Resolving attribute incompatibility in database integration: an evidential reasoning approach

Ee Peng Lim, Jaideep Srivastava, Shashi Shekhar

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

30 Citations (Scopus)

Abstract

Resolving domain incompatibility among independently developed databases often involves uncertain information. DeMichiel [5] showed that uncertain information can be generated by the mapping of conflicting attributes to a common domain, based on some domain knowledge. In this paper, we show that uncertain information can also arise when the database integration process requires information not directly represented in the component databases, but can be obtained through some summary of data. We therefore propose an extended relational model based on Dempster-Shafer theory of evidence [14] to incorporate such uncertain knowledge about the source databases. We also develop a full set of extended relational operations over the extended relations. In particular, an extended union operation has been formalized to combine two extended relations using Dempster's rule of combination. The closure and boundedness properties of our proposed extended operations are formulated.

Original languageEnglish
Title of host publicationProceedings - International Conference on Data Engineering
Editors Anon
PublisherPubl by IEEE
Pages154-162
Number of pages9
ISBN (Print)0818654007
Publication statusPublished - 1 Jan 1994
EventProceedings of the 10th International Conference on Data Engineering - Houston, TX, USA
Duration: 14 Feb 199418 Feb 1994

Publication series

NameProceedings - International Conference on Data Engineering

Other

OtherProceedings of the 10th International Conference on Data Engineering
CityHouston, TX, USA
Period14/2/9418/2/94

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems

Cite this

Lim, E. P., Srivastava, J., & Shekhar, S. (1994). Resolving attribute incompatibility in database integration: an evidential reasoning approach. In Anon (Ed.), Proceedings - International Conference on Data Engineering (pp. 154-162). (Proceedings - International Conference on Data Engineering). Publ by IEEE.