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Journal of Intelligent Information Systems

Erschienen in:

20.06.2019

IncompFuse: a logical framework for historical information fusion with inaccurate data sources

verfasst von: Jiawei Xu, Vladimir Zadorozhny, John Grant

Erschienen in: Journal of Intelligent Information Systems | Ausgabe 3/2020

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Abstract

We propose a novel framework, called IncompFuse, that significantly improves the accuracy of existing methods for reconstructing aggregated historical data from inaccurate historical reports. IncompFuse supports efficient data reliability assessment using the incompatibility probability of historical reports. We provide a systematic approach to define this probability based on properties of the data and relationships between the reports. Our experimental study demonstrates high utility of the proposed framework. In particular, we were able to detect noisy historical reports with very high detection accuracy.

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Titel: IncompFuse: a logical framework for historical information fusion with inaccurate data sources
verfasst von: Jiawei Xu
Vladimir Zadorozhny
John Grant
Publikationsdatum: 20.06.2019
Verlag: Springer US
Erschienen in: Journal of Intelligent Information Systems / Ausgabe 3/2020
Print ISSN: 0925-9902
Elektronische ISSN: 1573-7675
DOI: https://doi.org/10.1007/s10844-019-00569-6

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