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Multi-Objective Big Data View Materialization Using NSGA-II

Multi-Objective Big Data View Materialization Using NSGA-II

Akshay Kumar, T. V. Vijay Kumar
Copyright: © 2021 |Volume: 34 |Issue: 2 |Pages: 28
ISSN: 1040-1628|EISSN: 1533-7979|EISBN13: 9781799859130|DOI: 10.4018/IRMJ.2021040101
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MLA

Kumar, Akshay, and T. V. Vijay Kumar. "Multi-Objective Big Data View Materialization Using NSGA-II." IRMJ vol.34, no.2 2021: pp.1-28. http://doi.org/10.4018/IRMJ.2021040101

APA

Kumar, A. & Vijay Kumar, T. V. (2021). Multi-Objective Big Data View Materialization Using NSGA-II. Information Resources Management Journal (IRMJ), 34(2), 1-28. http://doi.org/10.4018/IRMJ.2021040101

Chicago

Kumar, Akshay, and T. V. Vijay Kumar. "Multi-Objective Big Data View Materialization Using NSGA-II," Information Resources Management Journal (IRMJ) 34, no.2: 1-28. http://doi.org/10.4018/IRMJ.2021040101

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Abstract

Big data views, in the context of distributed file system (DFS), are defined over structured, semi-structured and unstructured data that are voluminous in nature with the purpose to reduce the response time of queries over Big data. As the size of semi-structured and unstructured data in Big data is very large compared to structured data, a framework based on query attributes on Big data can be used to identify Big data views. Materializing Big data views can enhance the query response time and facilitate efficient distribution of data over the DFS based application. Given all the Big data views cannot be materialized, therefore, a subset of Big data views should be selected for materialization. The purpose of view selection for materialization is to improve query response time subject to resource constraints. The Big data view materialization problem was defined as a bi-objective problem with the two objectives- minimization of query evaluation cost and minimization of the update processing cost, with a constraint on the total size of the materialized views. This problem is addressed in this paper using multi-objective genetic algorithm NSGA-II. The experimental results show that proposed NSGA-II based Big data view selection algorithm is able to select reasonably good quality views for materialization.

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