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Managing Schemata for Semistructured Databases Using Constraints

  • André Bergholz
  • Johann Christoph Freytag
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1884)

Abstract

Managing semistructured data requires more flexibility than traditional database systems provide. Recently we proposed a query language for semistructured data represented as labeled directed graphs. This language is based on matching a partial schema into the database. In this paper we describe how we achieve this matching using constraints. We show how to match a schema into a database without using any additional information. In order to match schemata more efficiently, we are able to incorporate results of previously matched schemata. To this end, we formulate a sufficient condition for schema containment and describe how to test this condition, again, using constraints. We show how the knowledge of schema containment can be used for optimization. As a theoretical contribution we prove that, under some circumstances, schema matches can be found without any backtracking and in polynomial time.

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References

  1. Abi97.
    S. Abiteboul. Querying semi-structured data. In Proceedings of the International Conference on Database Theory (ICDT), pages 1–18, Delphi, Greece, January 1997.Google Scholar
  2. AMM97.
    P. Atzeni, G. Mecca, and P. Merialdo. To weave the web. In Proceedings of the International Conference on Very Large Databases (VLDB), pages 206–215, Athens, Greece, August 1997.Google Scholar
  3. AQM+97._S. Abiteboul, D. Quass, J. McHugh, J. Widom, and J. Wiener. The Lorel query language for semistructured data. Journal of Digital Libraries, 1(1):68–88, 1997.Google Scholar
  4. ASU79.
    A. Aho, Y. Sagiv, and J. D. Ullman. Equivalence of relational expressions. SIAM Journal on Computing, 8(2):218–246, 1979.zbMATHCrossRefMathSciNetGoogle Scholar
  5. Bar99.
    R. Bartak. Constraint programming: In pursuit of the holy grail. In Proceedings of the Week of Doctoral Students (WDS), Prague, Czech Republic, June 1999.Google Scholar
  6. BDHS96.
    P. Buneman, S. Davidson, G. Hillebrand, and D. Suciu. A query language and optimization techniques for unstructured data. In Proceedings of the ACM SIGMOD International Conference on Management of Data, pages 505–516, Montreal, Canada, June 1996.Google Scholar
  7. BF99.
    A. Bergholz and J. C. Freytag. Querying semistructured data based on schema matching. In Proceedings of the International Workshop on Database Programming Languages (DBPL), Kinloch Rannoch, Scotland, UK, September 1999.Google Scholar
  8. Bun97.
    P. Buneman. Semistructured data. In Proceedings of the Symposium on Principles of Database Systems (PODS), pages 117–121, Tucson, AZ, USA, May 1997.Google Scholar
  9. CK97.
    M. J. Carey and D. Kossmann. On saying “Enough Already!” in SQL. In Proceedings of the ACM SIGMOD International Conference on Management of Data, pages 219–230, Tucson, AZ, USA, May 1997.Google Scholar
  10. Coo71.
    S. A. Cook. The complexity of theorem-proving procedures. In Proceedings of the ACM Symposium on Theory of Computing, pages 151–158, Shaker Heights, OH, USA, May 1971.Google Scholar
  11. Ecl.
    ECLiPSe-The ECRC Constraint Logic Parallel System, http://www.ecrc.de/eclipse/.
  12. FFK+98._M. Fernandez, D. Florescu, J. Kang, A. Levy, and D. Suciu. Catching the boat with Strudel: Experiences with a web-site management system. In Proceedings of the ACM SIGMOD International Conference on Management of Data, pages 414–425, Seattle, WA, USA, June 1998.Google Scholar
  13. FLS98.
    D. Florescu, A. Levy, and D. Suciu. Query containment for conjunctive queries with regular expressions. In Proceedings of the Symposium on Principles of Database Systems (PODS), pages 139–148, Seattle, WA, USA, June 1998.Google Scholar
  14. Fre82.
    E. Freuder. A sufficient condition for backtrack-free search. Journal of the ACM, 29(1):24–32, 1982.zbMATHCrossRefMathSciNetGoogle Scholar
  15. Mac77.
    A. K. Mackworth. Consistency in networks of relations. Artificial Intelligence, 8(1):99–118, 1977.zbMATHCrossRefMathSciNetGoogle Scholar
  16. MW99.
    J. McHugh, and J. Widom. Query optimization for XML. In Proceedings of the International Conference on Very Large Databases (VLDB), pages 315–326, Edinburgh, Scotland, UK, September 1999.Google Scholar
  17. Rud98.
    M. Rudolf. Utilizing constraint satisfaction techniques for efficient graph pattern matching. In Proceedings of the International Workshop on Theory and Application of Graph Transformations (TAGT), Paderborn, Germany, November 1998.Google Scholar
  18. Zue93.
    A. Zuendorf. A heuristic for the subgraph isomorphism problem in executing PROGRES. Technical Report AIB 93-5, RWTH Aachen, Aachen, Germany, 1993.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • André Bergholz
    • 1
  • Johann Christoph Freytag
    • 2
  1. 1.Stanford UniversityStanfordUSA
  2. 2.Humboldt-Universität zu BerlinBerlinGermany

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