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A FRAMEWORK FOR REAL-TIME STREAMING ANALYTICS USING MACHINE LEARNING APPROACH

D. Jayanthi, G. Sumathi

Abstract


The continuous stream of data generated by sensors, machines, vehicles, mobile phones, social media networks, and other real-time sources are compelling organizations to imagine what they could do with this data if they could gain insight into it. A real-time streaming platform must meet the needs of data scientists, developers and data center operations teams without requiring extensive custom code or brittle integration of many third party components. As more and more data is generated and collected, data analysis requires scalable, flexible, and high performing tools to provide insights in a timely fashion. However, organizations are facing a growing big data ecosystem where new tools emerge and “die” very quickly. Therefore, it can be very difficult to keep pace and choose the right tools. This paper focuses on the challenges that stream processing solution should address. Also, this paper analyzes the traditional analytic tools to bridge the gap between data being generated and data that can be analyzed effectively.

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References


1.André Leon Sampaio Gradvohl, Hermes Senger, Luciana Arantes, Pierre Sens,” Comparing Distributed Online Stream Processing Systems Considering Fault Tolerance Issues,Journal of Emerging Technologies in Web Intelligence, Vol 6, No 2 (2014), 174-179, May 2014,doi:10.4304/jetwi.6.2.174-179.

Rahnama A.H.A, “Distributed real-time sentiment analysis for big data social streams”, IEEE International Conference on Control, Decision and Information Technologies (CoDIT), (Nov 2014) page(s):789-794,doi:10.1109/CoDIT.2014.6996998”

Gianmarco De Francisci Morale, “SAMOA: A Platform for Mining Big Data Streams”, 22nd International Conference on WWW 2013 Companion, May 13–17, 2013, Rio de Janeiro, Brazil. ACM 978-1-4503-2038-2/13/05.

Mohit Maske*, Dr. Prakash Prasad, “A Real Time Processing and Streaming of Wireless Network Data using Storm “, International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE),vol 5,Issue 1, Jan 2015, ISSN:2277 128X,page(s):506-510

Gianmarco De Francisci Morales,Albert Bifet,SAMOA: “Scalable Advanced Massive Online Analysis”, Journal of Machine Learning Research 16 (2015) page(s):149-153

Bifet, A. ,De Francisci Morales, G.,Big “Data Stream Learning with SAMOA”, 2014 IEEE International Conference on Data Mining Workshop (ICDMW),page(s):1199 – 1202, 978-1-4799-4275-6

Dilpreet Singh, Chandan K Reddy,”A survey on platforms for big data analytics”, Journal of Big Data 2014,page(s): 2:8 doi:10.1186/s40537-014-0008-6

Georg Krempl,Indre Žliobaite,” Open challenges for data stream mining research”,ACM SIGKDD Explorations - Special issue on big data archive,Volume 16 Issue 1, June 2014,Pages 1-10

Min Chen • Shiwen Mao • Yunhao Li,” Big Data: A Survey”, © Springer Science+Business Media New York 2014, Mobile Netw Appl (2014) 19:171–209 DOI 10.1007/s11036-013-0489-0




DOI: http://dx.doi.org/10.6084/ijact.v0i0.521

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