Advertisement

Incremental Mining of Schema for Semistructured Data

  • Aoying Zhou
  • Jinwen
  • Zhou Shuigeng
  • Zenping Tian
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1574)

Abstract

Semistructured data is specified by the lack of any fixed and rigid schema, even though typically some implicit structure appears in the data. The huge amounts of on-line applications make it important and imperative to mine schema of semistructured data, both for the users (e.g., to gather useful information and facilitate querying) and for the systems (e.g., to optimize access). The critical problem is to discover the implicit structure in the semistructured data. Current methods in extracting Web data structure are either in a general way independent of application background [8], [9], or bound in some concrete environment such as HTML etc [13], [14], [15]. But both face the burden of expensive cost and difficulty in keeping along with the frequent and complicated variances of Web data. In this paper, we first deal with the problem of incremental mining of schema for semistructured data after the update of the raw data. An algorithm for incrementally mining schema of semistructured data is provided, and some experimental results are also given, which shows that our incremental mining for semistructured data is more efficient than non-incremental mining.

Keywords

Data Mining Incremental Mining Semistructured Data Schema Algorithm 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    U.M Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, Advances in Knowledge Discovery and Data Mining. AAAVMIT Press, 1996.Google Scholar
  2. 2.
    M. S. Chen, J.H. Han, and P. S. Yu, Data Mining: An Overview from a Database Perspective. IEEE Trans. KDE, vo1.8, No.6, pp866–883, December 1996.Google Scholar
  3. 3.
    R. Agrawa1, T. Imielinski,and A. Swami. Mining Association Rules between Sets of Items in Large Databases. In Proc. of the ACM SIGMOD Conference on Management of Data. Washington, D.C.,May 1993.Google Scholar
  4. 4.
    R. Agrawa1, R Srikant. Fast Algorithms for Mining Association Rules. In Proc. of the 20th Int’l Conference on Very Large Databases. Santiago, Chile, Sept., 1994.Google Scholar
  5. 5.
    R. Srikant, R. Agrawa1. Mining Generalized Association Rules. In Proc. of the 21st Int’l Conference on Very Large Databases. Zurich, Switzerland, Sept., 1995.Google Scholar
  6. 6.
    Y. Fu and J. Han. Meta-rule-guided mining of association rules in relational databases. In Proc. of 1st Int’l Workshop on Integration of Knowledge Discovery with Deductive and Object-Oriented Databases (KDOOD’95), pp.39–46, Singapore, Dec., 1995.Google Scholar
  7. 7.
    K. Koperski and J. Han. Discovery of Spatial Association Rules in Geographic Information Databases. In Advances in Spatial Databases, Proceedings of 4’h Symposium, SSD’95. (Aug.6–9, Portland, Maine). Springer-Verlag, BerlinGoogle Scholar
  8. 8.
    S. Nestorov, S. Abitebou1, and R. Motwani, Inferring Structure in Semistructured data. (http://www.cs.stanford.edu/-rajeev)
  9. 9.
    K. Wang, H.Q. Liu, Schema Discovery for Semistructured Data. In Proc. of KDD’97.Google Scholar
  10. 10.
    Y. Papakonstantinow, H. Garcia-Marlia, and J. Widom, Object Exchange Across Heterogeneous Information Sources. In Proc. of ICDE, pp.251–260, Taiwan, March 1995.Google Scholar
  11. 11.
    R. Agrawa1, R Srikant, Fast Algorithms for Mining Association Rules. In Proc. of the 20th Int’l Conference on Very Large Databases, Santiago, Chile, Sept., 1994.Google Scholar
  12. 12.
    D.W. Cheung, J. Han, and C.Y. Wong, Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique, In Proc. of ICDE, New Orleans, LA., Feb., 1996.Google Scholar
  13. 13.
    G.O. Arocena and A.O. Mendelzon. “WebOQL: Restructuring Documents, Databases and Webs”, In Proc. of ICDE, Orlando, Florida, USA, February 1998Google Scholar
  14. 14.
    L. Lakshmanan, F. Sadri, and I. Subramanian. “A Declarative Language for Querying and Restructuring the Web“, In Proc. of 6th Int’l Workshop on Research Issues in Data Engineering, New Orleans, 1996.Google Scholar
  15. 15.
    A.O. Mendelzon, G. Mihaila, and T. Milo. “Querying the World Wide Web”, In Proc. of PDIS’96, Miami, December 1996Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Aoying Zhou
    • 1
  • Jinwen
    • 1
  • Zhou Shuigeng
    • 1
  • Zenping Tian
    • 1
  1. 1.Department of Computer ScienceFudan UniversityP.R.China

Personalised recommendations