Welcome to the InfoSci Platform
Evolutionary Optimization for Prioritized Materialized View Selection: An Exploratory Analysis

Evolutionary Optimization for Prioritized Materialized View Selection: An Exploratory Analysis

Heena Madaan, Anjana Gosain
Copyright: © 2022 |Volume: 12 |Issue: 3 |Pages: 18
ISSN: 2155-6377|EISSN: 2155-6385|EISBN13: 9781683182108|DOI: 10.4018/IJIRR.300295
Cite Article Cite Article

MLA

Madaan, Heena, and Anjana Gosain. "Evolutionary Optimization for Prioritized Materialized View Selection: An Exploratory Analysis." IJIRR vol.12, no.3 2022: pp.1-18. http://doi.org/10.4018/IJIRR.300295

APA

Madaan, H. & Gosain, A. (2022). Evolutionary Optimization for Prioritized Materialized View Selection: An Exploratory Analysis. International Journal of Information Retrieval Research (IJIRR), 12(3), 1-18. http://doi.org/10.4018/IJIRR.300295

Chicago

Madaan, Heena, and Anjana Gosain. "Evolutionary Optimization for Prioritized Materialized View Selection: An Exploratory Analysis," International Journal of Information Retrieval Research (IJIRR) 12, no.3: 1-18. http://doi.org/10.4018/IJIRR.300295

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Selecting appropriate views that provide faster query response time is a critical decision in data warehouse design. Top-level users expect quick results from a data warehouse for faster decision-making to gain a competitive edge in business. Prioritizing a view can distinguish views required to answer top-level users' queries from regular users and provide a better selection chance. The prioritized materialized view selection (PMVS) problem addresses how to utilize the given space to materialize prioritized views more relevant to users. Particle swarm optimization algorithm has been used to achieve minimized query processing costs. Evolutionary algorithms are widely known to solve complex optimization problems quickly by reaching a semi-optimal solution. This paper explores the performance of six evolutionary algorithms: particle swarm optimization, coral reef optimization, cuckoo search, ant colony optimization, grey wolf optimization, and artificial bee colony. The results of empirical and statistical analysis show that PSO, CRO, and GWO algorithms are best suited to solve PMVS.