Big Data Analytics Research Topics are shared here which is the fast-emerging domain in recent years. We provide scholars with  original topics and ideas. Obtain tailored services from us for all your research needs; we deliver high-quality work with rapid publication. Together with an extensive problem description, we provide numerous research topics in big data analytics:

1. Enhancing Real-Time Data Processing in Big Data Analytics

Problem Description:

Because of the extreme volume, velocity, and diversity of data streams, actual time processing continues to be a major problem, in spite of the developments in big data analytics. Generally, delays in decision-making and decreased performance of time-sensitive applications like stock trading, fraud identification, and healthcare tracking are resulting as recent frameworks confront issues in processing and exploring data in actual time.

Research Aim:

• For actual time data processing, we plan to construct scalable models.
• It is approachable to combine stream processing mechanisms such as Apache Flink and Apache Kafka along with big data environments.
• Specifically, for low-latency data processing and analysis, our team focuses on improving methods.

2. Improving Data Quality in Big Data Environments

Problem Description:

Typically, we imprecise analytics and decision-making are produced as big data platforms confront issues from imperfect data quality as a result of velocity, volume, and diversity of data resources. Because of the problems such as missing values, noise, data replication, and discrepancies, that are popular in huge datasets, the process of assuring high data quality is considered as difficult.

Research Aim:

• The efficient data cleaning and validation systems should be constructed.
• For data quality evaluation and enhancement, we aim to apply suitable approaches.
• Mainly, for actual time data quality tracking and improvement, our team investigates automated tools.

3. Privacy-Preserving Big Data Analytics

Problem Description:

The way of assuring big data analytics becomes more complicated, as the amount of confidential and individual data rises. Possible violations and non-adherence to rules such as CCPA and GDPR are produced as conventional data confidentiality criterions are insufficient for the scale and complication of big data platforms.

Research Aim:

• We intend to create confidentiality-preserving data analytics models.
• Data anonymization and encryption approaches must be investigated.
• In big data platforms, our team plans to assure adherence to data protection rules.

4. Scalability Challenges in Big Data Processing

Problem Description:

Because of the significant improvement of data volumes, scalability is examined as a significant issue in big data analytics. While adapting to process greater datasets, recent big data environments confront issues based on ineffectiveness and performance blockages, which results in decreased system receptiveness and enhanced expenses.

Research Aim:

• For distributed data processing, we focus on exploring scalable methods.
• Efficient approaches have to be created for dynamic resource allocation and load balancing.
• Typically, for scalability, our team intends to improve data storage and recovery technologies.

5. Integrating Big Data Analytics with Cloud Computing

Problem Description:

On the basis of adjustability and adaptability, major benefits are provided while combining big data analytics with cloud computing. As a means to entirely utilize the advantages of cloud-related big data analytics, limitations like cost management, data transfer delay, and protection must be solved.

Research Aim:

• For cloud platforms, we plan to construct effective data transfer and storage approaches.
• Generally, for big data processing, our team focuses on investigating cost-efficient cloud computing frameworks.
• In cloud-related big data analytics, it is appreciable to assure data protection and adherence.

6. Real-Time Fraud Detection Using Big Data Analytics

Problem Description:

Mainly, in the financial domain, the process of identifying and avoiding fraudulence in actual time is considered as a significant issue for associations. To detect fraud behaviors as they arise, conventional fraud detection models are not capable of processing huge amounts of data sufficiently in a faster manner and they are inactive.

Research Aim:

• Through the utilization of big data mechanisms, we aim to construct actual time fraud identification models.
• For anomaly identification in financial transactions, it is appreciable to apply methods of machine learning.
• Specifically, for extensive analysis, our team plans to combine various data resources.

7. Big Data Analytics for Predictive Healthcare

Problem Description:

Because of the amount and complication of healthcare data, the healthcare suppliers confront problems in utilizing big data analytics to enhance care standard and forecast patient results. For creating predictive models which are capable of improving patient care, efficient combination and analysis of various data resources are important.

Research Aim:

• By employing big data analytics, our team constructs predictive models for healthcare results.
• It is advisable to combine actual time monitoring data, electronic health records (EHRs), and genomic data.
• We focus on assuring data confidentiality and adherence to healthcare rules.

8. Optimizing Supply Chain Management with Big Data Analytics

Problem Description:

To decrease expenses and improve effectiveness, supply chain management contains the capability to take massive advantage of big data analytics. Generally, delays and ineffectiveness in decision-making are resulting as the amount and complication of supply chain data make it challenging to combine and examine in an efficient manner.

Research Aim:

• For combining and investigating supply chain data, we intend to create models.
• Predictive models have to be applied for inventory management and demand predictions.
• For dynamic supply chain improvement, our team investigates actual time analytics.

9. Enhancing Cybersecurity with Big Data Analytics

Problem Description:

Conventional protection criterions are inadequate to secure in opposition to cybersecurity attacks, as they are becoming progressively complicated. For improving threat identification and reaction, big data analytics provides possible approaches. But the process of combining and examining huge amounts of protection data in actual time sustains to be a major issue.

Research Aim:

• Big data analytics models should be constructed for cybersecurity threat identification.
• For actual time anomaly identification, we aim to apply techniques of machine learning.
• For widespread threat analysis, our team plans to combine various data resources.

10. Big Data Analytics for Environmental Monitoring

Problem Description:

The exploration of complicated and huge datasets from different resources like satellites, sensors, and climate systems are needed for ecological tracking. For forecasting ecological variations and handling natural sources sustainably, efficient combination and analysis of these data are crucial.

Research Aim:

• For combining and investigating ecological data, we focus on creating big data models.
• It is approachable to apply predictive models for pollution patterns and climate variation.
• For ecological management, our team intends to examine actual time tracking and alerting frameworks.

11. Big Data Analytics for Financial Market Prediction

Problem Description:

Because of the instability and complication of financial data, the process of forecasting financial market patterns through the utilization of big data analytics is difficult. For precise market forecasts and investment policies, the way of combining and examining various data resources like social media, market transactions, and news are significant.

Research Aim:

• Through the utilization of big data, we construct predictive models for financial market patterns.
• It is appreciable to combine and explore various financial data resources.
• For dynamic market predictions, our team investigates actual time analytics.

12. Energy Consumption Optimization with Big Data Analytics

Problem Description:

For decreasing expenses and ecological influences, the procedure of improving energy utilization in different domains is examined as important. Integrating and examining energy data in an efficient manner is challenging because of its quantity and complication. To detect and apply energy-saving criterions, endeavours are obstructed.

Research Aim:

• Generally, for combining and examining energy data, we plan to create suitable models.
• Predictive models should be applied for energy utilization and improvement.
• For dynamic energy management, our team focuses on investigating actual time tracking and analysis.

13. Big Data Analytics for Educational Performance Improvement

Problem Description:

The process of combining and examining huge amounts of student data from various resources like social media, learning management systems, and evaluations are encompassed while enhancing educational effectiveness through the utilization of big data analytics. Typically, for improving educational results, the way of constructing predictive models and detecting aspects which impact the effectiveness of the student are considered as significant.

Research Aim:

• In order to combine and examine educational data, our team intends to create suitable systems.
• For student effectiveness and persistence, we apply predictive models.
• Actual time analytics has to be investigated for customized learning and interference policies.

14. Big Data Analytics for Smart Agriculture

Problem Description:

The combination and exploration of huge amounts of data from resources like crop monitoring frameworks, soil sensors, and weather stations are needed for smart agriculture. Major problems are caused due to the complication of agricultural data. It is possible to optimize agricultural production and improve decision-making through the efficient utilization of big data analytics.

Research Aim:

• For combining and investigating agricultural data, we aim to construct big data models.
• It is significant to apply predictive models for resource management and crop production.
• For accurate farming, our team focuses on examining actual time tracking and analytics.

15. Improving Public Health Surveillance with Big Data Analytics

Problem Description:

As a means to track and forecast health crises, public health surveillance could take advantage of big data analytics. For efficient health tracking and reaction, the process of combining and exploring huge amounts of health data from various resources like ecological data, electronic health records, and social media are crucial.

Research Aim:

• To incorporate and examine public health data, it is approachable to create effective models.
• For disease identification and eruption forecast, we intend to apply predictive models.
• Actual time analytics must be investigated for public health tracking and altering frameworks.

How can I find free big data sets for my master dissertation of economics?

The process of identifying freely available big data sets is examined as complicating and intriguing. Appropriate to different regions of economic study like financial markets, macroeconomics, microeconomics, and more, we suggest an instruction based on how and where to detect these datasets:

1. Government and Public Data Portals

World Bank Open Data

• Outline: Encompassing inhabitant statistics, economic indicators, and more, this dataset offers open and free availability to global development data.
• Kinds of Data: Development signals, macroeconomic data, and more.
• Instance of Application: Among various countries, economic growth patterns could be examined.

International Monetary Fund (IMF) Data

• Outline: From across the world, IMF provides a wealth of financial and macroeconomic data encompassing exchange rates, GDP, and inflation.
• Kinds of Data: Financial statistics, macroeconomic statistics.
• Instance of Application: By employing IMF data, we can research the influence of monetary strategies on rising prices.

Eurostat

• Outline: In the European Union, it offers statistical data based on different social, economic, and ecological factors.
• Kinds of Data: Labor market statistics, economic performance, trade.
• Instance of Application: The Eurostat is useful for investigating economic discrepancies with the EU.

U.S. Bureau of Economic Analysis (BEA)

• Outline: Encompassing personal income, GDP, and trade, BEA provides data on the basis of the U.S. economy.
• Kinds of Data: Income data, macroeconomic signs, trade data.
• Instance of Application: On the financial growth, the impacts of trade strategies could be examined.

2. Financial and Market Data

Yahoo Finance

• Outline: Encompassing financial documents, stock prices and indices, it offers data of past and real-time financial records.
• Kinds of Data: Financial statements, stock market data.
• Instance of Application: On stock market instability, this data is beneficial for exploring the influence of economic news.

Quandl

• Outline: A diversity of economic and financial datasets such as economic indicators, market data, and commodities are provided in Quandl.
• Kinds of Data: Economic signs, financial data.
• Instance of Application: By utilizing this data, our team focuses on investigating the connection among economic signs and commodity prices.

St. Louis Federal Reserve (FRED)

• Outline: Economic data is included in an extensive database that is acquired from the Federal Reserve. It could encompass economic indicators, interest rates, and more.
• Kinds of Data: Labor market statistics, economic signs, financial data.
• Instance of Application: On economic behaviour, FRED is beneficial for exploring the impacts of interest rate variations.

3. Academic and Research Data Repositories

Harvard Dataverse

• Outline: Generally, Harvard Dataverse is considered as a free data repository, in which researchers are able to investigate, distribute, reutilize, and cite research data.
• Kinds of Data: Health data, social science data, economic data.
• Instance of Application: Through the utilization of this data, we plan to examine demographic impacts on economic activity.

Kaggle Datasets

• Outline: Datasets relevant to different domains such as social sciences, economics, and finance are included in this environment.
• Kinds of Data: Survey data, financial data, economic indicators.
• Instance of Application: For economic indicators, Kaggle is used for constructing predictive models.

OECD Data

• Outline: From OECD member countries, it offers a broad scope of ecological, economic, and social data.
• Kinds of Data: Employment data, economic indicators, education statistics.
• Instance of Application: We focus on utilizing this data to compare economic strategies and results among OECD countries.

4. Industry and Commerce Data

World Trade Organization (WTO) Data

• Outline: From the World Trade Organization, it provides trade-based data and statistics.
• Kinds of Data: Trade strategies, trade statistics, tariffs.
• Instance of Application: The influence of trade agreements on economic effectiveness could be investigated.

United Nations Comtrade Database

• Outline: From the United Nations, extensive international trade data are offered.
• Kinds of Data: Import and export data, trade data.
• Instance of Application: By employing this data, we examine the trade dynamics among various areas.

5. Survey and Public Opinion Data

Pew Research Center

• Outline: Generally, from surveys carried out globally, it provides a broad scope of data on the basis of economic, social, and political problems.
• Kinds of Data: Social patterns, survey data on public opinion.
• Instance of Application: On customer activity, this data is used to explore the influence of economic insights.

World Values Survey

• Outline: To seize people’s beliefs, principles, and economic activity, the World Values Survey offers data from universal surveys.
• Kinds of Data: Survey data on cultural, social, and economic problems.
• Instance of Application: Through the utilization of this data, our team investigates the connection among economic advancement and cultural values.

6. Open Data and Miscellaneous Sources

Google Dataset Search

• Outline: Over the web, involving the public, educational and government data repositories, it acts as a search engine to detect the preferable datasets.
• Kinds of Data: Diverse on the basis of search queries.
• Instance of Application: This data is employed for identifying datasets that are relevant to certain economic signs or domains.

DataHub

• Outline: For identifying and distributing open datasets, Data Hub is considered a community-based environment.
• Kinds of Data: Social data, economic data, financial data.
• Instance of Application: For comparative economic research, this platform is beneficial for collecting various datasets.

Knoema

• Outline: Involving economics, Knoema offers permission to use a broad scope of global statistical data on different topics.
• Kinds of Data: Industry data, economic data, demographic data.
• Instance of Application: Typically, macroeconomic patterns and their influences on various businesses could be examined.

Big Data Analytics Research Ideas

Big Data Analytics Research Ideas are guided for all level of scholars. Along with  thorough problem description, we provide many research topics in big data analytics, also direction based on how and where to identify free big data sets for master dissertation of economics are suggested by us in an extensive manner. The below mentioned details will be valuable as well as supportive.

1. An analytical study of information extraction from unstructured and multidimensional big data
2. Big data analytics: a link between knowledge management capabilities and superior cyber protection
3. Research on perception bias of implementation benefits of urban intelligent transportation system based on big data
4. Evaluation of high-level query languages based on MapReduce in Big Data
5. Understanding big data themes from scientific biomedical literature through topic modeling
6. Access control technologies for Big Data management systems: literature review and future trends
7. Big data-oriented energy prosumption service in smart community districts: a multi-case study perspective
8. Granular computing with multiple granular layers for brain big data processing
9. Big data analytics in sustainability reports: an analysis based on the perceived credibility of corporate published information
10. HyGraph: a subgraph isomorphism algorithm for efficiently querying big graph databases
11. Visualizing Big Data with augmented and virtual reality: challenges and research agenda
12. Computational storage: an efficient and scalable platform for big data and HPC applications
13. Gapprox: using Gallup approach for approximation in Big Data processing
14. Privacy preserving data publishing based on sensitivity in context of Big Data using Hive
15. An industrial big data pipeline for data-driven analytics maintenance applications in large-scale smart manufacturing facilities
16. A survey of open source tools for machine learning with big data in the Hadoop ecosystem
17. SOCR data dashboard: an integrated big data archive mashing medicare, labor, census and econometric information
18. A new approach to the space–time analysis of big data: application to subway traffic data in Seoul
19. Big Data and discrimination: perils, promises and solutions. A systematic review
20. A new Internet of Things architecture for real-time prediction of various diseases using machine learning on big data environment
21. Hadoop Map Reduce Techniques: Simplified Data Processing on Large Clusters with Data Mining
22. Big data and firm performance: The roles of market-directed capabilities and business strategy
23. Effective feature representation using symbolic approach for classification and clustering of big data
24. A longitudinal study of the actual value of big data and analytics: The role of industry environment
25. Information matching model and multi-angle tracking algorithm for loan loss-linking customers based on the family mobile social-contact big data network
26. A framework to simplify pre-processing location-based social media big data for sustainable urban planning and management
27. Role of institutional pressures and resources in the adoption of big data analytics powered artificial intelligence, sustainable manufacturing practices and circular economy capabilities
28. Language about the future on social media as a novel marker of anxiety and depression: A big-data and experimental analysis
29. Trajectory big data reveals spatial disparity of healthcare accessibility at the residential neighborhood scale
30. Significance and methodology: Preprocessing the big data for machine learning on TBM performance
31. Empowering conformance checking using Big Data through horizontal decomposition
32. Self-attention convolutional neural network optimized with season optimization algorithm Espoused Chronic Kidney Diseases Diagnosis in Big Data System
33. Big-data driven approaches in materials science for real-time detection and prevention of fraud
34. Big data spatial analysis of campers’ landscape preferences: Examining demand for amenities
35. Data Science, Machine learning and big data in Digital Journalism: A survey of state-of-the-art, challenges and opportunities
36. Orchestrating big data analytics capability for sustainability: A study of air pollution management in China
37. Exploratory analysis and performance prediction of big data transfer in High-performance Networks
38. High-performance implementation of evolutionary privacy-preserving algorithm for big data using GPU platform
39. IT-business alignment, big data analytics capability, and strategic decision-making: Moderating roles of event criticality and disruption of COVID-19
40. Enhancing big data security through integrating XSS scanner into fog nodes for SMEs gain