Big data analytics based on PANFIS MapReduce
Files
Date
2018
Authors
Za'in, C.
Pratama, M.
Lughofer, E.
Ferdaus, M.
Cai, Q.
Prasad, M.
Editors
Ozawa, S.
Pratama, M.
Roy, A.
Tan, A.W.
Angelov, P.P.
Pratama, M.
Roy, A.
Tan, A.W.
Angelov, P.P.
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Conference paper
Citation
Procedia Computer Science, 2018 / Ozawa, S., Pratama, M., Roy, A., Tan, A.W., Angelov, P.P. (ed./s), vol.144, pp.140-152
Statement of Responsibility
Conference Name
3rd International Neural Network Society Conference on Big Data and Deep Learning, INNS BDDL 2018 (17 Apr 2018 - 19 Apr 2018 : Bali, Indonesia)
Abstract
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.
School/Discipline
Dissertation Note
Provenance
Description
Access Status
Rights
Copyright 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license. (https://creativecommons.org/licenses/by-nc-nd/4.0/)