Title: Multi-objective multi-join query optimisation using modified grey wolf optimisation

Authors: Deepak Kumar; Sushil Kumar; Rohit Bansal

Addresses: ASET Amity University, Amity University Noida, India ' ASET Amity University, Amity University Noida, India ' Department of Management, RGIPT, Noida, India

Abstract: Nowadays information retrieved by a query is based upon extracting data across the world, which are located in different data sites. In distributed database management systems (DDBMS), due to partitioning or replication of data among several sites the relations required for an answer of a query may be stored at several data sites (DS). Many experimental results have showed that combination of optimal join order (OJO) and optimal selection of relations in query plan (QP) gives out better results compare to the several existing query optimising methodologies like teacher-learner based optimisation (TLBO), genetic algorithm (GA), etc. In this paper an approach has been proposed to compute a best optimal QP that could answer the user query with minimal cost values and minimum time using modified grey wolf optimisation algorithm (MGWO) which is multi-objective constrained. Proposed approach also aims for producing OJO in order to reduce the dimensionality complexity of the QP.

Keywords: data site; DS; distributed database management systems; DDBMS; grey wolf optimisation; optimal join order; OJO; teacher-learner based optimisation; TLBO.

DOI: 10.1504/IJAIP.2020.108760

International Journal of Advanced Intelligence Paradigms, 2020 Vol.17 No.1/2, pp.67 - 79

Received: 04 Nov 2017
Accepted: 11 Jan 2018

Published online: 03 Aug 2020 *

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