A Critical Study of Big Data Techniques and Predictive Analytics Algorithms - Rao, B. Jogeswara; Babu, M.S. Prasad (International Journal of Computer Sciences and Engineering, 2018)
Big data is defined as the collection of a broad set of data. The tremendous increase in the usage of the internet over social media applications and forums such as mailing system, e-collection of research scholar articles, retrieval and online transaction data in the field of health leads to high exponential growth in the storage of data. These vast collections of data may lead to arising problems in big data analytics. Subsequently, the predictions based on unknown future events were performed by using Predictive analytics. This approach is found to utilize numerous techniques such as machine learning, statistics, data mining, modelling, and artificial intelligence in analysing the data for predicting the future. However, in past few decades, there have been significant developments in various techniques, architecture, tools, and platforms for managing the enormous amount of big data and to predict its future events considering predictive analytic algorithms. This paper provides a detailed survey of existing techniques, computing tools used in big data analysis and predictive analytic algorithms with its advantages and limitations. Further, this paper discusses the essential aspects considered to overcome the analytic data problems regarding availability and scalability and its various applications.