Big data research [proposal ideas are shared by us its analytics is highly utilized among industries or firms for solving their business-related problems. Generally Research proposals play a crucial role since they shape the final outcome of your research paper. We’re dedicated to crafting papers that yield the best results for researchers. Here, we provide an instance of research proposal regarding the area of big data analytics in a structured format:

Research Proposal: Tackling Major Challenges in Big Data Analytics

Outline

Considering our data-based platform, big data analytics is one of the rapidly evolving domains. From extensive datasets, it extracts original perspectives and provides crucial capability to enhance firms. Apart from the benefits, several problems are arised due to the complications, evaluations and fast development of big data. Encompassing from data standard to system adaptability, this study efficiently intends to explore and solve the considerable twenty main issues in the field of big data analytics. This research mainly focuses on improving the availability, capability and authenticity of big data mechanisms through creating proper findings to these problems.

Introduction

Both possibilities and problems arise in the case of expansion of big data over diverse areas like IoT, finance and healthcare. Even though the capacity and application of big data analytics can be obstructed while addressing the several barriers, it effectively maintain the assurance of crucial perspectives and capabilities.  Along with novel findings to adopt the entire capacity of big data, this research proposal summarizes an extensive research into twenty important issues which related with big data analytics.

Goals

1. In big data analytics, twenty significant issues need to be detected and evaluated.
2. To solve these problems, innovative methodologies and models must be created.
3. By means of practical research and case works, we have to assure the suggested findings.

Literature Analysis

On the subject of big data analytics, numerous enduring obstacles are exposed through exploring the current literature. Generally, distinct problems like privacy considerations, data quality and system functionality are the key focus of the prior studies. For solving the relationship of these issues, there is a crucial necessity for a broad-based approach which is an existing challenge. 

Main References:

• Data Quality: In evaluating the findings of data standards, Kim and Choi (2015) intensely investigate the hybrid data cleansing techniques and emphasize the considerable problems.
• Adaptability: For managing an extensive amount of data, the constraints of existing big data systems must be evaluated effectively which is elaborately addressed by Jiang et al. (2014).
• Secrecy and Security: Regarding the big data, Li and Palanisamy (2016) discusses efficient privacy-preserving methods. In extensive security models, this reference paper addresses the critical gaps.

Detected Problems

In the field of big data analytics, we provide significant twenty problems that illustrate the fundamental challenges which our study intends to address:

1. Data Quality Management

• Key Concern: In extensive datasets, it is required to assure data flexibility, authenticity and integrality.
• Main Focus: We have to create advancement tools and evaluation of data standards in an automatic approach.
• Considerable Reference: As reflecting on big data backgrounds, data quality dimensions and problems are addressed in Batini et al. (2009).

2. Scalability and Performance

• Key Concern: For managing expansion of data volumes in an effective manner, evaluating the data processing systems can be a challenging task.
• Main Focus: To attain optimal functionality, distributed computing models are meant to be enhanced.
• Considerable Reference: Considering the big data systems, Zulkernine et al. (2013) specifies the challenges of adaptability.

3. Real-Time Data Processing

• Key Concern: In offering initial perspectives, it could be difficult to process and evaluate data streams in real-time.
• Main Focus: The functionality of real-time analytics models has to be optimized.
• Considerable Reference: Real-time big data analytics and its problems are discussed in Fang et al. (2015).

4. Data Integration

• Key Concern: From diverse sources and formats, it is crucial to synthesize heterogeneous data.
• Main Focus: Effective data synthesization and development findings should be developed.
• Considerable Reference: In big data synthesization, Dong and Srivastava (2013) extensively reviews the schema matching.

5. Privacy and Security

• Key Concern: Considering the big data platforms, it can be complex to secure sensible data and assure secrecy.
• Main Focus: Extensive privacy-preserving and security technologies must be modeled by us.
• Considerable Reference: On big data, secrecy problems are elaborately investigated in Li and Palanisamy (2016).

6. Data Governance

• Key Concern: In an efficient manner, it is required to handle data schemes, adherence and management.
• Main Focus: For data management and administrative adherence, implement the enhanced models.
• Considerable Reference: As regards big data, a data governance model is suggested by Ramakrishna et al. (2017).

7. Efficient Data Storage

• Key Concern: Extensive data sets are supposed to be collected and extracted in an effective approach.
• Main Focus: Regarding the functionality and adaptability, storage systems are meant to be enhanced.
• Considerable Reference: Specifically for big data, modern storage findings are investigated in Jiang et al. (2014).

8. Big Data Analytics and Query Optimization

• Key Concern: Across huge datasets, it demands to conduct effective analytics and enhance the queries.
• Main Focus: For enhanced query processing and development, advanced methods must be created.
• Considerable Reference: In big data, Kim et al. (2018) addresses the tactics of query optimization.

9. Energy Efficiency

• Key Concern: The energy usage of big data systems are required to be mitigated, which is considered as a major issue.
• Main Focus: Energy-effective data processing techniques should be investigated.
• Considerable Reference: Considering big data, energy-efficient computing is extensively explored in Beloglazov et al. (2011).

10. Data Visualization

• Key Concern: Specifically in an intelligible and adaptable approach, large-scale data ought to be visualized.
• Main Focus: Adaptable methods of data visualization must be examined.
• Considerable Reference: Data visualization problems in big data are intensely examined by Keim et al. (2010).

11. Infrastructure Management

• Key Concern: Big data architectures could be difficult to handle and preserve, which needs sufficient capabilities.
• Main Focus: For architecture tracking and productive management, advanced tools should be designed.
• Considerable Reference: Management problems of big data architectures are elaborately addressed in Chen et al. (2014).

12. Cloud Integration

• Key Concern: In cloud computing platforms, it can be complex to manage big data.
• Main Focus: On cloud environments, big data processing has to be enhanced.
• Considerable Reference: For big data, Armbrust et al. (2010) extensively examines cloud computing.

13. Data Lifecycle Management

• Key Concern: From consumption to logging, it demands to handle data lifecycle governance.
• Main Focus: Especially for systematic data lifecycle management, we must develop crucial models.
• Considerable Reference: In big data, data lifecycle management is intensely investigated in Chebotko et al. (2010).

14. IoT Data Management

• Key Concern: Data which is developed through IoT devices should be managed.
• Main Focus: Efficiently handle IoT data by creating advanced systems.
• Considerable Reference: IoT data management problems are explored in Gubbi et al. (2013).

15. Interoperability

• Key Concern: Among various big data streams, compatibility needs to be assured by us.
• Main Focus: To improve normalization and compatibility, efficient techniques are meant to be created.
• Considerable Reference: In big data systems, Zhang et al. (2011) address the issues of compatibility.

16. Data Provenance and Lineage

• Key Concern: The source and development of data has to be monitored efficiently.
• Main Focus: For preserving the data origin and heritage, we must implement techniques.
• Considerable Reference: In big data, Wang and Strong (1996) explore the problems of data lineage.

17. Handling Unstructured Data

• Key Concern: Unorganized data such as text and images ought to be operated and evaluated.
• Main Focus: To handle the unorganized data in an effective format, impactful methods need to be created by us.
• Considerable Reference: Generally in evaluating the unorganized data, main problems are addressed by Han et al. (2011).

18. Domain-Specific Applications

• Key Concern: For field-related problems, it is required to implement big data methods.
• Main Focus: In domains such as healthcare and finance, we have to solve the critical problems.
• Considerable Reference: Considering healthcare, Raghupathi and Raghupathi (2014) elaborately investigates the usage of big data.

19. Ethical Implications

• Key Concern: Moral problems of big data consumption have to be handled effectively.
• Main Focus: For ethical big data approaches, productive models are meant to be created.
• Considerable Reference: In big data analytics, moral problems are extensively addressed in Zwitter (2014).

20. Big Data and Blockchain Integration

• Key Concern: Particularly for authentic data management, blockchain has to be synthesized with big data.
• Main Focus: To improve data authenticity and clarity, synthesization techniques should be investigated.
• Considerable Reference: According to big data, Nakamoto (2008) exhibits various theories of blockchain.

Which is the best for big data analytics Python Scala or R LANGUAGE?

As reflecting on big data analytics, choosing the best language for performing projects is a complicated task. But, with our proper guidance, you are able to select suitable as well as compelling language for your project. Across these languages, some of the considerable merits and demerits are provided by us:

Python

Merits:

1. Practicality and Interpretability:

• For users who are new to python, it is very simple to learn and it includes uncomplicated syntax.
• Among data scientists and experts, cooperation is improved through the intelligibility of Python Code.

2. Enriched Ecosystem:

• Especially for big data and data analysis, Python encompasses a huge environment of libraries and models such as TensorFlow, Scikit-learn, pandas, SciPy and NumPy.
• By means of PySpark, Python efficiently synthesizes with Apache Spark for big data. Regarding machine learning and data manipulation, it offers an expansive API.

3. Flexibility:

• Python is effectively considered as a multipurpose language. Across data analysis, it is highly applicable for a broad scope of applications like automation and web development.

4. Community Assistance:

• Extensive and dynamic community is involved in Python. Considering the original tools and libraries, it assures effective assistance and constant growth.

5. Synthesization with Big Data Tools:

• With big data tools like Hive, Spark and Hadoop, Python can be broadly integrated.
• For distributed data processing, a user-friendly interface is offered through the libraries of Python like Dask.

Demerits:

1. Functionality:

• Particularly for CPU-involved programs, Python tends to be slow compared to other compiled languages such as Scala.
• When utilizing Python with big data models such as Spark, the performance gap is reduced significantly. Because, this model has the capability to manage distributed and parallel processing.

2. Concurrency Barriers:

• Regarding the multi-threaded applications, there is a sufficient necessity of Python’s GIL (Global Interpreter Lock). Even though in distributed big data processing, it is not considered as crucial.

Scala

Merits:

1. Functionality:

• Scala can be executed on the JVM (Java Virtual machine) and is a compiled language. In contrast to other interpreted languages such as Python, it offers best performance.
• In the case of its capability and consistency assistance, Scala is highly adaptable for extensive data processing.

2. Natural Synthesization with Spark:

• Scala is one of the powerful languages, where the Apache Spark was drafted initially. Extensive and optimized synthesization with Spark is involved in this language.
• To the advanced characteristics of Spark and developments, this language offers access in a direct manner.

3. Efficient Programming:

• Both component-based and object-oriented models are effectively assisted in Scala. For brief and meaningful codes, it provides access.
• For big data processing, it includes functional programming characteristics like multidimensional functions and stability, which is extremely beneficial.

4. Type Security:

• During the compiling time, Scala language aids in detecting the errors by means of its powerful static typing. It decreases the execution errors and enhances the code integrity.

5. Consistency and Similarity:

• Primarily for concurrent and parallel processing, this language offers extensive support. Top mange load densities of big data, it is very essential.

Demerits:

1. Complications:

• It can be difficult to interpret Scala’s syntax and perspectives of functional programming. For those new to this language, it is considered as a challenging learning path.
• Improvements for learners are restrained due to these issues.

2. Association and Environment:

• This language is not as vast as Python, even though it is regarded as an expanding community. It represents that there is a sufficient need of community assistance and libraries for carrying out non-Spark-based programs.

3. Less Adaptable for Prototyping:

• As against Python, it can be difficult to manage fast prototyping and experimentation due to the complications in Scala.

R Language

Merits:

1. Statistical Analysis:

• R language is developed as a popular choice among data scientists and statisticians, as it is efficiently proficient in data visualization and statistical analysis.
• For statistical modeling and plotting, an extensive set of packages are involved in this language like dplyr and ggplot2.

2. Data Visualization:

• Robust tools are provided by this R for data visualization. Personalized and complicated plots are facilitated here.

3. Huge Library Support:

• Particularly in the CRAN repository, R encompasses a wide collection of packages for data analysis. Broad scope of statistical and graphical methods is productively assisted.

4. Active Data Science Association:

• Among data scientists and statisticians, R language builds a powerful community. It efficiently promotes knowledge distribution and abundant resources.

Demerits:

1. Functionality:

• Specifically for extensive data processing, R is usually slower as compared to Python and Scala.
• Without synthesizing with other tools or further developments, it is more inappropriate for big data tasks because of functionality constraints.

2. Memory Management:

• In contrast to Python and Scala, memory management of R is not effectively capable. While handling the huge datasets, it is regarded as a critical barrier.

3. Synthesization with Big Data Models:

• As compared to Python and Scala, R is not synthesized smoothly even though it includes packages such as Sparklyr for combining with Spark.
• Instead of extensive data processing, R is broadly utilized for statistical analysis.

4. Rate of Progress for Non-Statisticians:

• For users who are not proficient in statistics context, it can be more complex to interpret R’s syntax and statistical analysis. .

Conclusion and Suggestions

• Python:

If you prefer flexibility, big data models, synthesization with a wide area of data analysis tools and practicality, Python is perceived as the optimal choice. For quick prototyping and enhancement, it is highly applicable.

• Scala:

Whether you require assistance for functional programming, smooth synthesization with Apache Spark and best performance, choose Scala which is extremely beneficial. Where consistency and capability are important, it can be suitable for extensive data processing tasks.

• R:

Primarily for projects which need enhanced data visualization and statistical analysis, R is a perfect option. Across statisticians and data scientists who concentrate on data reporting and analysis, it is broadly applicable.

Big Data Research Proposal Topics

Big Data Research Proposal Topics are shared in the existing environments, “Big data” plays a crucial role for improving the operational capability in fraud detection, smart traffic systems and furthermore. To aid you in writing an impactful research proposal in the field of big data analytics, sample research proposal topics are offered in this article.  Connect with our team for best solution.

1. Innovative application of big data technology in digital music copyright protection
2. Pedigree-ing Your Big Data: Data-Driven Big Data Privacy in Distributed Environments
3. Using Big Data Analytics to Create a Predictive Model for Joint Strike Fighter
4. Big data for institutional planning, decision support and academic excellence
5. Big Data Quality Framework: Pre-Processing Data in Weather Monitoring Application
6. Meeting Technology and Methodology into Health Big Data Analytics Scenarios
7. A survey of data partitioning and sampling methods to support big data analysis
8. A big data analytics framework for forecasting rare customer complaints: A use case of predicting MA members’ complaints to CMS
9. Big data cleaning model of smart grid based on Tensor Tucker decomposition
10. Hadoop as Big Data Operating System — The Emerging Approach for Managing Challenges of Enterprise Big Data Platform
11. Coding and Analytical Problems with Big Data When Conducting Research on Financial Crimes
12. Research on the Development of Maritime and Air Intelligence Big Data
13. Delphi Study to Identify Criteria for the Systematic Assessment of Data Protection Risks in the Context of Big Data Analytics
14. Cybersecurity in Big Data Era: From Securing Big Data to Data-Driven Security
15. A New Approach to Use Big Data Tools to Substitute Unstructured Data Warehouse
16. Research on the Application of Big Data Technology in Electronic Commerce Supply Chain
17. Conceptual design for comprehensive research support platform: Successful research data management generating big data from little data
18. Multidimensional Big Data Analytics over Big Web Knowledge Bases: Models, Issues, Research Trends, and a Reference Architecture
19. A system architecture for manufacturing process analysis based on big data and process mining techniques
20. Data quality assessment for on-line monitoring and measuring system of power quality based on big data and data provenance theory
21. Big data driven supply chain design and applications for blockchain: An action research using case study approach
22. Construction of a smart management system for physical health based on IoT and cloud computing with big data
23. Research on urban spatial structure based on the dual constraints of geographic environment and POI big data
24. A real-time Decision Support System for Big Data Analytic: A case of Dynamic Vehicle Routing Problems
25. Optimised Big Data analytics for health and safety hazards prediction in power infrastructure operations
26. Big data analytics adoption: Determinants and performances among small to medium-sized enterprises
27. Forecasting Chinese cruise tourism demand with big data: An optimized machine learning approach
28. Multi-output soft sensor modeling approach for penicillin fermentation process based on features of big data
29. Big data analytics as a roadmap towards green innovation, competitive advantage and environmental performance
30. Assessing the short-term effects of ozone exposure on the indicator of pharmacy visits in Nanjing based on mobile phone big data
31. A digital twin-based big data virtual and real fusion learning reference framework supported by industrial internet towards smart manufacturing
32. WeBrain: A web-based brainformatics platform of computational ecosystem for EEG big data analysis
33. Integrating Cuckoo search-Grey wolf optimization and Correlative Naive Bayes classifier with Map Reduce model for big data classification
34. Assessing the impact of big data on firm innovation performance: Big data is not always better data
35. Hybrid multi-objective evolutionary algorithm based on Search Manager framework for big data optimization problems
36. Feature engineering in big data analytics for IoT-enabled smart manufacturing – Comparison between deep learning and statistical learning
37. Consumption and symbolic capital in the metropolitan space: Integrating ‘old’ retail data sources with social big data
38. An intrusion detection approach using ensemble Support Vector Machine based Chaos Game Optimization algorithm in big data platform
39. The questions we ask: Opportunities and challenges for using big data analytics to strategically manage human capital resources
40. Internal circulation in China: Analyzing market segmentation and integration using big data for truck traffic flow