Aggregate Operations in the Information Source Tracking Method

  • Fereidoon Sadri
Conference paper
Part of the Workshops in Computing book series (WORKSHOPS COMP.)


The Information Source Tracking method, IST, is an approach to the management of uncertain and imprecise data in database systems. In this paper we study the processing of queries involving aggregate operations in the IST method. The problems discussed include producing all possible outcomes of an aggregate query and their probabilities, and determining the probability of a specific outcome. We present algorithms for the evaluation of aggregate queries, and show that some of these problems are intractable.


Information Source Query Processing Truth Assignment Disjunctive Normal Form Extended Relation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    J. Biskup, A Foundation of Codd’s Relational Maybe Operations, ACM Transactions on Database Systems, 8(4), 1983, 608–636.CrossRefMATHMathSciNetGoogle Scholar
  2. 2.
    S.A. Cook, The Complexity of Theorem Proving Procedures, Proc. 3rd ACM Symp.on Theory of Computing, 1971,151–158.Google Scholar
  3. 3.
    L. DeMichiel, Resolving Database Incompatibility:An Approach to Performing Operations over Mismatched Domains, IEEE Trans, on Knowledge and Data Engineering, 1(4), December 1989, 485–493.CrossRefGoogle Scholar
  4. 4.
    B. Doyon, Reliability of Answers to an SQL Query, Project Report, Department of Computer Science, Concordia University, May 1990.Google Scholar
  5. 5.
    M.R. Garey and D.S. Johnson, Computers and Intractability, A Guide to the Theory of NP-Completeness, Freeman Press, 1979.MATHGoogle Scholar
  6. 6.
    A.M. Keller and M. Winslett-Wilkins, On the Use of an Extended Relational Model to Handle Changing Incomplete Information, IEEE Trans. on Software Engineering, SE-11:7, July 1985, 620–633.CrossRefGoogle Scholar
  7. 7.
    K-C. Liu and R. Sunderraman, Indefinite and Maybe Information in Relational Databases, ACM Trans. on Database Systems, 15(1), March 1990, 1–39.CrossRefMathSciNetGoogle Scholar
  8. 8.
    K-C. Liu and R. Sunderraman, A Generalized Relational Model for Indefinite and Maybe Information, IEEE Trans. on Knowledge and Data Engineering, 3(1), March 1991, 65–77.CrossRefGoogle Scholar
  9. 9.
    D. Maier, The Theory of Relational Databases, Computer Science Press, 1983.MATHGoogle Scholar
  10. 10.
    Nils J. Nilsson, Probabilistic Logic, Artificial Intelligence, 28 (1986), 71–87.CrossRefMATHMathSciNetGoogle Scholar
  11. 11.
    F. Sadri, Reliability of Answers to Queries in Relational Databases, IEEE Trans. on Knowledge and Data Engineering, 3(2), June 1991, 245–251.CrossRefGoogle Scholar
  12. 12.
    F. Sadri, Modeling Uncertainty in Databases, IEEE Int. Conference on Data Engineering, May 1991, 122–131.Google Scholar
  13. 13.
    F. Sadri, Integrity Constraints in the Information Source Tracking Method, IEEE Trans. on Knowledge and Data Engineering, to appear.Google Scholar
  14. 14.
    F. Sadri, Information Source Tracking, Manuscript, September 1991. Submitted for publication.Google Scholar

Copyright information

© British Computer Society 1993

Authors and Affiliations

  • Fereidoon Sadri
    • 1
  1. 1.Department of Computer ScienceConcordia UniversityMontrealCanada

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