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Materialized View Selection using Artificial Bee Colony Optimization

Author

Listed:
  • Biri Arun

    (Jawaharlal Nehru University, School of Computer and Systems Sciences, New Delhi, India)

  • T.V. Vijay Kumar

    (Jawaharlal Nehru University, School of Computer and Systems Sciences, New Delhi, India)

Abstract

Data warehouse is an essential component of almost every modern enterprise information system. It stores huge amount of subject-oriented, time-stamped, non-volatile and integrated data. It is highly required of the system to respond to complex online analytical queries posed against its data warehouse in seconds for efficient decision making. Optimization of online analytical query processing (OLAP) could substantially minimize delays in query response time. Materialized view is an efficient and effective OLAP query optimization technique to minimize query response time. Selecting a set of such appropriate views for materialization is referred to as view selection, which is a nontrivial task. In this regard, an Artificial Bee Colony (ABC) based view selection algorithm (ABCVSA), which has been adapted by incorporating N-point and GBFS based N-point random insertion operations, to select Top-K views from a multidimensional lattice is proposed. Experimental results show that ABCVSA performs better than the most fundamental view selection algorithm HRUA. Thus, the views selected using ABCVSA on materialization would reduce the query response time of OLAP queries and thereby aid analysts in arriving at strategic business decisions in an effective manner.

Suggested Citation

  • Biri Arun & T.V. Vijay Kumar, 2017. "Materialized View Selection using Artificial Bee Colony Optimization," International Journal of Intelligent Information Technologies (IJIIT), IGI Global, vol. 13(1), pages 26-49, January.
    • Handle: RePEc:igg:jiit00:v:13:y:2017:i:1:p:26-49
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    Cited by:

    1. Jay Prakash & T. V. Vijay Kumar, 2020. "Multi-objective materialized view selection using NSGA-II," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(5), pages 972-984, October.
    2. Akshay Kumar & T. V. Vijay Kumar, 2022. "Multi-Objective Big Data View Materialization Using MOGA," International Journal of Applied Metaheuristic Computing (IJAMC), IGI Global, vol. 13(1), pages 1-28, January.
    3. Jay Prakash & T. V. Vijay Kumar, 2020. "Multi-objective materialized view selection using MOGA," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(2), pages 220-231, July.

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