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Dynamic Cost Ant Colony Algorithm for Optimize Distributed Database Query

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Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020) (AICV 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1153))

Abstract

Optimizing query in distributed database is considered as the most important part of a database system. The optimizer tries to find an optimal join order which reduces the query execution cost. Many factors may affect the execution cost of a query, including communication costs, resources, and access to large distributed data sets. When the number of relations and number of joins in a query increases, the complexity of the optimizer also increases. The success of query execution heavily influenced by the search method which is performed using the query optimizer. Processing of queries is considered as NP-hard problem and many researchers are focused on this problem in recent years. Researches are trying to build an appropriate algorithm to seek an optimal solution especially when the size of the database increases. In this paper, an ant colony algorithm as one of the hybrid strategy of evolutionary algorithms is utilized to find a solution for join query optimization problem in the distributed database systems. Unlike traditional ant colony-based query optimization techniques that based on static cost, the suggested model relies on dynamic cost which calculates the cost while the execution plan is built. Using this strategy, the algorithm aims to find an optimal join order which minimizes the total execution time. Experimental results show that the proposed model can handle different number of join entities. Also, the algorithm is affected by the number of ants used. Better results are obtained in case of large joined if the number of used ants increased.

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Author information

Authors and Affiliations

  1. Institute of Graduate Studies and Research, Alexandria University, Alexandria, Egypt

    Sayed A. Mohsin & Saad M. Darwish

  2. Faculty of Science, Alexandria University, Alexandria, Egypt

    Ahmed Younes

Authors
  1. Sayed A. Mohsin
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  2. Saad M. Darwish
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  3. Ahmed Younes
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Corresponding authors

Correspondence to Sayed A. Mohsin , Saad M. Darwish or Ahmed Younes .

Editor information

Editors and Affiliations

  1. Information Technology Department, Cairo University, Faculty of Computers and Information, Giza, Egypt

    Aboul-Ella Hassanien

  2. Faculty of Computers and Information, Benha University, Banha, Egypt

    Ahmad Taher Azar

  3. Faculty of Computers and Information, Suez Canal University, Ismailia, Egypt

    Tarek Gaber

  4. Departamento de Ciencias Computacionales, Universidad de Guadalajara, CUCEI, Guadajalara, Jalisco, Mexico

    Diego Oliva

  5. Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt

    Fahmy M. Tolba

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Mohsin, S.A., Darwish, S.M., Younes, A. (2020). Dynamic Cost Ant Colony Algorithm for Optimize Distributed Database Query. In: Hassanien, AE., Azar, A., Gaber, T., Oliva, D., Tolba, F. (eds) Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020). AICV 2020. Advances in Intelligent Systems and Computing, vol 1153. Springer, Cham. https://doi.org/10.1007/978-3-030-44289-7_17

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