A Hybrid Metaheuristic Framework for Materialized View Selection in Data Warehouse Environments

Abstract

The huge response time taken by the analytical queries is one of the most challenging problems in a data warehouse. The main reason for this high response time is the enormous amount of data being queried and the complex nature of the queries. This problem can be addressed with Materialized View Selection (MVS), where the optimal views with low response time are selected. To this end, the possible views for the analytical queries are randomly defined in the search space. Since most of the existing solutions are formulated based on semi-optimal solutions, this work introduces a hybrid metaheuristic-based framework to address the issue reliably. The major gaps in the existing research works are the resource wastage issue and the space constraint issue. These gaps are aimed to be addressed by the proposed hybrid framework. The proposed work integrates a Genetic Algorithm (GA) with Aquila Optimizer (AO) to search for the most optimal views from the search space. The presented solution follows highly efficient exploratory and exploitation behaviors to optimize the considered problem. Based on an iterative procedure, the most optimal views are selected by the hybrid framework, and these views are evaluated for minimum cost and time. Finally, performance evaluations are carried out to signify the practicality of the proposed approach.