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Random Walk Grey Wolf Optimizer Algorithm for Materialized View Selection (RWGWOMVS)

Random Walk Grey Wolf Optimizer Algorithm for Materialized View Selection (RWGWOMVS)

Anjana Gosain, Kavita Sachdeva
Copyright: © 2020 |Pages: 22
ISBN13: 9781799829751|ISBN10: 1799829758|ISBN13 Softcover: 9781799829768|EISBN13: 9781799829775
DOI: 10.4018/978-1-7998-2975-1.ch005
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MLA

Gosain, Anjana, and Kavita Sachdeva. "Random Walk Grey Wolf Optimizer Algorithm for Materialized View Selection (RWGWOMVS)." Novel Approaches to Information Systems Design, edited by Naveen Prakash and Deepika Prakash, IGI Global, 2020, pp. 101-122. https://doi.org/10.4018/978-1-7998-2975-1.ch005

APA

Gosain, A. & Sachdeva, K. (2020). Random Walk Grey Wolf Optimizer Algorithm for Materialized View Selection (RWGWOMVS). In N. Prakash & D. Prakash (Eds.), Novel Approaches to Information Systems Design (pp. 101-122). IGI Global. https://doi.org/10.4018/978-1-7998-2975-1.ch005

Chicago

Gosain, Anjana, and Kavita Sachdeva. "Random Walk Grey Wolf Optimizer Algorithm for Materialized View Selection (RWGWOMVS)." In Novel Approaches to Information Systems Design, edited by Naveen Prakash and Deepika Prakash, 101-122. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-2975-1.ch005

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Abstract

Optimal selection of materialized views is crucial for enhancing the performance and efficiency of data warehouse to render decisions effectively. Numerous evolutionary optimization algorithms like particle swarm optimization (PSO), genetic algorithm (GA), bee colony optimization (BCO), backtracking search optimization algorithm (BSA), etc. have been used by researchers for the selection of views optimally. Various frameworks like multiple view processing plan (MVPP), lattice, and AND-OR view graphs have been used for representing the problem space of MVS problem. In this chapter, the authors have implemented random walk grey wolf optimizer (RWGWO) algorithm for materialized view selection (i.e., RWGWOMVS) on lattice framework to find an optimal set of views within the space constraint. RWGWOMVS gives superior results in terms of minimum total query processing cost when compared with GA, BSA, and PSO algorithm. The proposed method scales well on increasing the lattice dimensions and on increasing the number of queries triggered by users.

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