The Shapley Value of Inconsistency Measures for Functional Dependencies
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 tuple...
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| Veröffentlicht in: | Logical methods in computer science Jg. 18, Issue 2 |
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| Hauptverfasser: | , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Logical Methods in Computer Science e.V
15.06.2022
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| Schlagworte: | |
| ISSN: | 1860-5974, 1860-5974 |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | 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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| ISSN: | 1860-5974 1860-5974 |
| DOI: | 10.46298/lmcs-18(2:20)2022 |