Genetic optimization for the join ordering problem of database queries

Chande, Swati V.; Sinha, Manish Kumar

doi:10.1109/indcon.2011.6139336

Cited by 10 publications

(2 citation statements)

References 8 publications

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“…为了降低时间复杂度, 研究人员进一步提出了贪心算法、随机算法等. 贪心算法的中心思想是在树的每一级保持元组数目尽可能少的中间关系, 而随机算法包括遗传算法 [10] 、QuickPick [5] 等. 它们随机地产生连接决策, 并依据代价进行更新.…”

Section: 启发式方法unclassified

RLO: a reinforcement learning-based method for join optimization

Zhang

et al. 2020

Sci. Sin.-Inf.

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A machine learning-based method to detect fluorescent spots and an accelerated, parallel implementation of this method Chinese Science Bulletin 59, 3573 (2014); Rotated neighbor learning-based auto-configured evolutionary algorithm SCIENCE CHINA Information Sciences 59, 052101 (2016); Data fusion using Bayesian theory and reinforcement learning method SCIENCE CHINA Information Sciences 63, 170209 (2020); Nash-Q learning-based collaborative dispatch strategy for interconnected power systems

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Section: 启发式方法unclassified

RLO: a reinforcement learning-based method for join optimization

Zhang

et al. 2020

Sci. Sin.-Inf.

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“…Chande et al, [10] concentrated on join ordering issues in relational database and employed Genetic Algorithm to minimize the difficulty of competent choosing of join ordering for creating an optimum strategy by an optimizer. The queries employed for genetic query optimizer (GQO) are implemented in diverse atmosphere as to match their accuracy.…”

Section: Literature Surveymentioning

confidence: 99%

Query optimization using clustering and Genetic Algorithm for Distributed Databases

Lakshmi

Kumari

2016

2016 International Conference on Computer Communication and Informatics (ICCCI)

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Query Optimization is principally a multifaceted exploration job that searches for best plan amongst the semantically equal plans that are obtained from any given query. The execution of any processing datasets essentially depends on the capability of query optimization procedure to acquire competent query processing approaches. A Distributed Database System (DDS) is a group of autonomous cooperating integrated procedure. Query at a specified place may necessitate information from distant places in a Distributed Environment. In query optimization, the cost is accompanied by every query execution plan. Cost is the summation of native cost that is I/O cost, CPU cost at every location and cost of transmitting information amongst locations. The key issue of a Query Optimization in a Distributed Database System is to obtain an effective query strategy with an efficient accuracy and minimum response time or cost to execute the given query. In this paper a novel methodology is suggested that selected the best query plan as to execute the given query employing Genetic Algorithm Strategy for Distributed Databases and a Clustering Approach within the databases so as to execute the query plan. Genetic Algorithms are extensively employed and acceptable methods for very challenging optimization problems. This proposed technique gives efficient performance in different environment. The Experimental analysis of the proposed methodology is carried out on 100 different queries distributed over 20 different sites having 8 relations in each query. This is compared with the DB2 distributed optimizers and achieved an increased reliability and high performance with respect to the optimization cost and accuracy for the queries in the distributed databases

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Multi-join Query Optimization Using Modified ACO with GA

Kumar

Biswas

2020

Advances in Intelligent Systems and Computing

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Genetic optimization for the join ordering problem of database queries

Cited by 10 publications

References 8 publications

RLO: a reinforcement learning-based method for join optimization

RLO: a reinforcement learning-based method for join optimization

Query optimization using clustering and Genetic Algorithm for Distributed Databases

Multi-join Query Optimization Using Modified ACO with GA

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