using structured and unstructured data analytics in healthcare. Future research may examine the benefits that medical institutions achieve as a result of the analysis of structured and unstructured data in the clinical and management areas and what limitations they encounter in these areas. For this purpose, it is planned to conduct in-depth interviews with chosen medical facilities in Poland. These facilities could give additional data for empirical analyses based more on their suggestions. Further research should also include medical institutions from beyond the borders of Poland, enabling international comparative analyses.
Future research in the healthcare field has virtually endless possibilities. These regard the use of Big Data Analytics to diagnose specific conditions [ 47 , 66 , 69 , 76 ], propose an approach that can be used in other healthcare applications and create mechanisms to identify “patients like me” [ 75 , 80 ]. Big Data Analytics could also be used for studies related to the spread of pandemics, the efficacy of covid treatment [ 18 , 79 ], or psychology and psychiatry studies, e.g. emotion recognition [ 35 ].
Availability of data and materials
The datasets for this study are available on request to the corresponding author.
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This research was fully funded as statutory activity—subsidy of Ministry of Science and Higher Education granted for Technical University of Czestochowa on maintaining research potential in 2018. Research Number: BS/PB–622/3020/2014/P. Publication fee for the paper was financed by the University of Economics in Katowice.
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PhD Thesis on "Big Data in Official Statistics"
![thesis in big data Profile image of Carlo Vaccari](https://0.academia-photos.com/563289/205888/240327/s65_carlo.vaccari.jpg)
The explosion in the amount of data, called “data deluge”, is forcing to redefine many scientific and technological fields, with the affirmation in any environment of Big Data as a potential source of data. Official statistics institutions a few years ago started to open up to external data sources such as administrative data. The advent of Big Data is introducing important innovations: the availability of additional external data sources, dimensions previously unknown and questionable consistency, poses new challenges to the institutions of official statistics, imposing a general rethinking that involves tools, software, methodologies and organizations. The relative newness of the field of study on Big Data requires first of all an introduction phase, for addressing the problems of definition and for defining the areas of technology involved and the possible fields of application. The challenges that the use of Big Data poses to institutions that deal with official statistics are then presented in detail, after a brief discussion of the relationship between the new "data science" and statistics. Although at an early stage, there is already a number, limited but growing practical experience in the use of Big Data as a data source for use in statistics by public (and private) institutions. The review of these experiences can serve as a stimulus to address in a more conscious and organized way the challenges that the use of this data source requires all producers of official statistics. The worldwide spread of data sources (web, e-commerce, sensors) has also prompted the statistical community to take joint action to tackle the complex set of methodological, technical and legal problems. And so many national statistical institutes along with the most prestigious international organizations have initiated joint projects that will develop in the coming years to address the complex issues raised by Big Data for statistical methodology and computer technology.
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Intersections
Johanna Giczi
More than five years ago Eurostat started a project with the aim to 'tame' sources of Big Data in a way that they can be incoporated into official statistical systems. In order to solve the problems a statistician might be faced with during the official statistical application of Big Data, first of all, we give an overview of traditional data collection, and then point to the differences one has to face when dealing with Big Data. We introduce common sources of data (traditional, administrative) and highlight the ways huge sets of data are different compared to them. Next, we discuss characteristics of Big Data versus traditional statistical methods based on the qualitative criteria of official statistics, and we also elaborate on the problems of analysing Big Data. Finally, we provide a list of use cases for Big Data in official statistical data collections.
![thesis in big data thesis in big data](https://a.academia-assets.com/images/loswp/related-pdf-icon.png)
Carlo Vaccari
Gregory Farmakis
The aim of this paper is to assess the feasibility of employing novel methodologies for producing high quality Official Statistics based on Big Data. Big Data can be described as high volume, high velocity and high variety of information that require new methods of processing. Big Data is such a rich source of data that the Official Statistics community cannot ignore. Official statistics are statistics published by government agencies or other public bodies that aim to response to policy needs. They are based almost exclusively on survey data collections and administrative data. On the other hand, Big Data challenge the way we think about data assets, the sources we collect them from and the way we analyze them. This illustrates that paradigms are shifting, since a reverse approach of designing statistics is being applied. With Big Data, it is fundamental to explore the vast amounts of data available first and then to decide on the quantities to be measured. Thus, inference techniques used for Official Statistics will need to make a shift too. In this paper, we explore the potential of using Big Data sources as input for supplemeting or replacing Official Statistics is examined. The work is carried out under the framework of a European Commission / Eurostat project. Five sources are examined: (a) the Automatic Identification System (AIS) records for replacing Official Maritime Transport Statistics on vessel movement, (b) a classifieds site concerning house sales and rental prices in order to produce Housing Price Official Statistics, (c) the Facebook and Twitter content to produce well-being quality of life measures, complementary to Official Statistics, (d) Credit card transaction data for supplementing consumption expenditure statistics and (e) the ‘‘δι@ύγεια’’ site publishing government expenditure decisions for national accounting purposes. The potential of using the selected Big Data sources as input of Official Statistics is investigated in terms of the quality of the statistics they produce. The quality of the data is disscussed according to the dimensions defined by the European Statistical System, namely: a) relevance, b) accuracy, c) timeliness and puctuality, d) accessibility and clarity and e) coherence. The cost involved in the production prossess is also discussed. The evaluation of the effect of methodological restrictions on the overall quality of the statistics produced reveals several potential benefits as well as disadvantages. Among all, Big Data are characterized by the amount of information and the frequency at which they are produced. The high geographical coverage and the accuracy of the data, are some of the pros. On the other hand, a major issue concerning Big Data is the accessibility due to confidentiality or other policies. In addition, the volume of the data increases further the processing needs for validation. It is highlighted, that the quantities produced can be used supplementary to the respective Official Statistics’ indicators, while at most of the cases, it is not feasible to replace them. However, exploiting the vast ammounts of data available in a methodologically sound way may enhance the fast production of low cost and high quality official statistics. That is, Big Data is a field that requires new tailored methods to accelerate the analysis of large amount of data.
Big Data is an extremely interesting data source for statistics. Since more and more data is generated in our modern world and is digitally stored, it could certainly be used to replace traditional sources or provide additional information for official statistics. Especially given declines in survey response rates, information gathered from Big Data is an interesting addition. However, extracting statistically-relevant information from Big Data sources is not an easy task. In this paper the current state of the art of research on the use of Big Data for official statistics at Statistics Netherlands is described. The paper is based on the real world experiences of the authors obtained during these studies. The topics discussed are related to Big Data methodology, privacy and security concerns and the skills required for successfully employing Big Data. Introduction Big Data is a term that one hears more and more often at conferences, meetings and seminars. Since its first introductio...
Rob Kitchin
The development of big data is set to be a significant disruptive innovation in the production of official statistics offering a range of opportunities, challenges and risks to the work of National Statistical Institutions (NSIs). This paper provides a synoptic overview of these issues in detail, mapping out the various pros and cons of big data for producing official statistics, examining the work to date by NSIs in formulating a strategic and operational response to big data, and plotting some suggestions with respect to ongoing change management needed to address the use of big data for official statistics.
Journal of Official Statistics
More and more data are being produced by an increasing number of electronic devices physically surrounding us and on the internet. The large amount of data and the high frequency at which they are produced have resulted in the introduction of the term ‘Big Data’. Because these data reflect many different aspects of our daily lives and because of their abundance and availability, Big Data sources are very interesting from an official statistics point of view. This article discusses the exploration of both opportunities and challenges for official statistics associated with the application of Big Data. Experiences gained with analyses of large amounts of Dutch traffic loop detection records and Dutch social media messages are described to illustrate the topics characteristic of the statistical analysis and use of Big Data.
More and more data are being produced by an increasing number of electronic devices physically surrounding us and on the internet. The large amount of data and the high frequency at which they are produced have resulted in the introduction of the term ‘Big Data’. Because of the fact that these data reflect many different aspects of our daily lives and because of their abundance and availability, Big Data sources are very interesting from an official statistics point of view. However, first experiences obtained with analyses of large amounts of Dutch traffic loop detection records, call detail records of mobile phones and Dutch social media messages reveal that a number of challenges need to be addressed to enable the application of these data sources for official statistics. These and the lessons learned during these initial studies will be addressed and illustrated by examples. More specifically, the following topics are discussed: the three general types of Big Data discerned, the...
Giulio Barcaroli
1. Big data are becoming more and more important as additional data sources for Official Statistics (OS). Istat set up three projects aimed to experiment three different roles of Big data sources, namely: (i) “new” sources enabling access to data not yet collected by Official Statistics (ii) “additional” sources to be used in conjunction with traditional data sources and (iii) “alternative” sources fully or partially replacing traditional ones.
Serena Signorelli
This paper tries to give a short overview on the use of Big Data for statistical purposes, on the problems that arise with this kind of data, especially about quality, shows some applications that havee been done using Big Data in combination with traditional survey data. Finally, a small-scale case study is presented by critically highlighting problems and solutions arising the transition form Big Data to information.
It is widely recognised that important methodological and quality issues are associated with Big Data. Especially selectivity issues become prominent when trying to apply established statistical methods. Using sampling theory as the framework for the production of estimates based on Big Data may not be effective, especially if the data cannot be linked to a known population of units. The question arises to what extent an approach originally developed for survey based statistics can be applied to Big Data. The paper discusses possible quality approaches to the production of official statistics when dealing with Big Data, for instance the compilation of statistics not aimed at predefined populations. These approaches could result in rapid information with high relevance from a user’s perspective. However, the introduction of such approaches requires an assessment of the role that National Statistical Institutes aspire to play in the era of Big Data.
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Writing a thesis is the final step in obtaining a Bachelor or Master degree. A thesis is always coupled to a scientific project in some field of expertise. Candidates who want to write their thesis in the Big Data Analytics group should, therefore, be interested and trained in a field related to our research areas .
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For students interested in a thesis, we offer interesting topics and a close, continuous supervision during the entire thesis time. Every thesis is supervised by at least one member of our team, who can give advice and help in critical situations. The condensed results of our best master theses have been published at top scientifc venues, such as VLDB, CIKM, EDBT, etc.
A selection of open thesis topics can be found on this page. We also encourage interested students to suggest own ideas in the context of our research areas and to contact individual members of the group directly. An ideal thesis topic is connected in some form to the research projects of a group member. That group member will then become a supervisor for the thesis. Hence, taking a look at the personal pages and our current projects is a good starter for a thesis project. Recent publications on conferences, such as VLDB or SIGMOD , or open research challenges on, for example, Kaggle are good resources for finding interesting thesis ideas.
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- We aim to translate the batch processing-based Sindy algorithms for the discovery of inclusion dependencies with Akka into a reactive, more efficient data profiling approach.
- We aim to translate the Many algorithm for inclusion dependency discovery on Web Tables into a partializing IND discovery algorithm that is better suited for data integration scenarios.
- The data profiling language DPQL is a recently developed metadata profiling interface that serves the discovery of complex metadata patterns.
- We aim to develop efficient profiling approaches that find these metadata patterns as fast as possible.
- IoT applications, multi-sensor systems and many distributed software systems record time series in different frequencies, temporal alignments, speeds, and formats, which makes their integrated analysis a technically and algorithmically challenging task. We therefore aim to develop a time series engineering library that assists the integration and preparation of time series for analytical tasks, such as anomaly detection, forecasting, clustering etc.
- As part of the project, we could generate and measure our own times series with different sensors and aggregate the measurements afterwards with the time series library into a single multivariate time series.
- Based on the movement events of agents in cities, we aim to plan the placement of info-stations, such that these stations inform as many nearby agents as possible in some fixed time period.
- The project will be conducted in collaboration with the emergenCity project.
- We will use the streams of movement data and the Lambda engine that is currently in development at the UMR.
- Keywords: Lambda queries, lattice search
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- The project will be conducted in collaboration with the VirtualDoc project.
- Keywords: time series analytics, machine learning
- In this project, we aim to slice time series into semantically meaningful subsequences. In contrast to traditional sliding or hopping windows, semantic windows should capture variable-lengths concepts, such as hearth beats in ECG data. These subsequences will then support anomaly detection algorithms or clustering algorithms in creating better results.
- Discovering anomalies in streaming data is a challenging task; hence, we aim to translate batch anomaly detection algorithm(s) into the streaming scenario.
- Our goal is to discovery anomalies as fast as possibly by sacrificing as little precision as possible.
- Keywords: stream processing
- In film scoring, certain visual scenes are accompanied by appropriate sounds; we plan to automate this process with artificial intelligence.
- Given a database with already scored films, we first extract the scene-to-sound mappings and, then, train a model to learn the scoring process.
- The project will be conducted in collaboration with a professional film scorer.
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- First-Line schema matching produces similarity matrices which indicate how likely two attributes of different schemata represent the same semantic concept.
- Second-Line schema matching consumes similarity matrices and aims to produce improved similarity matrices.
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- We aim to improve the Hungarian Method by improving its efficiency in exchange for a bit of fuzzyness/approximation (= reduced correctness)
- Also interesting: Can we allow (to some extend) 1:n and n:m mappings in the attribute matching?
- Knowledgebases are a valuable source of publicly available data and data integration scenarios. To make these scenarios usable also for relational data integration systems, this project aims to develop a shredding algorithms that translates linked open data into meaningful relational tables for data integration purposes.
- Data integration test scenarios are very rare, especially if these scenarios should offer special properties, such as join- and unionable tables, unary and complex attributes matches, a broad selection of data types, schema-based and schema-less data, real-world data values and many other properties. This project, therefore, aims to develop a relation decomposer that takes existing, integrated datasets as input and automatically generates different integration scenarios with specific properties from these seed datasets via relational decomposition.
- The Web Data Commons Crawl is a large dataset of relational tables that stem from crawled HTML Web tables. These tables often store data about same/similar concepts, but they are due to their crawling completely unconnected. Hence, we aim to integrate the WDC commons corpus in a possibly meaningful and correct way, which is both a technically and conceptually challenging task.
- Data in data lakes is subject to constant changes. Data lakes, thereby, lack most of the control mechanisms that traditional database systems would use to, for example, standardise schemata, maintain indexes or enforce constraints. In this project, we aim to develop a system named lakehouse that dynamically integrates certain parts of a Data Lake to serve certain user-defined queries.
- The National Research Data Infrastructure (NFDI) deploys a meta-search engine for research data . We aim to improve their results by integrating multi-modal and heterogeneous research data.
- The federated learning technique DataGossip proposes to exchange not only model weights, but also some training data items for better convergence on skewed data distributions; we aim to improve this technique with more intelligent training data selection techniques.
- Keywords: federated learning, distributed computing
- The BYTE Challenge is a digital learning platform for computer science that targets children from grade 3 to 13.
- In this project, we aim to assist the platform development and the assessment and curation of digital learning material, which includes videos, quizzes, papers etc.
- Efficient Partial Inclusion Dependency Discovery
- Entwicklung einer Chat-KI für Data Engineering
- Image2Surface: Predicting Surface Properties of Workpieces from Laserscan Images
- Image2Surface: Data Engineering for Visual Analytics
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- Efficient Distributed Discovery of Bidirectional Order Dependencies (2020)
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- Multivalued Dependency Detection (2016)
- DataRefinery - Scalable Offer Processing with Apache Spark (2016)
- Spinning a Web of Tables through Inclusion Dependencies (2014)
- Discovery of Conditional Unique Column Combination (2014)
- Discovering Matching Dependencies (2013)
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BIG DATA MASTER THESIS
What is a master’s thesis? The master thesis is an innovative scholarship with the assistance and guidelines of the research professors and advisors. The master thesis and doctoral dissertation are more or less similar with a small difference such as the master thesis is shorter than the dissertation and is narrowly concentrated on the topic. We have well-experienced research experts in the field of big data . Using their experience below we have stated the notable big data master thesis topics as the reference for the research scholars.
![thesis in big data Guidance for Big Data Master Thesis](https://phdprime.com/wp-content/uploads/2022/10/Big-Data-Writing-Master-Tips.jpg)
Latest Thesis and Research Topics in Big Data
- Predictive analysis
- Big data analysis
- Big data maturity model
- Internet of things
- Big data virtualization
- SQL on Hadoop
- It is an interesting topic for master thesis and further research in big data using recent and classical research
- It is considered the real-time application result for business intelligence and big data
- It has applications in the areas such as customer service, finance, etc.
- It explores the fundamental details of the customers
- It is used to discover huge volume data sets to recognize the hidden patterns
- It deploys in the process of regulation and organizes the capabilities of big data
- It is beneficial in measuring the big data maturity
- The data partition has two types such as soft and hard
- There are different algorithms in data mining and big data
- It is used to divide the data into various sets using some techniques
- IoT devices are used to extract the connectivity among the devices and it is sued to sense the data
- IoT and big data work with the concurrence
- The tools in virtualization are used to regulate the process of data analytics
- The process produces some structures in the big data system
- It is used for the implementation process of the Hadoop platform with the accumulation of SQL-style querying systems
- It divides the work among Mapreduce such as the raw form of HDFS clusters and normal HDFS clusters
The above-mentioned are the notable trends in the contemporary big data period. The research scholars should select their big data master thesis topics from the latest research trends. For more research references in big data, the research scholars can reach us. Now, it’s time to discuss the several functions of the master thesis . Let us start with the importance of a master thesis.
Is a master thesis important?
The thesis is considered as the most important work because it is the innovative expertise of the researchers so it is particularly important. The master thesis is a chance for the researchers to show what the ability of the researcher is. The specialization of a master’s degree is to write the thesis and do projects in the degree stage . In the following, our research experts have explained the originality of the master thesis.
Does a master’s thesis have to be original?
The research students select their thesis topic on their own because that is directly relevant to their degree. The research topic from the students is the only essential part of the thesis and it is parallel to the common research papers. It is not essential to add any raw data on the topic if the process doesn’t take place.
The aforementioned is about the significant features of the master thesis which is useful for the research scholars to develop their research in big data master thesis topics . In the following, our research experts have described the details of the specifications of the topic in the thesis.
Finding topic
- The research questions arise from various discussions and seminars. So, the researchers should collect their ideas to create an innovative research
- Researchers should focus on novel research ideas to provide the finest research topic and recreate the past research with various technologies
- Research students can offer innovative thesis topics for the research
Can you get a PhD without a master’s thesis?
The master’s research program may have additional requirements for the researchers who are incomplete in their thesis work . The students who have completed their thesis are acceptable with their capstone project.
Can you fail your master thesis?
If the student fails a master’s thesis means it doesn’t mean that the student didn’t write the thesis. The failure takes place due to the student’s efforts in the thesis. The students can request the research supervisors verify the research thesis . This might help students that why they failed in their thesis.
How many references should a master’s thesis have?
In general, the master thesis should showcase a minimum of one secondary resource for every thousand words . This may vary in the doctoral thesis that every 150 words should have a secondary source.
The research scholars can choose our research experts to start their research projects in big data. Additionally, our research experts offer the whole guidance for the big data master thesis topics and the guidance which starts from topic selection to the research code implementation process. For your reference, we have listed down the guideline for writing a thesis.
Tips to write a thesis
- The journal articles used to set the destination
- It helps to spot the research relevant to the topic
- Gaze the confirmation in the paper
- The article pages up to 25 and in some cases, it ends in 15 pages
- Set the deadline and allocate to work reach the aim
- The assignment writing will carry over until the researcher ends it
- Select the time and work according to that time allocation
As a result of this page, we believe that you have received some knowledge about the big data master thesis topics . Our research experts assist with your big data Dissertation such as the significant uses of methods, modifications in the protocols, appropriate research implementation, etc. So, the research scholar can reach us to reach better heights in research.
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BIG DATA MASTER THESIS
“Big data” can be generally defined as large arrival of data volume, variety, and velocities of data resources which enable cost-effective, creative data analysis for improved insight as well as visualization. In practice, big data size is changing gradually. It must permit the diverse types of inputs to be completely assimilated and evaluated to assist us in drawing conclusions. This article provides you deep insight into the Big Data master thesis where you can get to cover all aspects needed to do big data research and thesis work effectively. Let us first start with understanding the various processes in big data,
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Big Data Processes
- Data acquisition
- Recording of information
- Annotations
- Data representation
- Feature extraction
- Data analytics
- Interpretation
What are big data analysis techniques?
- Identifying data entities and redundancies
- Processing the missing and abnormal values
- Processing the skewness
- Standardization and discretization of data
- Constructing attributes
More particular applications of Big Data in real life can be found on our website from which you can better understand these processes. Big data master thesis is the most sought-after research assistance service in the world, with students and researchers from renowned universities rushing to us. We are able to deliver the most trustworthy and comprehensive research help in Big Data thanks to our updated technical team of professionals. Let us now discuss the recent research directions of big data
What are the current directions of Big Data analytics?
- Collecting transforming and analyzing big data with the support of data center
- Integrating information from multiple sources along with the distribution of data for the purpose of management and computation.
- Utilising the attributes like machine learning mechanism and data mining for large scale analysis of data
- Tools of data visualization are used along with machine learning techniques
- The concerns of privacy and security in big data are handled efficiently using the statistical theory and big data sampling processes
You can reach out to our experts for any of these areas of big data research . Big data is widely regarded as the most important technological advancement in today’s digital world. Contact us if you’re looking for a vast repository of research-related data drawn from real-time big data platforms. Let us now look into ongoing research areas in big data
Our ongoing activities in big data
- Basic theory study, analysis, and development
- Working with advanced techniques, methods, and algorithms
- Developing advanced techniques based on the latest technologies in order to enhance the efficiency of big data applications and solve many big data issues
- Enabling research scholars and students from all over the world to interact with big data researchers and scientists to integrate new technologies to carve out innovations
- Advanced technologies and methodologies for being developed to solve potential problems in Big Data analytics
Despite the fact that Big Data appears to be a big topic that would require several books and programs to cover, our developers are now focusing on the fundamentals of Big Data so that students understand what else to think about when digging deep into Big Data algorithms and strategies . Let us now look into the major demands of big data
What are the requirements of big data models?
- Novel applications, techniques, and advanced solutions for creating a positive impact in big data research
- New big data model for real-time data analysis and processing with enhanced security features to ensure privacy and secrecy of data.
Here are a few of the world’s top technical specialists who have been working with Big Data projects from their inception. Let us now discuss the significance of the research.
What is the purpose of a research project?
Every research work has its own significance. But each one cannot be implemented in the real world. Here are many assumptions, hypotheses, and establishments that have the capacities to be worked out beyond. Almost all science-based imaginations and fiction are becoming reality. The following are the important aspects in which privacy and security policies have to be given priority
- Agricultural, logistic and financial data
- Sensor, web, and city data
- Integration/fusion of data for decision making
- Mining and visualization of data
- Utilising big data analytics in real-time
For instance,
- Smart cities management
We have experienced qualified and professional engineers and skilled writers who have earned world-class certification to provide you with full support in all of these areas. In Big data master thesis, we utilize a systematic plan to maintain proper shape and consistency in the language of the scholarly work. All of your ideas, points of view, and references will be organized logically. Let’s look at some massive data processing techniques now.
Real-time applications of Big Data analytics
- Data obtained from location and GPS
- Integrated personal information from satellite images
- Call data records
- Enables reliable tracking of location and proper recommendation of routes
- Most useful in routing drones for applications in military, emergency situations and identifying infections
- Location information
- Determining the mobility pattern across the globe for containment of infectious diseases and planning transportation
- Data on location and consumption pattern
- Data from a smart meter, history of usage, and gas status
- Promoting the use of green energy by increasing conservation
- Establishing use efficiency by predicting energy consumption rate
- Record of patients’ data and electronic health record
- Health history data, X-rays, and images
- Enhance the health monitoring purposes and used in studying patient’s immune response
- Recommendation of activity for maintaining physical health and elderly people
- Data on network signal strength and network user information
- Geolocation and sensor data
- Network log activities, video camera data, and weblogs
- It is used in effective network signaling and network dynamics prediction
- Management of networks and cell deployment data generation
- Log and social media data
- Product reviews, tweets, and blog posts
- User service recommendations which are effective and efficient
- Online surveys and questionnaires, ECG, EMG, and pulse rate
- Data sensings like gyroscopes, accelerometer, and magnetometer
- Utilising smartphones and other online network frameworks for collecting and analyzing data on a large scale
- Selection and review of products
- Location and data buying behavioral analysis
- Reviews on customer products and help in analyzing product’s strengths and weaknesses
There are also many more important big data applications in real-time specific to the requirements. Speak with one of our technical specialists about the practices we implemented to improve the effectiveness of our Big Data programs . Because we stick to a zero plagiarism standard, our writers promise that there’ll be no duplication in the final edition of your thesis that we prepare. We guarantee a thorough grammatical verification, internal review, and on-time submission . Let us now discuss the integrated and upgraded big data methods in further detail.
What are the technologies used in Big Data analytics?
- Data retrieval, mining, analytics, and distribution
- Massive parallelism, machine learnin g, and AI
- High-speed networking and high-performance computation
- Hadoop, Spark-based big data analytics technologies
For quantitative, analytical, theoretical, and coding platforms related to all these methodologies, you can approach us for great big data master thesis writing . Our professionals can explain everything about Big Data and answer all of your questions at once. Let’s now get into the different types of Big Data tools
Best Big Data Management Tools
NoSQL provides for non-relational database for the purpose of storing wearing and managing data that is both unstructured and structured. It does not need normalization and application porting integration . Computational overhead is reduced big data distribution across different hosts led by elastic scaling . The following are the important NoSQL-based tools in managing big data storage systems.
- It is a highly reliable system for storing large volumes of data with fault tolerance
- It is used in reading the data once and interpreting it for writing many times by consuming minimal storage
For the pros and cons of these tools, you can get in touch with us at any time. The following are the major tools in managing the big database
- It is one of the important Hadoop tools for enabling machine learning and real-time data processing
- The tool is significantly used in operations of reading and writing, batch processing, joining streams, node failure handling, and many more
- Inbuilt applications in Spark is used in implementing many common programming languages
- Summarising, analysis of queries and data with SQL interface is one of the biggest advantages provided by Apache hive
- It facilitates and helps in maintaining the writing with the use of approaches like indexing
- It provides a data storage facility by and column-based data
- Large datasets storage that located at the top of HDFS
- It provides for aggregating and analyzing datasets with multiple rows in a very less period
- Analysis of large generator datasets is made easier
- It provides increased performance, throughput and the response time is also quick
- It is an RDBMS data import and export tool
- Time for processing data is reduced by providing a mechanism for computational offloading
Once you reach out to us, we will provide you with a huge amount of standard and very precise research data regarding the use of these tools. Let us now look into some of the important tools that are used in big data processing mechanisms,
- For data extraction from and to Hadoop, the flume is used
- HDFS data streaming by easy to use and flexible framework leading to efficient aggregation
- It is an important tool used in handling streaming functions and batches
- It is a highly efficient real-time analysis tool used in Hadoop based distributed stream processing
- By using distributed snapshots this tool provides increased performance in data operation by enabling fault tolerance
- It also provides an integrated runtime environment for batch processing and data streaming applications
- Hadoop cluster job parallelization tool that works by enabling coordination and workflow
- Multiple job execution with fault tolerance is allowed by this tool
- It is also used in seamless job control in web service APIs
- It is an important tool used in job management computation and scheduling of resources
- It is a programming framework based on Hadoop used in batch processing
- It can store a huge volume of distributed data in a cost-effective manner and so its scalability is also very high
- It is a tool that provides a proper Framework for processing data which is used in defining the workflow
- It also gives execution steps using a proper acyclic graphical representation
- Its interface is very simple and can be used in very fast data processing applications
- Switching from the MapReduce platform is also enabled by this tool
- It is one of the important large data processing tools used in clustering, classifying, regression, collaborative filtration, segmenting, and statistical modeling applications
- It is useful in complementing applications that involve the use of distributed data mining
- This tool is used in Hadoop based allocation of resources and scheduling jobs
- Hadoop 2.0 mechanism forms the basis of this tool which is used in managing resources and metadata maintenance while at the same time tracking user data
- Efficient resource utilization by adding YARN into Hadoop and higher data availability is provided by this tool
Any Big Data system’s success is largely determined by its tools and algorithm. Algorithms are used to regulate, find, and build the cognitive models of a Big Data system . One of the most significant functions of Big Data algorithms is to extract valuable information and analyze them for arriving at results . As a reason, in order to write the best code and programming for your big data projects , you’ll need to expand your skills in all major programming languages. Let’s have a look at some of the most essential big data programming languages in this area.
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Top 3 programming languages for Big Data analytics
- It is a general-purpose programming language that consists of a large number of open-source packages used for the following purposes
- Data modeling, pre-processing, mining, and computation
- Machine learning, analysis of network graphs, and processing natural languages
- It is a highly user-friendly and object-oriented programming language that is well known for its flexible and supportive aspects that allows it to integrate with various other platforms for big data processing like Apache spark
- It is one of the common open-source programming languages used in data analysis and visualization
- It is also highly significant in handling complicated data as it provides for efficient storage systems and performing vector operations
- It is useful in performing all the following popular data related functions in a more efficient manner
- Reading and writing data into the memory
- Data cleansing, storage, visualization, mining, and machine learning
- It is one of the important tools in carrying out big data analytics and processing
- Apache Spark provides for complicated app development platform using multiple programming languages with Java enabled virtual machine-based data processing
- It is used in scala supported big data processing, analysis, and management
- It enables simple, quick, inherently immutable applications which reduces highly threaded security in the same kind of languages
You can surely get full support on all these tools and programming languages from us . Our professionals usually offer utmost priority to all of the vital parts of these Big Data research fields so that consumers can pleasantly execute their exploration . Our writers are likewise extremely organized about following your institution’s formatting rules and norms. You can therefore experience our services more confidently.
We are helping individuals to carve out customized big data systems for their enhancement. We have got qualified teams of research experts, writers, developers, engineers, and technical teams to assist you in all aspects of your big data master thesis. We will look into the important stages in master thesis writing
Main Stages of writing a master’s thesis
Writing the best thesis is one of the important aspects to showcase your field knowledge, talent, and innovation thus, in turn, attracting a huge volume of readers. In this regard, our expert writers have been providing all the necessary resources and support to our customers in writing one of the best thesis works in any big data master thesis topic . In the following, you can find some important aspects of a master thesis
- Choose one of the most interesting and recent topics
- Try to create a holistic proposal
- Utilise all the relevant resources to carry out the research
- Give utmost importance to proofreading, checking, and formatting
- Have brief talks and detailed discussions with your guide and mentor regarding the content
As a result, you may want the assistance of professionals in the subject in order to begin your Big Data Master Thesis. We have links with experts from the world’s best firms, institutes, and academics; therefore we are well-versed in the technical aspects of contemporary Big Data research. Hence you can have all your research needs to be met in one place. Let us now talk about some important thesis topics in big data,
Top 6 Big Data Master Thesis Topics
- Data retrieval based on queries
- Social network sentiment analysis both offline and online
- Correlated big data analysis for protecting the privacy
- Preserving the privacy and ensuring the security of big data users
- Big spatial data similarity search
- Allocation of resources in Big Data System with elevated security awareness
These are some of the most popular and current study areas in the field of big data. For any type of research support, including PhD proposals, dissertation writing help , paper publishing, assignments, producing literature reviews, and big data master thesis, feel free to contact our developers. We are happy to help you.
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[Research] Master Thesis Topic: Big Data & Business Analytics
Hello there. So, currently I’m studying Big Data & Business Analytics for my Masters Degree. I’m near the last semester and slowly the question of topic for the thesis gets relevant. Unfortunately I’m lacking ideas at the moment (I’m really uncreative sadly). So I thought maybe you guys have some input for me which topics / fields to consider? I’m really open for everything regarding Big Data, Machine Learning or Business Analytics.
Also it would be really interesting for me which topics you researched for your bachelor or master’s degrees. Maybe I’ll find inspiration there.
The phase where the university helps us to find a topic is still ahead but I thought it could be helpful to get some ideas early. I hope I labeled this post correctly and it’s not too beginner-ish for your community. If not, please correct me.
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A Review on Data Quality Dimensions for Big Data
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Bibliometrics & citations, view options, recommendations, a data quality in use model for big data.
Beyond the hype of Big Data, something within business intelligence projects is indeed changing. This is mainly because Big Data is not only about data, but also about a complete conceptual and technological stack including raw and processed data, ...
A Review on Data Cleansing Methods for Big Data
Massive amounts of data are available for the organization which will influence their business decision. Data collected from the various resources are dirty and this will affect the accuracy of prediction result. Data cleansing offers a better ...
Relating Big Data and Data Quality in Financial Service Organizations
Today’s financial service organizations have a data deluge. A number of V’s are often used to characterize big data, whereas traditional data quality is characterized by a number of dimensions. Our objective is to investigate the complex ...
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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.
MASTER THESIS Big Data and Business Intelligence: a data-driven strategy for e-commerce organizations in the hotel industry Date 03-09-2015 Personal information Author Mike Padberg E-mail [email protected] Study program Master in Business Administration Business Information Management ...
Thesis Title: Investigating the Impact of Big Data Analytics on Supply Chain Operations: Case Studies from the UK Private Sector A thesis submitted for the degree of Doctor of Philosophy By Ruaa Hasan Brunel Business School Brunel University London 2021 . 2 | P a g e
The Evolution of Big Data and Its Business Applications. Doctor of Philosophy (Information Science), May 2018, 113 pp., 12 tables, 10 figures, references, 203 titles. The arrival of the Big Data era has become a major topic of discussion in many sectors because of the premises of big data utilizations and its impact on decisionmaking. It is an -
This thesis proposes eight big data quality parameters in total. Six out of eight of those models were coded and made into a final year project by a group of Master's degree students at SJSU. A case study is carried out using linear regression analysis, and all the big data quality parameters are validated with positive results.
in Janssen et al. 2017; Mallinger & Stefl, 2015). There is an existing gap in the literature for studying big data decision-making. as a process and especially on a strategic level. This section combines theories from both traditional decision-making and decision-making in big data studies, as well as the gene.
The "Business Analytics and Big Data" track as a melting pot for topics in information systems (IS) and neighbouring disciplines has a long and successful history at the European Conference on Information Systems (ECIS). From its initial year in 2012 to 2021, the track has received 512 submissions.
PDF | On Jan 1, 2021, Nadeem U. Shahid and others published Impact of Big Data on Innovation, Competitive Advantage, Productivity, and Decision Making: Literature Review | Find, read and cite all ...
High dimension:"Big data" is not just about size. The second feature means that the data have many variables relative to the sample size. Machine learning, which is often thought of as a hallmark of big data research, is a common solution to the dimension challenge, and it is increasingly used in finance research.
In this thesis, big data is dened as a framework, in which its ethical implications, possibilities, and challenges are identied and examined. Additionally, the elements needed to successfully extract valuable insights from big data are mapped. The theoretical research focuses on big data as a framework and it examines the foreseen
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.
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 ...
A widely used definition of big data analytics regards them as " a new generation of technologies and architectures, designed to economically extract value from very large volumes of a wide variety of data, by enabling high velocity capture, discovery and / or analysis " ( Mikalef, Pappas, Krogstie, & Giannakos, 2017 ).
The introduction of Big Data Analytics (BDA) in healthcare will allow to use new technologies both in treatment of patients and health management. The paper aims at analyzing the possibilities of using Big Data Analytics in healthcare. The research is based on a critical analysis of the literature, as well as the presentation of selected results of direct research on the use of Big Data ...
2014. The explosion in the amount of data, called "data deluge", is forcing to redefine many scientific and technological fields, with the affirmation in any environment of Big Data as a potential source of data. Official statistics institutions a few years ago started to open up to external data sources such as administrative data.
Writing a thesis is the final step in obtaining a Bachelor or Master degree. A thesis is always coupled to a scientific project in some field of expertise. ... Big Data Analytics Hans-Meerwein-Straße 6 D-35043 Marburg +49 6421 28-22185 +49 6421 28-28902 [email protected] social media contact information. Facebook; X (ehemals ...
Predictive analysis. It is an interesting topic for master thesis and further research in big data using recent and classical research. It is considered the real-time application result for business intelligence and big data. Big data analysis. It has applications in the areas such as customer service, finance, etc.
Big data master thesis is the most sought-after research assistance service in the world, with students and researchers from renowned universities rushing to us. We are able to deliver the most trustworthy and comprehensive research help in Big Data thanks to our updated technical team of professionals. Let us now discuss the recent research ...
successfully. Adopting big data analytics implementation strategies may benefit leaders to successfully achieve business goals to improve organizational performance while reducing operating costs. Background of the Problem . Implementing big data analytics has the potential to improve a company's competitive advantage, growth, and performance.
Title of thesis. Big Data in Finnish Companies - What has been done and the results achieved. Degree. Master of Science in Economics and Business Administration. Degree programme. Information andService Economy. Thesis advisor(s) Merja Halme, Anu Bask. Year of approval. 2015. Number of pages. 69. Language. English. Abstract
[Research] Master Thesis Topic: Big Data & Business Analytics Hello there. So, currently I'm studying Big Data & Business Analytics for my Masters Degree. I'm near the last semester and slowly the question of topic for the thesis gets relevant. Unfortunately I'm lacking ideas at the moment (I'm really uncreative sadly).
Big Data wave has led to a rapid increase in the amount of data being collected by organizations. While the accuracy and reliability of prediction models are often prioritized, the quality of the collected data is frequently overlooked. Poor data quality can result in the common problem of 'garbage in, garbage out'.