Title: Mining big data streams using business analytics tools: a bird's eye view on MOA and SAMOA

Authors: P.M. Arunkumar; S. Kannimuthu

Addresses: Department of Information Technology, Velalar College of Engineering and Technology, Erode, Tamilnadu, India ' Department of Computer Science and Engineering, Karpagam College of Engineering, Coimbatore, Tamilnadu, India

Abstract: Big data evolves as the prominent field in modern computing era. Big data analytics and its impact on extracting business intelligence is becoming indispensable for plethora of applications. The non-proprietary software revolution paved the way for illustrious evolution of tools like Weka, rapid miner, orange and R. Traditional data mining techniques hardly adapts to the requirements of rapid data analysis. The data stream processing algorithms that handle multitude of data endow with greater challenge in real time. Big data mining requires further improvisation in traditional tools to address the challenges of massive data processing. This paper highlights the importance of data stream mining and explores two important open source frameworks, namely massive online analysis (MOA) and scalable advanced massive online analysis (SAMOA). The implications of both the tools augurs well for further deliberations in big data research community. Business information system (BIS) models can reach unprecedented heights with the proliferation of these business analytics tools.

Keywords: big data; data mining; data streams; massive online analysis; MOA; business intelligence.

DOI: 10.1504/IJBIDM.2020.108761

International Journal of Business Intelligence and Data Mining, 2020 Vol.17 No.2, pp.226 - 236

Received: 13 Oct 2017
Accepted: 22 Dec 2017

Published online: 08 Apr 2020 *

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