Hybrid algorithm for materialised view selection. (30th October 2020)

• Record Type: Journal Article
• Authors: Mayata, Raouf; Boukra, Abdelmadjid
• Is Part Of: International journal of innovative computing and applications. Volume 11:Number 4(2020)
• Page Start: 167
• Page End: 180
• Publication Date: 2020-10-30
• Subjects: data warehouse -- materialised view selection -- MVS -- metaheuristic -- quantum evolutionary -- colliding bodies optimisation -- CBO; Evolutionary computation -- Periodicals; Neural networks (Computer science) -- Periodicals; Genetic programming (Computer science) -- Periodicals; Biologically-inspired computing -- Periodicals; Swarm intelligence -- Periodicals; Quantum computers -- Periodicals; 006.3
• Languages: English
• ISSNs: 1751-648X

Abstract

Data warehouses store current and historical data, which are used for creating reports, for the purpose of supporting decision-making. A data warehouse uses materialised views in order to reduce the query processing time. Since materialising all view is not possible, due to space and maintenance constraints, materialised view selection became one of the crucial decisions in designing a data warehouse for optimal efficiency. In this paper the authors present a new hybrid algorithm named (QCBO) based on both quantum inspired evolutionary algorithm (QEA) and colliding bodies optimisation (CBO) to resolve the materialised view selection (MVS) problem. Also, some aspects of the well-known greedy algorithm (HRU) are included. The experimental results show that QCBO provides a fair balance between exploitation and exploration. Comparative study reveals the efficiency of the proposed algorithm in term of solution quality compared to well-known algorithms.