Integration of proximity relations under consistency constraint

Yu Chih Liu, Tsu Hao Chang, Jun Lin Lin

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

Abstract

Though the data integration problem of relational databases has been well studied, little work addresses the problem In the context of fuzzy relational databases. The fuzzy relational model relies on the specification of either a similarity relation or a proximity relation for each scalar domain in the fuzzy database. Without the constraint of max-min transitivity, proximity relations can be defined in an intuitive way. This paper states a resolution in integrating multiple proximity relations. First, the closure property of weighted combination of multiple proximity relations is shown. Then, the notion of consistency constraint is defined. It has been proved that the weighted combination of proximity relations under such constrain Is still consistent with each of the proximity relations prior to their integration.

Original languageEnglish
Title of host publicationProceedings of the International Conference on Information and Knowledge Engineering
EditorsN. Goharian, N. Goharian
Pages182-185
Number of pages4
Volume1
Publication statusPublished - 2003
Externally publishedYes
EventProceedings of the International Conference on Information and Knowledge Engineering 2003 - Las Vegas, NV, United States
Duration: Jun 23 2003 → Jun 26 2003

Other

OtherProceedings of the International Conference on Information and Knowledge Engineering 2003
CountryUnited States
CityLas Vegas, NV
Period6/23/03 → 6/26/03

Keywords

  • Data integration
  • Fuzzy databases
  • Proximity relations
  • Similarity relations

ASJC Scopus subject areas

  • Library and Information Sciences
  • Information Systems

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    Liu, Y. C., Chang, T. H., & Lin, J. L. (2003). Integration of proximity relations under consistency constraint. In N. Goharian, & N. Goharian (Eds.), Proceedings of the International Conference on Information and Knowledge Engineering (Vol. 1, pp. 182-185)