On-Demand Index for Efficient Structural Joins

  • Kun-Lung Wu
  • Shyh-Kwei Chen
  • Philip S. Yu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4016)

Abstract

A structural join finds all occurrences of structural, or containment, relationship between two sets of XML node elements: ancestor and descendant. Prior approaches to structural joins mostly focus on maintaining offline indexes on disks or requiring the elements in both sets to be sorted. However, either one can be expensive. More important, not all node elements are beforehand indexed or sorted. We present an on-demand, in-memory indexing approach to performing structural joins. There is no need to sort the elements. We discover that there are similarities between the problems of structural joins and stabbing queries. However, previous work on stabbing queries, although efficient in search time, is not directly applicable to structural joins because of high storage costs. We develop two storage reduction techniques to alleviate the problem of high storage costs. Simulations show that our new method outperforms prior approaches.

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References

  1. 1.
    Al-Khlifa, S., Jagadish, H.V., Koudas, N., Patel, J.M., Srivastava, D., Wu, Y.: Structural joins: A primitive for efficient XML query pattern matching. In: Proc.of IEEE ICDE (2002)Google Scholar
  2. 2.
    Bruno, N., Koudas, N., Srivastava, D.: Holistic twig joins: Optimal XML patternmatching. In: Proc. of ACM SIGMOD (2002)Google Scholar
  3. 3.
    Chien, S.-Y., Vagena, Z., Zhang, D., Tsotras, V.J., Zaniolo, C.: Efficient structural joins on indexed XML documents. In: Proc. of VLDB (2002)Google Scholar
  4. 4.
    Dietz, P.F., Sleator, D.D.: Two algorithms for maintaining order in a list. In: Proc. of ACM Conf. on Theory of Computing (1987)Google Scholar
  5. 5.
    Grust, T., van Keulen, M., Teubner, J.: Staircase join: Teach a relational DBMSto watch its (axis) steps. In: Proc. of VLDB (2003)Google Scholar
  6. 6.
    Jiang, H., Lu, H., Wang, W., Ooi, B.C.: XR-Tree: Indexing XML data for efficient structural joins. In: Proc. of IEEE ICDE (2003)Google Scholar
  7. 7.
    Jiang, H., Wang, W., Lu, H., Yu, J.: Holistic twig join on indexed XML documents.In: Proc. of VLDB (2003)Google Scholar
  8. 8.
    Li, Q., Moon, B.: Indexing and querying XML data for regular path expressions. In: Proc. of VLDB (2001)Google Scholar
  9. 9.
    McHugh, J., Widom, J.: Query optimization for XML. In: Proc. of VLDB (1999)Google Scholar
  10. 10.
    XML Data Repository. Dept. of Computer Science and Engineering, University of Washington, http://www.cs.washington.edu/research/xmldatasets
  11. 11.
    Samet, H.: Design and Analysis of Spatial Data Structures. Addison-Wesley, Reading (1990)Google Scholar
  12. 12.
    Vagena, Z., Moro, M.M., Tsotras, V.J.: Efficient processing of XML containmentqueries using partition-based schemes. In: Proc. of IDEAS (2004)Google Scholar
  13. 13.
    Wang, W., Jiang, H., Lu, H., Yu, J.X.: PBi Tree coding and efficient processing of containment joins. In: Proc. of IEEE ICDE (2003)Google Scholar
  14. 14.
    Wu, K.-L., Chen, S.-K., Yu, P.S.: Query indexing with containment-encoded intervals for efficient stream processing. Knowledge and Information Systems 9(1), 62–90 (2006)CrossRefGoogle Scholar
  15. 15.
    Zhang, C., Naughton, J., DeWitt, D., Luo, Q., Lohman, G.: On supporting containment queries in Relational database management systems. In: Proc. of ACM SIGMOD (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Kun-Lung Wu
    • 1
  • Shyh-Kwei Chen
    • 1
  • Philip S. Yu
    • 1
  1. 1.IBM T.J. Watson Research Center 

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