Massive Data Analysis: Tasks, Tools, Applications, and Challenges

Murali K. Pusala (), Mohsen Amini Salehi (), Jayasimha R. Katukuri (), Ying Xie () and Vijay Raghavan ()
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Murali K. Pusala: University of Louisiana Lafayette, Center of Advanced Computer Studies (CACS)
Mohsen Amini Salehi: University of Louisiana Lafayette, School of Computing and Informatics
Jayasimha R. Katukuri: University of Louisiana Lafayette, Center of Advanced Computer Studies (CACS)
Ying Xie: Kennesaw State University, Department of Computer Science
Vijay Raghavan: University of Louisiana Lafayette, Center of Advanced Computer Studies (CACS)

A chapter in Big Data Analytics, 2016, pp 11-40 from Springer

Abstract: Abstract In this study, we provide an overview of the state-of-the-art technologies in programming, computing, and storage of the massive data analytics landscape. We shed light on different types of analytics that can be performed on massive data. For that, we first provide a detailed taxonomy on different analytic types along with examples of each type. Next, we highlight technology trends of massive data analytics that are available for corporations, government agencies, and researchers. In addition, we enumerate several instances of opportunities that exist for turning massive data into knowledge. We describe and position two distinct case studies of massive data analytics that are being investigated in our research group: recommendation systems in e-commerce applications; and link discovery to predict unknown association of medical concepts. Finally, we discuss the lessons we have learnt and open challenges faced by researchers and businesses in the field of massive data analytics.

Keywords: Recommendation System; Link Prediction; Graph Database; Hadoop Distribute File System; MapReduce Framework (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/978-81-322-3628-3_2

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