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arXiv · 2009.13819

The Shapley Value of Inconsistency Measures for Functional Dependencies

Ester Livshits·Benny Kimelfeld
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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.

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Ester Livshits, Benny Kimelfeld. 2020-09-29. The Shapley Value of Inconsistency Measures for Functional Dependencies. https://doi.org/10.46298/lmcs-18(2%3A20)2022

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