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Materialized view selection using HBMO

Author

Listed:
  • T. V. Vijay Kumar

    (Jawaharlal Nehru University)

  • Biri Arun

    (Jawaharlal Nehru University)

Abstract

Strategic business decision making has become far more complex, challenging and consequential in today’s modern and highly competitive economy. So, managers have been using decision support systems to assist them in making accurate, efficient and effective decisions. These systems takes hours and days to process massive data sets in order to find relevant information for answering analytical queries. As a result the query response times are high. This response time can be reduced substantially by selecting and materializing pre-computed views that can provide answers to analytical queries. In this paper, an attempt has been made to select optimal sets of views, which would significantly reduce response time of analytical queries. In this regard, honey bee mating optimization based view selection algorithm (HBMOVSA) is proposed that selects Top-K views, from amongst all possible views, in a multidimensional lattice. Experimental results show that HBMOVSA is able to select comparatively better quality of views when compared with those selected by the most fundamental view selection algorithm HRUA.

Suggested Citation

  • T. V. Vijay Kumar & Biri Arun, 2017. "Materialized view selection using HBMO," 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. 8(1), pages 379-392, January.
    • Handle: RePEc:spr:ijsaem:v:8:y:2017:i:1:d:10.1007_s13198-015-0356-4
    DOI: 10.1007/s13198-015-0356-4
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    References listed on IDEAS

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    1. T.V. Vijay Kumar, 2013. "Answering query-based selection of materialised views," International Journal of Information and Decision Sciences, Inderscience Enterprises Ltd, vol. 5(1), pages 103-116.
    2. T.V. Vijay Kumar & Kalyani Devi, 2012. "Materialised view construction in data warehouse for decision making," International Journal of Business Information Systems, Inderscience Enterprises Ltd, vol. 11(4), pages 379-396.
    3. Omid Haddad & Abbas Afshar & Miguel Mariño, 2006. "Honey-Bees Mating Optimization (HBMO) Algorithm: A New Heuristic Approach for Water Resources Optimization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 20(5), pages 661-680, October.
    4. T.V. Vijay Kumar & Santosh Kumar, 2015. "Materialised view selection using randomised algorithms," International Journal of Business Information Systems, Inderscience Enterprises Ltd, vol. 19(2), pages 224-240.
    5. Biren Shah & Karthik Ramachandran & Vijay Raghavan, 2006. "A Hybrid Approach for Data Warehouse View Selection," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 2(2), pages 1-37, April.
    6. T.V. Vijay Kumar & Mohammad Haider, 2015. "Query answering-based view selection," International Journal of Business Information Systems, Inderscience Enterprises Ltd, vol. 18(3), pages 338-353.
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    Cited by:

    1. 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.
    2. 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.
    3. 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.

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