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Answering query-based selection of materialised views

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  • T.V. Vijay Kumar

Abstract

Materialised views aim to improve the response time of analytical queries posed on a data warehouse. This entails that they contain information that can provide answers to most of the future queries. The selection of such information is referred to as view selection. Several view selection algorithms exist in literature, most of which are greedy-based. In this paper, an answering query-based view selection approach (AQVSA), which considers both the size and the query frequency of each view, to greedily select top-k views for materialisation is presented. AQVSA first arrives at a reduced set of candidate views based on the query frequency of each view. This is followed by greedily selecting beneficial views from amongst these candidate views. Further, the experimental results show that AQVSA is able to achieve an acceptable trade-off between the total cost of evaluating all the views and the total number of queries answered by the selected views.

Suggested Citation

  • 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.
    • Handle: RePEc:ids:ijidsc:v:5:y:2013:i:1:p:103-116
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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. 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.
    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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