Furthermore, this encouraged us to write this blog for you: To present the recent advancement in Big Data, with the main objective in the application of surveying and processing large volume of data. This blog will provide insights on the following:
- What tools do you need?
- How to go about implementation in Big data?
- How to write the thesis?
Topic and base paper selection in cloud Big data:
To address the issues in Big data an exhaustive background research needs to be performed by referring to IEEE transaction papers in the prevailing years between 2010 to 2017.
Presently, Big Data is one of the most trending topics in the technology world. This spectrum is observed to be growing, and affecting several business industries. Here is a list of most important challenging areas in Big Data that is reflecting its near future that can be formulated into PhD topics:
- Convergence of IOT, cloud, and big data
- Integration of Big data in Cloud environment
- Virtualization in deep intelligence
- Analytics of Big data in Business intelligence
- Combination of big data with machine learning
- Big data security
- Regression analytics in advanced machine learning concepts
As you analyze the problems faced by Big data, the next will be to develop a well-informed strategies and savvy integrations. Besides, Big data can be analyzed by using the software tools commonly used as part of Advance Analytics disciplines such as Predictive Analysis Data Mining, and Text Analytics. To maximize the benefits of Big data, this developed framework need to be implemented and validated in real time analytics. This approach will comprise of tools such as Hadoop and related tools such as Yarn Spook, Spark, and Pig as well as No Sql databases. These technologies form the core of an open source software framework that supports the processing structured and unstructured data. You can pursue further research on a novel technique to achieve a high level of security needed for big data operations and, these shall be generated during the PhD tenure.
The large amount of data collected during the tenure of PhD including the research methodologies, implementation and results are split into smaller chunks and presented in the form of distinct sections in the thesis as follows:
In this chapter, you should provide a comprehensive background research prioritizing the area where big data can be driven to its efficacy and further identify the problems faced by this spectrum. Furthermore, this section should provide an overview of the complete research to be conducted.
- Literature review
In this section, you should analyze the research gap of the existing system based on the readiness, assessments, and ecosystem. Such that, it will provide an initial understanding of the issues to be addressed in the research.
- Research Methodology
This chapter should include the key milestones that will be required to provide a detailed timeline, investment models and visual prototypes for a practical and powerful solution for real time analytics in the research.
- Design and Implementation
You should include the implementation process in this section. Further details of process involved in creating a long-term strategy to maximize the security and benefits of Big data collaboratively should embrace in this section. Further, recommends an organizational structure, models and resources required for the research.
- Discussion and Conclusion
In order to facilitate the research, you ought to interpret the results in detail and compare the results obtained in previous research studies. This chapter further should concentrates on the best techniques to exploit big data to meet your specific objectives, and provides an insight towards new opportunities.