Genetic algorithms for large join query optimization

Dong, Hongbin; Liang, Yiwen

doi:10.1145/1276958.1277193

Cited by 22 publications

(36 citation statements)

References 6 publications

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“…According to the commutative, equivalence and associative rules for the join operators, the logical optimizer generates firstly many different join trees, each of them is equivalent to the given query graph. For that such join tree, every base relation is represented by a leaf and the results of the join operation is represented by an inner node [3]. The assessment criteria of the logical optimizer depends mainly on the total cost of join operations included in any query access plan alternative.…”

Section: Optimization Processmentioning

confidence: 99%

“…Those factors include: (a) The number of base relations, (b) the equivalent join tree, (c) the expected cardinality of each involved relation, and (d) the expected occurrence for each distinct value of the involved relations' attributes [3]. Some of these factors are known a priori, such as the number of involved relations and the structure of the join tree.…”

Section: The Proposed Cost Modelmentioning

confidence: 99%

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An Enhanced Ant Colony-based Approach for Query Optimization

Hanafy¹,

Gadallah²,

Hefny³

2017

IJCA

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One of the mandatory processes for all those types of applications is the inquiry process of the stored huge amounts of data. Such process is either a predefined or an ad-hoc query. From the logical point of view, the query process depends mainly on many algebraic operations, including selection, projection and joining operations. The most important one of them is the join operation, which represents the key factor of the inquiry process to retrieve the related information from different data tables. Many approaches have been proposed aiming to reduce the cost of join operations. Yet, there is still a need for more query optimizing processes in order to reduce the query response time. This paper proposes an enhanced optimal query processing approach for inner and outer join, where the proposed model exploits an adopted Ant Colony Optimization.

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Section: Optimization Processmentioning

confidence: 99%

Section: The Proposed Cost Modelmentioning

confidence: 99%

An Enhanced Ant Colony-based Approach for Query Optimization

Hanafy¹,

Gadallah²,

Hefny³

2017

IJCA

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show abstract

“…Firstly, it generates a set of candidate query access plans represented as join trees or query graphs. In each join tree, each relation is represented by a leaf node and each join operation is represented by an inner node [2]. Consequently, the logical optimizer starts evaluating each candidate query access plan respecting the total cost of the involved join operations.…”

Section: Optimization Processmentioning

confidence: 99%

“…Those factors include: (a) The number of base relations, (b) the equivalent join tree, (c) the expected cardinality of each involved relation, and (d) the expected occurrence for each distinct value of the involved relations' attributes [2]. Some of these factors are known a priori such as the number of involved relations and the structure of the join tree.…”

Section: The Cost Model Of the Proposed Approachmentioning

confidence: 99%

Ant Colony-Based Approach for Query Optimization

Hanafy

Gadallah

2016

Data Mining and Big Data

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Abstract. Many approaches have been proposed aiming to reduce the cost of join operations. Such join operations represent the key factor of the inquiry process to retrieve related information from different data tables in large relational databases. Yet, there is still a need for more intelligent query optimizing approaches to reduce the response time of query execution. This paper proposes an approach for reaching optimal query access plans for complex relational database queries including a set of join operations. The proposed approach is based on ant colony optimization technique to benefit from its ability of parallel search over several constructive computational threads which aims to reach an optimal query access plan. A comparative study shows the added value of the proposed approach.

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“…The authors showed clear advantages of randomized algorithms to choose good plans. In addition, others works can be mentioned describing the quality of such algorithms [4], [5], [7], [8]. In spite of the good results demonstrated by randomized algorithms, these works present as result, the average cost or the best costs obtained by these algorithms.…”

Section: Introductionmentioning

confidence: 99%

Stableness in large join query optimization

Bini

Lange

Sunyé

et al. 2009

2009 24th International Symposium on Computer and Information Sciences

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In relational database model, the use of exhaustive search methods in the large join query optimization is prohibitive because of the exponential increase of search space. An alternative widely discussed is the use of randomized search techniques. Several previous researches have been showed that the use of randomized sampling in query optimization permits to find, in average, near optimal plans in polynomial time. However, due to their random components, the quality of yielded plans for the same query may vary a lot, making the response time of a submitted query unpredictable. On the other hand, the use of heuristic optimization may increase stability of response time. This characteristic is essential in environments where response time must be predicted. In this paper, we will compare a randomized algorithm and a heuristic algorithm applied to large join query optimization. We used an open source DBMS as experimental framework and we compared the quality and stability of these algorithms.

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Genetic algorithms for large join query optimization

Cited by 22 publications

References 6 publications

An Enhanced Ant Colony-based Approach for Query Optimization

An Enhanced Ant Colony-based Approach for Query Optimization

Ant Colony-Based Approach for Query Optimization

Stableness in large join query optimization

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