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

Xu Jiawei; Zadorozhny Vladimir; Grant John

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

机译：IncompFuse：具有不准确的数据源的历史信息融合的逻辑框架

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摘要

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.

机译：我们提出了一种新颖的框架，称为IncompFuse，显着提高了从不准确的历史报告重建聚合历史数据的现有方法的准确性。使用历史报告的不兼容概率，IncompFuse支持高效的数据可靠性评估。我们提供了一种系统方法，可根据报告之间的数据和关系的属性来定义此概率。我们的实验研究表明了所提出的框架的高效用。特别是，我们能够以非常高的检测准确度检测嘈杂的历史报告。

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来源
《Journal of Intelligent Information Systems》 |2020年第3期|463-481|共19页
作者
Xu Jiawei; Zadorozhny Vladimir; Grant John;
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作者单位

Univ Pittsburgh Sch Comp & Informat Dept Informat & Networked Syst Pittsburgh PA 15260 USA;

Univ Pittsburgh Sch Comp & Informat Dept Informat & Networked Syst Pittsburgh PA 15260 USA;

Univ Maryland Dept Comp Sci College Pk MD 20742 USA|Univ Maryland UMIACS College Pk MD 20742 USA;

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原文格式 PDF
正文语种 eng
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关键词
Inaccurate data sources; Incompatibility probability; Error detection;

机译：数据源不准确;不相容概率;错误检测;

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

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