Evidential Integration of Semantically Heterogeneous Aggregates in Distributed Databases with Imprecision

Xin Hong; S McClean; Bryan Scotney; PJ Morrow

doi:10.1007/11875581_115

Evidential Integration of Semantically Heterogeneous Aggregates in Distributed Databases with Imprecision

Xin Hong, S McClean, Bryan Scotney, PJ Morrow

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

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 language	English
Title of host publication	Intelligent Data Engineering and Automated Learning – IDEAL 2006
Publisher	Springer
Pages	961-969
Volume	4224/2
ISBN (Print)	978-3-540-45485-4
DOIs	https://doi.org/10.1007/11875581_115
Publication status	Published (in print/issue) - 26 Sept 2006

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title = "Evidential Integration of Semantically Heterogeneous Aggregates in Distributed Databases with Imprecision",

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.",

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AB - 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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BT - Intelligent Data Engineering and Automated Learning – IDEAL 2006

PB - Springer

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Evidential Integration of Semantically Heterogeneous Aggregates in Distributed Databases with Imprecision

Abstract

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