Frequent Queries Selection for View Materialization

  • T. V. Vijay Kumar
  • Gaurav Dubey
  • Archana Singh
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 177)

Abstract

A data warehouse stores historical data for answering analytical queries. These analytical queries are long, complex and exploratory in nature and, when processed against a large data warehouse, consume a lot of time for processing. As a result the query response time is high. This time can be reduced by materializing views over a data warehouse. These views aim to improve the query response time. For this, they are required to contain relevant information for answering future queries. In this paper, an approach is presented that identifies such relevant information, obtained from previously posed queries on the data warehouse. The approach first identifies subject specific queries and then, from amongst such subject specific queries, frequent queries are selected. These selected frequent queries contain information that has been accessed frequently in the past and therefore has high likelihood of being accessed by future queries. This would result in an improvement in query response time and thereby result in efficient decision making.

Keywords

Subject Area Data Warehouse Query Optimization Dice Coefficient Query Response Time 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • T. V. Vijay Kumar
    • 1
  • Gaurav Dubey
    • 3
  • Archana Singh
    • 2
  1. 1.School of Computer and Systems SciencesJawaharlal Nehru UniversityNew DelhiIndia
  2. 2.Amity School of Computer SciencesNoidaIndia
  3. 3.Amity Institute of Information TechnologyNoidaIndia

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