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International Conference on Emerging Technologies in Computer Engineering

ICETCE 2019: Emerging Technologies in Computer Engineering: Microservices in Big Data Analytics pp 224–239Cite as

Join Query Optimization Using Genetic Ant Colony Optimization Algorithm for Distributed Databases

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 985))

Abstract

With the increase in geographical spread of data both in terms of quality and quantity, attention on the storage, retrieval and modification of this distributed data has become a prime area of research. The focus is on efficient, accurate and timely availability of information extracted from various underlying data centers. Processing of queries from these distributed database environments has become a challenging task for the database researchers because as the number of relations increases in the database, the join order complexity also increases. There are N! ways of solving a particular query where N represents the number of Relations in the join query. The success of query processed in the Distributed Database Environment depends largely on the search strategy implemented by the query optimizer whose task is to search an optimal Query Evaluation Plan in minimum time amongst the various query plans that can minimize the consumption of computer resources. Various search strategies beginning from Deterministic Algorithms to the most recent and modern Evolutionary Algorithms have contributed incalculably towards query optimization but they bear their own set of limitations and drawbacks. This research paper focuses on the implementation of a hybrid strategy of Evolutionary Algorithms for the optimization of join queries in DDBMS. The hybrid strategy is an integration of Ant Colony Optimization Algorithm and Genetic Algorithm and has been coined as GACO-D (Genetic Ant Colony Optimization Algorithm for Distributed Database). This paper focuses on the search of an optimal Join Order in minimum response time using GACO-D and also compares its performance with existing strategies.

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  1. International School of Informatics and Management, Jaipur, India

    Preeti Tiwari & Swati V. Chande

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  1. Preeti Tiwari
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  2. Swati V. Chande
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Correspondence to Preeti Tiwari .

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Editors and Affiliations

  1. College of Engineering, Iowa State University, Ames, IA, USA

    Arun K. Somani

  2. National University of Singapore, Singapore, Singapore

    Seeram Ramakrishna

  3. Swami Keshvanand Institute of Technology Management and Gramothan, Jaipur, India

    Anil Chaudhary

  4. Swami Keshvanand Institute of Technology Management and Gramothan, Jaipur, India

    Chothmal Choudhary

  5. Swami Keshvanand Institute of Technology Management and Gramothan, Jaipur, India

    Basant Agarwal

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Tiwari, P., Chande, S.V. (2019). Join Query Optimization Using Genetic Ant Colony Optimization Algorithm for Distributed Databases. In: Somani, A., Ramakrishna, S., Chaudhary, A., Choudhary, C., Agarwal, B. (eds) Emerging Technologies in Computer Engineering: Microservices in Big Data Analytics. ICETCE 2019. Communications in Computer and Information Science, vol 985. Springer, Singapore. https://doi.org/10.1007/978-981-13-8300-7_19

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