skip to main content
10.1145/1967486.1967559acmotherconferencesArticle/Chapter ViewAbstractPublication PagesiiwasConference Proceedingsconference-collections
research-article

Web usage mining for the recommendation of materialized webviews

Published: 08 November 2010 Publication History

Abstract

In this paper, we propose an approach, which is based on web usage mining techniques, to recommend webviews to be materialized. The webview materialization is a term used to represent the transformation of dynamic web data into equivalent static web data. That is the creation of a static instance of a dynamic web page, at a certain point in time. In this work we will extend our previous approach [6] which concerns the use of sequential patterns to recommend the materialized webviews. Firstly we analyze the DIWS historic to extract the frequent sequential patterns and the frequent association rules. Then we use these repetitive behaviors to calculate a materialization weight for each webview. The webviews with high materialization weights are the most recommended for the materialization. Our experiment results show that our approach reduces the materialization risk more than those using only the recent time period to select the materialized webviews.

References

[1]
Agrawal, R. and Skirant, R. 1994. Fast algorithms for mining association rules. In Proceedings of the 20th Intl. Conference on Very Large Databases, Santiago, Chile, pages 478_499, June 1994.
[2]
Agrawal, R. and Skirant, R. 1995. Mining sequential patterns. In Proceedings of the 11th international conference on data engineering (ICDE'95). 1995
[3]
Aouiche, K. and Darmont, J. 2009. Data mining-based materialized view and index selection in data warehouses. J. Intell. Inf. Syst. 33(1): 65--93 (2009)
[4]
Aouiche, K., Jouve, P. and Darmont, J. 2006. Clustering-Based Materialized View Selection in Data Warehouses. In 10th East-European Conference on Advances in Databases and Information Systems (ADBIS 2006), Thessaloniki, Greece, Vol. 4152 of LNCS, pp. 81--95. 2006
[5]
Ben Ammar, A., Abdellatif, A. and Ben Ghezala, H. 2006. Forms of Data Materialization in Data-Intensive Web Sites. IJCSNS International Journal of Computer Science and Network Security, VOL. 6 84 No. 12, December 2006
[6]
Ben Ammar, A., Badis, M. and Abdellatif, A. 2010. Motifs Séquentiels pour la Sélection des Webiews à Matérialiser. In 6èmes Journées francophones sur les Entrepôts de Données et l'Analyse en ligne Djerba 11--13 Juin 2010.
[7]
Labrinidis, A. and Roussopoulos, N. 2001. Adaptive webview materialization. In the Fourth International Workshop on the Web and Databases, held in conjunction with ACM SIGMOD, 2001.
[8]
Labrinidis, A. and Roussopoulos, N. 1999. On the materialization of webviews. In Proc. of the ACM SIGMOD Workshop on the Web and Databases WebDB 99, pages 79--84, 1999.
[9]
Labrinidis, A. and Roussopoulos, N. 2002. Online View Selection for the Web. In Proc. Of the ACM SIGMOD Conference, 2002.
[10]
Labrinidis, A. and Roussopoulos, N. 2000. WebView Materialization. Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data. pp. 367--378. May 14--19, 2000, Dallas, Texas, United States
[11]
Saidi, S., Slimani, Y. and Arour, K. 2007. Webview selection from user access Patterns. In PIKM '07, November 9, 2007, Lisboa, Portugal.
[12]
TPC-W Benchmark Specification. http://www.tpc.org/tpcw
[13]
Tsay, Y. J. and Chiang, J. Y. 2005. CBAR: an efficient method for mining association rules. Knowledge-Based Systems 18 (2005) 99--105.
[14]
Zhang, Y. and Qin, X. 2005. State transfer graph: An efficient tool for webview maintenance. In In the proceedings of WAIM2005 In Hangzhou, China, pages 513_525, 2005

Cited By

View all

Index Terms

  1. Web usage mining for the recommendation of materialized webviews
        Index terms have been assigned to the content through auto-classification.

        Recommendations

        Comments

        Information & Contributors

        Information

        Published In

        cover image ACM Other conferences
        iiWAS '10: Proceedings of the 12th International Conference on Information Integration and Web-based Applications & Services
        November 2010
        895 pages
        ISBN:9781450304214
        DOI:10.1145/1967486
        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]

        Sponsors

        • IIWAS: International Organization for Information Integration
        • Web-b: Web-b

        In-Cooperation

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        Published: 08 November 2010

        Permissions

        Request permissions for this article.

        Check for updates

        Author Tags

        1. association rules
        2. materialization weight
        3. materialized webview
        4. recommendation
        5. selection
        6. sequential patterns
        7. web usage mining

        Qualifiers

        • Research-article

        Conference

        iiWAS '10
        Sponsor:
        • IIWAS
        • Web-b

        Contributors

        Other Metrics

        Bibliometrics & Citations

        Bibliometrics

        Article Metrics

        • 0
          Total Citations
        • 132
          Total Downloads
        • Downloads (Last 12 months)0
        • Downloads (Last 6 weeks)0
        Reflects downloads up to 13 Feb 2025

        Other Metrics

        Citations

        Cited By

        View all

        View Options

        Login options

        View options

        PDF

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader

        Figures

        Tables

        Media

        Share

        Share

        Share this Publication link

        Share on social media