Eris：衡量多维数据源之间的不一致性
Eris: Measuring discord among multidimensional data sources
AI总结：
本文针对多数据源集成中无法确定真实值时的一致性评估问题，提出 discordance 度量、代数算子及基于线性/二次规划的关系数据库实现，并在 COVID-19 和合成数据上验证了其高效性。
摘要：
数据集成是数据库中的一个经典问题，通常分解为模式匹配、实体匹配和数据融合。为了解决数据融合问题，大多假设可以确定 ground truth（真实值）。然而，一般情况下，不同数据源中的数据采集过程并不完美，无法提供准确的值合并。因此，在缺乏确定 ground truth 的方法时，至少量化一个数据集距离内部一致有多远是很重要的。为此，我们提出了一致数据的定义，并定义了一个 discordance（不一致性）度量，作为一种衡量分歧的方法，以改进基于可信度的决策。我们定义了数值属性的 discord 测量问题：给定一组不确定的原始观测值或聚合结果（例如与 COVID-19 相关的病例/住院/死亡数据），以及关于同一现实的不同概念化（例如粒度或单位）对齐的信息，我们希望评估不同数据源是否一致，如果不一致，则使用 discordance 度量来量化它们的不一致程度。我们还定义了一组代数算子，用于描述不同数据源的对齐，并具有正确性保证，同时给出了两种可选的关系数据库实现，将问题归约为线性规划或二次规划。我们在 COVID-19 数据和合成数据上对这些方法进行了评估，实验结果表明，在现实场景中可以高效地进行不一致性测量。
英文摘要：
Data integration is a classical problem in databases, typically decomposed into schema matching, entity matching and data fusion. To solve the latter, it is mostly assumed that ground truth can be determined. However, in general, the data gathering processes in the different sources are imperfect and cannot provide an accurate merging of values. Thus, in the absence of ways to determine ground truth, it is important to at least quantify how far from being internally consistent a dataset is. Hence, we propose definitions of concordant data and define a discordance metric as a way of measuring disagreement to improve decision making based on trustworthiness. We define the discord measurement problem of numerical attributes in which given a set of uncertain raw observations or aggregate results (such as case/hospitalization/death data relevant to COVID-19) and information on the alignment of different conceptualizations of the same reality (e.g., granularities or units), we wish to assess whether the different sources are concordant, or if not, use the discordance metric to quantify how discordant they are. We also define a set of algebraic operators to describe the alignments of different data sources with correctness guarantees, together with two alternative relational database implementations that reduce the problem to linear or quadratic programming. These are evaluated against both COVID-19 and synthetic data, and our experimental results show that discordance measurement can be performed efficiently in realistic situations.
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