Affiliations: [a] JNTUH, Hyderabad, Telangana, India | [b] CSE Department, VNRVJIET, Hyderabad, Telangana, India | [c] CSE Department, JNTUCEH, Hyderabad, Telangana, India
Correspondence:
[*]
Corresponding author: B. Srivani, Research Scholar, JNTUH, Hyderabad, Telangana, India. E-mail: srivaanib@gmail.com.
Abstract: Rapid growth in technology and information lead the human to witness the improved growth in velocity, volume of data, and variety. The data in the business organizations demonstrate the development of big data applications. Because of the improving demand of applications, analysis of sophisticated streaming big data tends to become a significant area in data mining. One of the significant aspects of the research is employing deep learning approaches for effective extraction of complex data representations. Accordingly, this survey provides the detailed review of big data classification methodologies, like deep learning based techniques, Convolutional Neural Network (CNN) based techniques, K-Nearest Neighbor (KNN) based techniques, Neural Network (NN) based techniques, fuzzy based techniques, and Support vector based techniques, and so on. Moreover, a detailed study is made by concerning the parameters, like evaluation metrics, implementation tool, employed framework, datasets utilized, adopted classification methods, and accuracy range obtained by various techniques. Eventually, the research gaps and issues of various big data classification schemes are presented.
Keywords: Big data streaming, classification, CNN, accuracy, Map Reduce framework
