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The Shapley Value of Inconsistency Measures for Functional Dependencies

Authors :
Ester Livshits
Benny Kimelfeld
Source :
Logical Methods in Computer Science, Vol Volume 18, Issue 2 (2022)
Publication Year :
2022
Publisher :
Logical Methods in Computer Science e.V., 2022.

Abstract

Quantifying the inconsistency of a database is motivated by various goals including reliability estimation for new datasets and progress indication in data cleaning. Another goal is to attribute to individual tuples a level of responsibility to the overall inconsistency, and thereby prioritize tuples in the explanation or inspection of dirt. Therefore, inconsistency quantification and attribution have been a subject of much research in Knowledge Representation and, more recently, in Databases. As in many other fields, a conventional responsibility sharing mechanism is the Shapley value from cooperative game theory. In this paper, we carry out a systematic investigation of the complexity of the Shapley value in common inconsistency measures for functional-dependency (FD) violations. For several measures we establish a full classification of the FD sets into tractable and intractable classes with respect to Shapley-value computation. We also study the complexity of approximation in intractable cases.

Details

Language :
English
ISSN :
18605974
Volume :
ume 18, Issue 2
Database :
Directory of Open Access Journals
Journal :
Logical Methods in Computer Science
Publication Type :
Academic Journal
Accession number :
edsdoj.f86801d66b3241bebdaade750235f4c3
Document Type :
article
Full Text :
https://doi.org/10.46298/lmcs-18(2:20)2022