Condensed Representation of Database Repairs for Consistent Query Answering

  • Jef Wijsen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2572)


Repairing a database means bringing the database in accordance with a given set of integrity constraints by applying modifications that are as small as possible. In the seminal work of Arenas et al. on query answering in the presence of inconsistency, the possible modifications considered are deletions and insertions of tuples. Unlike earlier work, we also allow tuple updates as a repair primitive. Update-based repairing is advantageous, because it allows rectifying an error within a tuple without deleting the tuple, thereby preserving other consistent values in the tuple. At the center of the paper is the problem of query answering in the presence of inconsistency relative to this refined repair notion. Given a query, a trustable answer is obtained by intersecting the query answers on all repaired versions of the database. The problem arising is that, in general, a database can be repaired in infinitely many ways. A positive result is that for conjunctive queries and full dependencies, there exists a condensed representation of all repairs that permits computing trustable query answers.


Integrity Constraint Inductive Logic Programming Conjunctive Query Query Answer Query Answering 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    S. Abiteboul, R. Hull, and V. Vianu. Foundations of Databases. Addison-Wesley, 1995.Google Scholar
  2. 2.
    M. Arenas, L. Bertossi, and M. Kifer. Applications of Annotated Predicate Calculus to Querying Inconsistent Databases. In Proc. 1st Int. Conf. on Computational Logic (CL 2000), volume 1861 of LNAI, pages 926–941. Springer, 2000.Google Scholar
  3. 3.
    M. Arenas, L. E. Bertossi, and J. Chomicki. Consistent query answers in inconsistent databases. In Proc. 18th ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, pages 68–79. ACM Press, 1999.Google Scholar
  4. 4.
    M. Arenas, L. E. Bertossi, and J. Chomicki. Specifying and querying database repairs using logic programs with exceptions. In Proc. 4th Int. Conf. on Flexible Query Answering Systems (FQAS 2000), Advances in Soft Computing, pages 27–41. Springer, 2000.Google Scholar
  5. 5.
    M. Arenas, L. E. Bertossi, and J. Chomicki. Scalar aggregation in FD-inconsistent databases. In Proc. 8th Int. Conf. on Database Theory (ICDT 2001), volume 1973 of LNCS, pages 39–53. Springer, 2001.Google Scholar
  6. 6.
    C. Beeri and M. Y. Vardi. A proof procedure for data dependencies. Journal of the ACM, 31(4):718–741, Oct. 1984.zbMATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    L. Bertossi and C. Schwind. Analytic tableaux and database repairs: Foundations. In Proc. 2nd Int. Symposium on Foundations of Information and Knowledge Systems (FoIKS 2002), volume 2284 of LNCS, pages 32–48. Springer, 2002.Google Scholar
  8. 8.
    F. Bry. Query answering in information systems with integrity constraints. In First IFIP WG 11.5 Working Conference on Integrity and Internal Control in Information Systems: Increasing the Confidence in Information Systems, Zurich, Switzerland, December 4–5, 1997, pages 113–130. Chapman Hall, 1997.Google Scholar
  9. 9.
    A. Calí, D. Calvanese, G. D. Giacomo, and M. Lenzerini. Data integration under integrity constraints. In Proc. 14th Int. Conf. on Advanced Information Systems Engineering (CAiSE 2002), volume 2348 of LNCS, pages 262–279. Springer, 2002.Google Scholar
  10. 10.
    G. Greco, S. Greco, and E. Zumpano. A logic programming approach to the integration, repairing and querying of inconsistent databases. In Proc. 17th Int. Conf. on Logic Programming (ICLP 2001), volume 2237 of LNCS, pages 348–364. Springer, 2001.Google Scholar
  11. 11.
    G. Greco, S. Greco, and E. Zumpano. A logical framework for querying and repairing inconsistent databases. IEEE Trans. on Knowledge and Data Engineering, to appear.Google Scholar
  12. 12.
    D. Lembo, M. Lenzerini, and R. Rosati. Source inconsistency and incompleteness in data integration. In Proc. 9th Int. Workshop on Knowledge Representation meets Databases (KRDB 2002), number 54 in CEUR Workshop Proceedings, 2002.Google Scholar
  13. 13.
    J. Lin and A. O. Mendelzon. Merging databases under constraints. International Journal of Cooperative Information Systems, 7(1):55–76, 1998.CrossRefGoogle Scholar
  14. 14.
    S.-H. Nienhuys-Cheng and R. de Wolf. The subsumption theorem in inductive logic programming: Facts and fallacies. In L. D. Raedt, editor, Advances in Inductive Logic Programming, pages 265–276. IOS Press, 1996.Google Scholar
  15. 15.
    G. D. Plotkin. A note on inductive generalization. In B. Meltzer and D. Michie, editors, Machine Intelligence 5, pages 153–163, Edinburgh, 1969. Edinburgh University Press.Google Scholar
  16. 16.
    P. R. J. van der Laag and S.-H. Nienhuys-Cheng. Completeness and properness of refinement operators in inductive logic programming. Journal of Logic Programming, 34(3):201–225, 1998.zbMATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Jef Wijsen
    • 1
  1. 1.Université de Mons-Hainaut (UMH), Institut d’InformatiqueMonsBelgium

Personalised recommendations