度量近似函数依赖：一项比较研究
Measuring Approximate Functional Dependencies: a Comparative Study
AI总结：
本文针对真实数据中的近似函数依赖发现问题，基于 Shannon 熵与逻辑熵形式化比较多种 AFD 度量，并通过结构敏感性和排序有效性实验给出实践选型建议。
摘要：
近似函数依赖（AFD）是在关系中“几乎”成立的函数依赖（FD）。尽管人们已经提出多种度量来量化一个 FD 近似成立的程度，但这些度量难以比较，而且当需要在真实世界数据（即仅近似满足该 FD 的数据）中发现 FD 时，应优先选择哪种度量并不明确。为此，本文对 AFD 度量进行了形式化和定性比较。我们通过以 Shannon 熵和逻辑熵对这些度量进行新颖表述，获得了形式化比较。在定性方面，我们针对输入关系的结构属性进行了敏感性分析，并定量研究了 AFD 度量在真实世界数据上对 AFD 排序的有效性。基于该分析，我们针对实践中应使用的 AFD 度量给出了明确建议。
英文摘要：
Approximate functional dependencies (AFDs) are functional dependencies (FDs) that "almost" hold in a relation. While various measures have been proposed to quantify the level to which an FD holds approximately, they are difficult to compare and it is unclear which measure is preferable when one needs to discover FDs in real-world data, i.e., data that only approximately satisfies the FD. In response, this paper formally and qualitatively compares AFD measures. We obtain a formal comparison through a novel presentation of measures in terms of Shannon and logical entropy. Qualitatively, we perform a sensitivity analysis w.r.t. structural properties of input relations and quantitatively study the effectiveness of AFD measures for ranking AFDs on real world data. Based on this analysis, we give clear recommendations for the AFD measures to use in practice.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
