Compatible parameters for semantic measurement in extended fuzzy relational databases

Julie Yu Chih Liu, Tsu Hao Chang

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

Abstract

Database integration is much more complex in the context of fuzzy databases than in classical databases due to the measurement of the closeness of attribute values. In extended possibility-based fuzzy relational databases, the measurement comprises parameters in addition to attribute values. As the parameters used in databases are different, the closeness of the same pairs of attribute values will not be the same. It leads into the problem in defining the data redundancy consistently after data integration. However, the requirement of employing identical parameters in different databases is too stern to follow. This paper studies the closeness of attribute values varying from parameters of the measurement, and provides a flexible guide to define the parameters in fuzzy databases to be integrated.

Original languageEnglish
Title of host publicationProceedings of the International Conference on Artificial Intelligence IC-AI 2003
EditorsH.R. Arabnia, R. Joshua, Y. Mun, H.R. Arabnia, R. Joshua, Y. Mun
Pages988-993
Number of pages6
Volume2
Publication statusPublished - 2003
Externally publishedYes
EventProceedings of the International Conference on Artificial Intelligence, IC-AI 2003 - Las Vegas, NV, United States
Duration: Jun 23 2003Jun 26 2003

Other

OtherProceedings of the International Conference on Artificial Intelligence, IC-AI 2003
CountryUnited States
CityLas Vegas, NV
Period6/23/036/26/03

Keywords

  • Data integration
  • Fuzzy databases
  • Proximity relations

ASJC Scopus subject areas

  • Artificial Intelligence

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  • Cite this

    Liu, J. Y. C., & Chang, T. H. (2003). Compatible parameters for semantic measurement in extended fuzzy relational databases. In H. R. Arabnia, R. Joshua, Y. Mun, H. R. Arabnia, R. Joshua, & Y. Mun (Eds.), Proceedings of the International Conference on Artificial Intelligence IC-AI 2003 (Vol. 2, pp. 988-993)