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Restoring Consistency in Ontological...
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Restoring Consistency in Ontological Multidimensional Data Models via Weighted Repairs

Abstract

High data quality is a prerequisite for accurate data analysis. However, data inconsistencies often arise in real data, leading to untrusted decision making downstream in the data analysis pipeline. In this paper, we study the problem of inconsistency detection and repair of the Ontology Multi-dimensional Data Model (OMD). We propose a framework of data quality assessment, and repair for the OMD. We formally define a weight-based repair-by-deletion semantics, and present an automatic weight generation mechanism that considers multiple input criteria. Our methods are rooted in multi-criteria decision making that consider the correlation, contrast, and conflict that may exist among multiple criteria, and is often needed in the data cleaning domain.

Authors

Haque E; Chiang F

Volume

159

Pagination

pp. 1085-1094

Publisher

Elsevier

Publication Date

January 1, 2019

DOI

10.1016/j.procs.2019.09.277

Conference proceedings

Procedia Computer Science

ISSN

1877-0509

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