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A View Recommendation Greedy Algorithm for Materialized Views Selection

  • T. V. Vijay Kumar
  • Mohammad Haider
  • Santosh Kumar
Part of the Communications in Computer and Information Science book series (CCIS, volume 141)

Abstract

View selection is one of the key problems in view materialization. Several algorithms exist in literature for view selection, most of them are greedy based. The greedy algorithms, in each iteration, select the most beneficial view for materialization. Most of these algorithms are focused around algorithm HRUA. HRUA exhibits high run time complexity. As a result, it becomes infeasible to select views for higher dimensions. This scalability problem is addressed by the greedy algorithm VRGA proposed in this paper. Unlike HRUA, VRGA selects views from a smaller search space, comprising of recommended views, instead of all the views in the lattice. This enables VRGA to select views efficiently for higher dimensional data sets. Further, experimental results show that VRGA, in comparison to HRUA, requires significantly lesser benefit computations, view evaluation time and memory. Alternatively, HRUA has a slight edge over VRGA as regards to the total cost of evaluating all the views.

Keywords

Materialized view view selection greedy algorithm 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • T. V. Vijay Kumar
    • 1
  • Mohammad Haider
    • 1
    • 2
  • Santosh Kumar
    • 1
    • 3
  1. 1.School of Computer and Systems SciencesJawaharlal Nehru UniversityNew DelhiIndia
  2. 2.Mahatma Gandhi Mission’s College of Engineering and TechnologyNoidaIndia
  3. 3.Krishna Institute of Engineering and TechnologyGhaziabadIndia

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