Parameter-Free Determination of Distance Thresholds for Metric Distance Constraints

11 years 7 months ago

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—The importance of introducing distance constraints to data dependencies, such as differential dependencies (DDs) [28], has recently been recognized. The metric distance constraints are tolerant to small variations, which enable them apply to wide data quality checking applications, such as detecting data violations. However, the determination of distance thresholds for the metric distance constraints is non-trivial. It often relies on a truth data instance which embeds the distance constraints. To ﬁnd useful distance threshold patterns from data, there are several guidelines of statistical measures to specify, e.g., support, conﬁdence and dependent quality. Unfortunately, given a data instance, users might not have any knowledge about the data distribution, thus it is very challenging to set the right parameters. In this paper, we study the determination of distance thresholds for metric distance constraints, in a parameter-free style. Speciﬁcally, we compute an expected utili...

Shaoxu Song, Lei Chen 0002, Hong Cheng

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