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Solving Heterogeneous Big Data Mining Problems Using Multi-Objective Optimization

Solving Heterogeneous Big Data Mining Problems Using Multi-Objective Optimization

Farid Bourennani
Copyright: © 2019 |Volume: 10 |Issue: 4 |Pages: 20
ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781522566090|DOI: 10.4018/IJAMC.2019100102
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MLA

Bourennani, Farid. "Solving Heterogeneous Big Data Mining Problems Using Multi-Objective Optimization." IJAMC vol.10, no.4 2019: pp.18-37. http://doi.org/10.4018/IJAMC.2019100102

APA

Bourennani, F. (2019). Solving Heterogeneous Big Data Mining Problems Using Multi-Objective Optimization. International Journal of Applied Metaheuristic Computing (IJAMC), 10(4), 18-37. http://doi.org/10.4018/IJAMC.2019100102

Chicago

Bourennani, Farid. "Solving Heterogeneous Big Data Mining Problems Using Multi-Objective Optimization," International Journal of Applied Metaheuristic Computing (IJAMC) 10, no.4: 18-37. http://doi.org/10.4018/IJAMC.2019100102

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Abstract

Nowadays, we have access to unprecedented quantities of data composed of heterogeneous data types (HDT). Heterogeneous data mining (HDM) is a new research area that focuses on the processing of HDT. Usually, input data is transformed into an algebraic model before data processing. However, how to combine the representations of HDT into a single model for a unified processing of big data is an open question. In this article, the authors attempt to find answers to this question by solving a data integration (DI) problem which involves the processing of seven HDT. They propose to solve the DI problem by combining multi-objective optimization and self-organizing maps to find optimal parameters settings for most accurate HDM results. The preliminary results are promising, and a post processing algorithm is proposed which makes the DI operations much simpler and more accurate.

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