Evidential Integration of Semantically Heterogeneous Aggregates in Distributed Databases with Imprecision

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

The mass function of evidential theory provides a means of representing ignorance in lack of information. In this paper we propose mass function models of aggregate views held as summary tables in a distributed database. This model particularly suits statistical databases in which the data usually presents imprecision, including missing values and overlapped categories of aggregate classification. A new aggregation combination operator is developed to accomplish the integration of semantically heterogeneous aggregate views in such distributed databases.
Original languageEnglish
Title of host publicationIntelligent Data Engineering and Automated Learning – IDEAL 2006
PublisherSpringer
Pages961-969
Volume4224/2
ISBN (Print)978-3-540-45485-4
DOIs
Publication statusPublished - 26 Sep 2006

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    Hong, X., McClean, S., Scotney, B., & Morrow, PJ. (2006). Evidential Integration of Semantically Heterogeneous Aggregates in Distributed Databases with Imprecision. In Intelligent Data Engineering and Automated Learning – IDEAL 2006 (Vol. 4224/2, pp. 961-969). Springer. https://doi.org/10.1007/11875581_115