Ester Livshits ; Benny Kimelfeld - The Shapley Value of Inconsistency Measures for Functional Dependencies

lmcs:8618 - Logical Methods in Computer Science, June 15, 2022, Volume 18, Issue 2 - https://doi.org/10.46298/lmcs-18(2:20)2022
The Shapley Value of Inconsistency Measures for Functional Dependencies

Authors: Ester Livshits ; Benny Kimelfeld

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.


Volume: Volume 18, Issue 2
Published on: June 15, 2022
Accepted on: March 1, 2022
Submitted on: October 28, 2021
Keywords: Computer Science - Databases


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