What are the research scopes in big data analysis and problem faced by the researcher while preparing a thesis on big data analytics?

In Brief:

• Big Data is a Collection of large and complex data sets which are difficult to process with on-hand database management tools.
• Scalability and performance in big data analysis can be achieved with the help of parallelization techniques. Parallelization and divide and conquer are the best algorithms used to handle big data.
• Many Organizations uses online big data applications which are economical and effective, but it lay way for many security threats.

PA – What are the research scopes in big data analysis

Introduction

In recent days Big Data Analytics has become a most popular research area. It has acquired a lot of attention in academic as well as industries both private and public sectors. It is developing as an emerging at the same time challenging research area. Many scientists defined big data in different ways; Gartner defines it as high volume, high velocity and a wide variety of information that requires new processing form which permits enhanced decision making, insight discovery and process optimization. It is a collection of large and complex data sets that are difficult to process in on-hand database management tools. Every day worldwide trillions of data are created on social media networking sites, from scientific experiments, over the mobile conversation, network sensors and other sources. So we require new tool and technique which can help in big data organization, store and analyze (Several research papers published by various researchers about how to tackle big data problems effectively). It is an emerging research area with lots of research scopes, where primary research carried out, but a lot are expecting to be in future. PhD Projects in Big Data will increase the scope of your research work.

Research Scope in Big Data Analytics

Present database management systems are inadequate to store large flood of big data. Researchers have suggested that commercial DBMS are unsuitable for processing a large amount of data and suggesting new big database management system which will be economical and scalable. Scalability and performance in big data analysis can be achieved with the help of parallelization techniques and algorithms. Many researchers have defined new theories, technologies and methods for managing and analyzing big data. Present day’s database management systems are insufficient to store increasing flood of big data. Hence, arise a need for hierarchical storage architectures which are efficient in handling storage challenges of Big Data. Existing data processing algorithms are excellent in handling homogeneous and static data, but present-day data are created from numerous resources which are heterogeneous and dynamic. Scalable data processing algorithms are in need to process such types of data. Speed is the main criteria need to consider while processing queries in big data. Indexing is the best choice in a complex query processing case. Parallelization and divide and conquer are the best algorithms used to handling big data. Many Organizations uses online big data applications which are economical and effective, but it makes way for many security threats. Security is the main concern for the researcher as well as organizations. The PhD Program Big Data Analytics is an interdisciplinary, i.e., comprises more than one branch STEM, PhD program concentrating on system and technology used for processing data and information. Unlike other data science program it comprises the human and social implications of information, technology, bringing in critical components of cognition, ethics, biases and storytelling into a strong, big data analytics curriculum.

Research problems in Big Data analysis

Let us discuss some of the Research Problems in Big Data Research Topics

• Scalability- Scale up or increase the structure of system architecture to meet parallel data processing.
• Adoption and analytics of big data for cloud computing platforms-It reduce the cost of complex analytics in the cloud.
• Security and Confidentiality problems
• Effective storing and transmission problem
• Graph databases

PhD Thesis on Big Data Analytics which act as a major research sector concerning the future; it is one of the popular emerging research areas. Main things need to consider while writing PhD Projects in Big Data is selecting PhD Research Topics in Big Data Analytics.

Selecting PhD Research Topics in Big Data Analytics

There are many topics on PhD Big Data domain; you have to look for topics which are more interesting and related to future research enhancement with open source tools. Big Data handles a large amount of data collecting, processing and transmitting it through modern technology. It made way to analyze and manage large datasets and how to create, use and apply it in different fields like science and physics. Clear sight over big data and its application help you to identify a good research topic and significant research problem statement needs to be addressed in future, which will bring scope to your research work. Here some of the PhD Research Topics in Big Data Analytics Enhancing Fault Detection for Big Data Analytics through Evolutionary Computation.

• An effective approach to detecting a failure in big data analytics using clustering and classification technique
• An effective technique applied for improving classification problem using a clustering approach.
• Incorporating the Apriori Algorithm with MapReduce approach to Remove Classification problems

Confusion while selecting a Big Data tool

Confusion often arises while selecting the tool for big data analysis and storage area, which will be the best technology for storage HBase or Cassandra. Hadoop Map Reduction or spark, which will be the better option for data analytics and storage? These are some of the common queries that need to be considered. But most of the times many ends up being confused and select inappropriate technology which will lead to wastage of resources, money, time and effort. The best way to handle it is by getting professional help. You can get help from professionals who are experts in big data tools. Our PhD Guidance in Big Data Helps in all aspect like choosing the topic, tool selection and other features. Based on their advice, and you can select the best tools.

Conclusion

Thesis preparation is a long and complicated process. Many research works often fail due to lack of knowledge in a particular research field, and confusion may arise in handling different tools, you can get support from professionals who are experts in big data tools. And you can also get guidance from our PhD Guidance in Big Data service to identify the right approach for your Research and Thesis work.

References

1. Kakhani, M. K., Kakhani, S., & Biradar, S. R. (2015). Research issues in big data analytics. International Journal of Application or Innovation in Engineering & Management, 2(8), 228-232.
2. Akhil, S., & Uma, D. K. (2017). Survey on the Challenges and Issues on Big Data Analytics. International Journal of Mechanical Engineering and Technology, 8, 12.
3. Acharjya, D. P., & Ahmed, K. (2016). A survey on big data analytics: challenges, open research issues and tools. International Journal of Advanced Computer Science and Applications, 7(2), 511-518.