Evolutionary Computation Access on Incremental Map Reduce for Mining Large Scale Data
M. Blessa Binolin Pepsi1, S. Haseena2, S. Saroja3
1M. Blessa Binolin Pepsi, Senior Assistant Professor, Department of Information Technology, Mepco Schlenk Engineering College, Sivakasi (Tamil Nadu), India.
2S. Haseena, Senior Assistant Professor, Department of Information Technology, Mepco Schlenk Engineering College, Sivakasi (Tamil Nadu), India.
3S. Saroja, Senior Assistant Professor, Department of Information Technology, Mepco Schlenk Engineering College, Sivakasi (Tamil Nadu), India.
Manuscript received on 21 July 2019 | Revised Manuscript received on 03 August 2019 | Manuscript Published on 10 August 2019 | PP: 860-865 | Volume-8 Issue-2S3 July 2019 | Retrieval Number: B11610782S319/2019©BEIESP | DOI: 10.35940/ijrte.B1161.0782S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In recent era, data updates arrive constantly from different areas like social network, finance, healthcare, e-commerce etc… Hence the data becomes large and computation on it becomes difficult. A framework for mining data earlyand to refresh the computed result with the new data arrival is proposed. The framework includes an incremental mapreduce method on hadoop with evolutionary computation algorithm for reduction in time complexity and increased accuracy. Proposed approach is a key pair level incremental iterative processing to Mapreduce for mining big data and uses particle swarm optimization to avoid recomputation from scratch on the new data arrived. Thereby the I/O overhead gets reduced for accessing predefined states. Experimental results were tested on three iterative algorithms in hadoop showed good performance compared to traditional mapreduce with sequential computation access.
Keywords: Mapreduce, Incremental Iterative Computation, Big Data, Evolutionary Computation, Bipartite Graph.
Scope of the Article: Data Mining