Recommending XMLTable views for XQuery workloads

Iman Elghandour, Ashraf Aboulnaga, Daniel C. Zilio, Calisto Zuzarte

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Physical structures, for example indexes and materialized views, can improve query execution performance by orders of magnitude. Hence, it is important to choose the right configuration of these physical structures for a given database. In this paper, we discuss the types of materialized views that are suitable for an XML database. We then focus on XMLTable materialized views and present a procedure to recommend them given an XML database and a workload of XQuery queries. We have implemented our XMLTable View Advisor in a prototype version based on IBM® DB2® V9.7, which supports both relational and XML data, and we experimentally demonstrate the effectiveness of our advisor's recommendations.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages129-144
Number of pages16
Volume5679 LNCS
DOIs
Publication statusPublished - 6 Nov 2009
Externally publishedYes
Event6th International XML Database Symposium, XSym 2009 - Lyon, France
Duration: 24 Aug 200924 Aug 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5679 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other6th International XML Database Symposium, XSym 2009
CountryFrance
CityLyon
Period24/8/0924/8/09

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ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Elghandour, I., Aboulnaga, A., Zilio, D. C., & Zuzarte, C. (2009). Recommending XMLTable views for XQuery workloads. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5679 LNCS, pp. 129-144). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5679 LNCS). https://doi.org/10.1007/978-3-642-03555-5_11