Application of Ant Colony Optimization Algorithm to Multi-Join Query Optimization

Li, Nana; Liu, Yujuan; Dong, Yongfeng; Gu, Jun

doi:10.1007/978-3-540-92137-0_21

Cited by 24 publications

(21 citation statements)

References 3 publications

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“…However, these algorithms are successfully applied in centralized query optimization [1,21] and distributed database design [13,18]. Our proposed algorithm employs the model once introduced in OGA97 algorithm and its search strategy is based on the ant colony optimization metaheuristic.…”

Section: Related Workmentioning

confidence: 99%

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A multi-colony ant algorithm for optimizing join queries in distributed database systems

2013

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Distributed database systems provide a new data processing and storage technology for decentralized organizations of today. Query optimization, the process to generate an optimal execution plan for the posed query, is more challenging in such systems due to the huge search space of alternative plans incurred by distribution. As finding an optimal execution plan is computationally intractable, using stochastic-based algorithms has drawn the attention of most researchers. In this paper, for the first time, a multi-colony ant algorithm is proposed for optimizing join queries in a distributed environment where relations can be replicated but not fragmented. In the proposed algorithm, four types of ants collaborate to create an execution plan. Hence, there are four ant colonies in each iteration. Each type of ant makes an important decision to find the optimal plan. In order to evaluate the quality of the generated plan, two cost models are used-one based on the total time and the other on the response time. The proposed algorithm is compared with two previous genetic-based algorithms on chain, tree and cyclic queries. The experimental results show that the proposed algorithm saves up to about 80 % of optimization time with no significant difference in the quality of generated plans compared with the best existing genetic-based algorithm.

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Section: Related Workmentioning

confidence: 99%

“…fragmentation and allocation) in [13,18]. Furthermore, ant and bee colony algorithms have been applied for join query optimization in centralized database systems which have led to better results compared to genetic algorithms [1,21]. Two cost models are introduced-one based on the total time, and the other on the response time.…”

Section: Introductionmentioning

confidence: 98%

A multi-colony ant algorithm for optimizing join queries in distributed database systems

2013

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“…As one of the stochastic-based algorithms, the ACO algorithm was used in this investigation as a search methodology for optimizing queries in both centralized and distributed database environments [25]. In [26], the author proposed a multi-colony ant algorithm to improve join inquiries in distributed systems in which tables can be copied, however, they cannot be partitioned or fragmented.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Cost Ant Colony Algorithm to Optimize Query for Distributed Database Based on Quantum-Inspired Approach

2021

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A distributed database model can be effectively optimized through using query optimization. In such a model, the optimizer attempts to identify the most efficient join order, which minimizes the overall cost of the query plan. Successful query processing largely relies on the methodology implemented by the query optimizer. Many researches are concerned with the fact that query processing is considered an NP-hard problem especially when the query becomes bigger. Regarding large queries, it has been found that heuristic methods cannot cover all search spaces and may lead to falling in a local minimum. This paper examines how quantum-inspired ant colony algorithm, a hybrid strategy of probabilistic algorithms, can be devised to improve the cost of query joins in distributed databases. Quantum computing has the ability to diversify and expand, and thus covering large query search spaces. This enables the selection of the best trails, which speeds up convergence and helps avoid falling into a local optimum. With such a strategy, the algorithm aims to identify an optimal join order to reduce the total execution time. Experimental results show that the proposed quantum-inspired ant colony offers a faster convergence with better outcome when compared with the classic model.

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“…A new algorithm for solving the problem of multijoin query optimization [ 14 ] was designed based on ant colony optimization. The algorithm interprets defining heuristic information, implementing local and global pheromone update, and designing state transition rule.…”

Section: Related Workmentioning

confidence: 99%

A Novel Adaptive Cuckoo Search for Optimal Query Plan Generation

Gomathi¹,

Sharmila²

2014

The Scientific World Journal

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The emergence of multiple web pages day by day leads to the development of the semantic web technology. A World Wide Web Consortium (W3C) standard for storing semantic web data is the resource description framework (RDF). To enhance the efficiency in the execution time for querying large RDF graphs, the evolving metaheuristic algorithms become an alternate to the traditional query optimization methods. This paper focuses on the problem of query optimization of semantic web data. An efficient algorithm called adaptive Cuckoo search (ACS) for querying and generating optimal query plan for large RDF graphs is designed in this research. Experiments were conducted on different datasets with varying number of predicates. The experimental results have exposed that the proposed approach has provided significant results in terms of query execution time. The extent to which the algorithm is efficient is tested and the results are documented.

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Application of Ant Colony Optimization Algorithm to Multi-Join Query Optimization

Cited by 24 publications

References 3 publications

A multi-colony ant algorithm for optimizing join queries in distributed database systems

A multi-colony ant algorithm for optimizing join queries in distributed database systems

Dynamic Cost Ant Colony Algorithm to Optimize Query for Distributed Database Based on Quantum-Inspired Approach

A Novel Adaptive Cuckoo Search for Optimal Query Plan Generation

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