Efficient policy-based inconsistency management in relational knowledge bases

Maria Vanina Martinez, Francesco Parisi, Andrea Pugliese, Gerardo I. Simari, V. S. Subrahmanian

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


Real-world databases are frequently inconsistent. Even though the users who work with a body of data are far more familiar not only with that data, but also their own job and the risks they are willing to take and the inferences they are willing to make from inconsistent data, most DBMSs force them to use the policy embedded in the DBMS. Inconsistency management policies (IMPs) were introduced so that users can apply policies that they deem are appropriate for data they know and understand better than anyone else. In this paper, we develop an efficient "cluster table" method to implement IMPs and show that using cluster tables instead of a standard DBMS index is far more efficient when less than about 3% of a table is involved in an inconsistency (which is hopefully the case in most real world DBs), while standard DBMS indexes perform better when the amount of inconsistency in a database is over 3%.

Original languageEnglish (US)
Title of host publicationScalable Uncertainty Management - 4th International Conference, SUM 2010, Proceedings
Number of pages14
StatePublished - 2010
Externally publishedYes
Event4th International Conference on Scalable Uncertainty Management, SUM 2010 - Toulouse, France
Duration: Sep 27 2010Sep 29 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6379 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference4th International Conference on Scalable Uncertainty Management, SUM 2010

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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