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Temporal Rule Discovery


Declarative rules, such as functional dependencies, are widely used for cleaning data. 

Several systems take them as input for detecting errors and computing a ``clean" version of the data. To support domain experts,in specifying these rules, several tools have been proposed to profile the data and mine rules. However, existing discovery techniques have traditionally ignored the time dimension. Recurrent events, such as persons reported in locations, have a duration in which they are valid, and this duration should be part of the rules or the cleaning process would simply fail.

In this work, we studied the rule discovery problem for temporal web data. The results were published in PVLDB 2015.

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