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 -
The Shapley Value of Inconsistency Measures for Functional DependenciesArticle

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

    2 Documents citing this article

    Consultation statistics

    This page has been seen 1667 times.
    This article's PDF has been downloaded 542 times.