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• W3089767549 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." @default.
• W3089767549 created "2020-10-08" @default.
• W3089767549 creator A5006706357 @default.
• W3089767549 creator A5076186858 @default.
• W3089767549 date "2022-06-15" @default.
• W3089767549 modified "2023-09-30" @default.
• W3089767549 title "The Shapley Value of Inconsistency Measures for Functional Dependencies" @default.
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• W3089767549 doi "https://doi.org/10.46298/lmcs-18(2:20)2022" @default.
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