一句话：系统研究函数依赖违反下常见不一致性度量的 Shapley 值计算复杂度，给出可处理与难处理类的完整分类及近似复杂度结果。
论文关注如何量化数据库不一致性，并将整体不一致性公平地归因到单个元组。作者以合作博弈论中的 Shapley 值作为责任分摊机制，针对函数依赖（FD）违反的常见不一致性度量，系统研究其 Shapley 值计算复杂度。对于若干度量，论文将 FD 集合完整分类为 Shapley 值可处理类与不可处理类；在不可处理情形下，进一步分析近似计算的复杂度。结果揭示了不同不一致性度量与 FD 结构对责任归因计算难度的影响，为数据清洗、数据可靠性评估以及元组检查优先级排序提供理论依据。
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.
@article{paperbot1690,
title = {The Shapley Value of Inconsistency Measures for Functional Dependencies},
author = {Ester Livshits and Benny Kimelfeld},
journal = {Logical Methods in Computer Science},
volume = {Volume 18, Issue 2},
year = {2022},
doi = {10.46298/lmcs-18(2:20)2022}
}