Maintaining temporal views over non-temporal information sources for data warehousing

  • Jun Yang
  • Jennifer Widom
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1377)

Abstract

An important use of data warehousing is to provide temporal views over the history of source data that may itself be non-temporal. While recent work in view maintenance is applicable to data warehousing, only non-temporal views have been considered. In this paper, we introduce a framework for maintaining temporal views over non-temporal information sources in a data warehousing environment. We describe an architecture for the temporal data warehouse that automatically maintains temporal views over non-temporal source relations, and allows users to ask temporal queries using these views. Because of the dimension of time, a materialized temporal view may need to be updated not only when source relations change, but also as time advances. We present incremental techniques to maintain temporal views for both cases.

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References

  1. 1.
    L. BÆkgaard and L. Mark. Incremental computation of time-varying query expressions. IEEE Trans. on Knowledge & Data Eng., 7(4):583–590, 1995.CrossRefGoogle Scholar
  2. 2.
    J. A. Blakeley, N. Coburn, and P.-å. Larson. Updating derived relations: Detecting irrelevant and autonomously computable updates. ACM Trans. on Database Systems, 14(3):369–400, 1989.CrossRefMathSciNetGoogle Scholar
  3. 3.
    M. H. Böhlen, C. S. Jensen, and R. T. Snodgrass. Evaluating the completeness of TSQL2. In Proc. of the Intl. Workshop on Temporal Databases, pages 153–174, 1995.Google Scholar
  4. 4.
    J. Chomicki and D. Toman. Implementing temporal integrity constraints using an active DBMS. IEEE Trans. on Knowledge & Data Eng., 7(4):566–581, 1995.CrossRefGoogle Scholar
  5. 5.
    J. Clifford, C. E. Dyreson, T. Isakowitz, C. S. Jensen, and R. T. Snodgrass. On the semantics of“NOW” in databases. ACM Trans. on Database Systems, 22(2):171–214, 1997.CrossRefGoogle Scholar
  6. 6.
    T. Griffin and L. Libkin. Incremental maintenance of views with duplicates. In Proc. of the 1995 ACM SIGMOD Intl. Conf. on Management of Data, pages 328–339, 1995.Google Scholar
  7. 7.
    A. Gupta and I. S. Mumick. Maintenance of materialized views: Problems, techniques, and applications. IEEE Data Eng., 18(2):3–18, 1995.Google Scholar
  8. 8.
    H. V. Jagadish, I. S. Mumick, and A. Silberschatz. View maintenance issues for the chronicle data model. In Proc. of the 1995 ACM Symp. on Principles of Database Systems, pages 113–124, 1995.Google Scholar
  9. 9.
    C. S. Jensen and L. Mark. Differential query processing in transaction-time databases. In Temporal Databases: Theory, Design, and Implementation, chapter 19, pages 457–491. Benjamin/Cummings, 1993.Google Scholar
  10. 10.
    C. S. Jensen, M. D. Soo, and R. T. Snodgrass. Unifying temporal models via a conceptual model. Information Systems, 19(7):513–547, 1994.CrossRefGoogle Scholar
  11. 11.
    W. Labio and H. Garcia-Molina. Expiring data from the warehouse. Technical report, Computer Science Dept., Stanford Univ., 1997. http://www-db.stanford.edu/pub/papers/expire.ps.Google Scholar
  12. 12.
    S. B. Navathe and R. Ahmed. A temporal relational model and a query language. Information Sciences, 49(1):147–175, 1989.CrossRefGoogle Scholar
  13. 13.
    G. özsoyoğlu and R. Snodgrass. Temporal and real-time databases: A survey. IEEE Trans. on Knowledge & Data Eng., 7(4):513–532, 1995.CrossRefGoogle Scholar
  14. 14.
    D. Plexousakis. Integrity constraint and rule maintenance in temporal deductive knowledge bases. In Proc. of the 1993 Intl. Conf. on Very Large Data Bases, pages 146–157, 1993.Google Scholar
  15. 15.
    X. Qian and G. Wiederhold. Incremental recomputation of active relational expressions. IEEE Trans. on Knowledge & Data Eng., 3(3):337–341, 1991.CrossRefGoogle Scholar
  16. 16.
    D. Quass. Maintenance expressions for views with aggregation. In Proc. of the ACM Workshop on Materialized Views: Techniques & Applications, pages 110–118, 1996.Google Scholar
  17. 17.
    D. Quass, A. Gupta, I. S. Mumick, and J. Widom. Making views self-maintainable for data warehousing. In Proc. of the 1996 Intl. Conf. on Parallel & Distributed Information Systems, 1996.Google Scholar
  18. 18.
    R. T. Snodgrass. The temporal query language TQuel. ACM Trans. on Database Systems, 12(2):247–298, 1987.CrossRefGoogle Scholar
  19. 19.
    R. T. Snodgrass, S. Gomez, and L. E. McKenzie. Aggregates in the temporal query language TQuel. IEEE Trans. on Knowledge & Data Eng., 5(5):826–842, 1993.CrossRefGoogle Scholar
  20. 20.
    J. Widom. Research problems in data warehousing. In Proc. of the 1995 Intl. Conf. on Information & Knowledge Management, pages 25–30, 1995.Google Scholar
  21. 21.
    J. L. Wiener, H. Gupta, W. J. Labio, Y. Zhuge, H. Garcia-Molina, and J. Widom. A system prototype for warehouse view maintenance. In Proc. of the ACM Workshop on Materialized Views: Techniques & Applications, pages 26–33, 1996.Google Scholar
  22. 22.
    J. Yang and J. Widom. Maintaining temporal views over non-temporal information sources for data warehousing. Technical report, Computer Science Dept., Stanford Univ., 1998. http://www-db.stanford.edu/pub/papers/yw-tempview.ps.Google Scholar

Copyright information

© Springer-Verlag 1998

Authors and Affiliations

  • Jun Yang
    • 1
  • Jennifer Widom
    • 1
  1. 1.Computer Science DepartmentStanford UniversityUSA

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