Evidential Integration of Semantically Heterogeneous Aggregates in Distributed Databases with Imprecision
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- Hong X., McClean S., Scotney B., Morrow P. (2006) Evidential Integration of Semantically Heterogeneous Aggregates in Distributed Databases with Imprecision. In: Corchado E., Yin H., Botti V., Fyfe C. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2006. IDEAL 2006. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg
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.
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