Performance Analysis of Meta-Heuristic-Based Query Optimization Algorithms for Large-scale Decision Support Systems
Keywords:
Cost Analysis, Genetic Algorithm, Meta-heuristic Algorithms, Performance Evaluation, Query Optimization, Run-time Analysis, Stochastic ApproachAbstract
Database systems continue to be fascinated in the alluring quest of query optimisation, a field distinguished by continual heuristic developments. Swift data access and analysis are of utmost importance in the dynamic world of Decision Support System (DSS) databases. This research introduces a novel stochastic DSS query optimizer, expanding the capabilities of existing genetic approaches. The Enumeration-based Query Optimizer (EBQO), Genetic-based Query Optimizer (GBQO), and the recently suggested Stochastic-based Query Optimizer (SBQO) emerge as prominent candidates within the spectrum of query optimisation approaches. In comparison to EBQO and GBQO, SBQO, a stochastic technique, exhibits superior relevance to query optimisation. Notably, SBQO surpasses its rivals in two essential areas: runtime effectiveness and Total Costs Optimisation. This notable efficiency underlines how well stochastic strategies work for obtaining the best results when it comes to query optimisation. The importance of stochastic approaches in improving query optimisation efforts is highlighted by these results, opening up a path to more effective and favourable outcomes. This study provides a convincing demonstration of the significant advantages that cutting-edge stochastic techniques, such as SBQO, may bring to the field of query optimisation, a crucial component of effective database administration and decision support.
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