Big Data Analytics based on PANFIS MapReduce

Za'in, C; Pratama, M; Lughofer, E; Ferdaus, M; Cai, Q; Prasad, M

Big Data Analytics based on PANFIS MapReduce

Za'in, C Pratama, M Lughofer, E Ferdaus, M Cai, Q Prasad, M

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Publication Type:: Conference Proceeding
Citation:: Procedia Computer Science, 2018, 144 pp. 140 - 152
Issue Date:: 2018-01-01

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Field	Value	Language
dc.contributor.author	Za'in, C	en_US
dc.contributor.author	Pratama, M	en_US
dc.contributor.author	Lughofer, E	en_US
dc.contributor.author	Ferdaus, M	en_US
dc.contributor.author	Cai, Q	en_US
dc.contributor.author	Prasad, M https://orcid.org/0000-0001-9719-2253	en_US
dc.date.issued	2018-01-01	en_US
dc.identifier.citation	Procedia Computer Science, 2018, 144 pp. 140 - 152	en_US
dc.identifier.uri	http://hdl.handle.net/10453/131740
dc.description.abstract	© 2018 The Authors. Published by Elsevier Ltd. In this paper, a big data analytic framework is introduced for processing high-frequency data stream. This framework architecture is developed by combining an advanced evolving learning algorithm namely Parsimonious Network Fuzzy Inference System (PANFIS) with MapReduce parallel computation, where PANFIS has the capability of processing data stream in large volume. Big datasets are learnt chunk by chunk by processors in MapReduce environment and the results are fused by rule merging method, that reduces the complexity of the rules. The performance measurement has been conducted, and the results are showing that the MapReduce framework along with PANFIS evolving system helps to reduce the processing time around 22 percent in average in comparison with the PANFIS algorithm without reducing performance in accuracy.	en_US
dc.relation.ispartof	Procedia Computer Science	en_US
dc.relation.isbasedon	10.1016/j.procs.2018.10.514	en_US
dc.title	Big Data Analytics based on PANFIS MapReduce	en_US
dc.type	Conference Proceeding
utslib.citation.volume	144	en_US
utslib.for	0804 Data Format	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	10 Technology	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	144	en_US

Abstract:

© 2018 The Authors. Published by Elsevier Ltd. In this paper, a big data analytic framework is introduced for processing high-frequency data stream. This framework architecture is developed by combining an advanced evolving learning algorithm namely Parsimonious Network Fuzzy Inference System (PANFIS) with MapReduce parallel computation, where PANFIS has the capability of processing data stream in large volume. Big datasets are learnt chunk by chunk by processors in MapReduce environment and the results are fused by rule merging method, that reduces the complexity of the rules. The performance measurement has been conducted, and the results are showing that the MapReduce framework along with PANFIS evolving system helps to reduce the processing time around 22 percent in average in comparison with the PANFIS algorithm without reducing performance in accuracy.

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http://hdl.handle.net/10453/131740