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MOWS: macro and micro online webview selection

Published: 12 June 2013 Publication History

Abstract

In this paper we present an approach, called MOWS, to select materialized webview in data--intensive websites (DIWS). A webview is a static instance of a dynamic web page. The materialization of webviews consists of storing the results of some requests on the server in order to avoid repetitive data generation from the sources. The aim is to improve the query response time. Our contribution in this work is to apply two steps for the selection of webviews. The first one is executed periodically and it consists of filtering the candidate webviews. It is called macro-selection. The second is executed online and it consists of selecting the materialized webviews. It is called micro-selection. Our experiment results show that our solution is very efficient to improve query response time especially for the websites with high size. For this type of websites, our approach overcomes the existing solutions and it improves their query response times by more than 70%.

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cover image ACM Other conferences
WIMS '13: Proceedings of the 3rd International Conference on Web Intelligence, Mining and Semantics
June 2013
408 pages
ISBN:9781450318501
DOI:10.1145/2479787
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 12 June 2013

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Author Tags

  1. macro-selection
  2. materialization weight
  3. materialized webview
  4. micro-selection
  5. multi-constraint selection
  6. quality of data
  7. quality of service
  8. selection
  9. web-usage mining

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WIMS '13
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  • UAM

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WIMS '13 Paper Acceptance Rate 28 of 72 submissions, 39%;
Overall Acceptance Rate 140 of 278 submissions, 50%

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