Patterns Count-Based Labels for Datasets

Yuval Moskovitch, H. V. Jagadish

Abstract: Counts of attribute-value combinations are central to the profiling of a data set, particularly in determining fitness for use and in eliminating bias and unfairness. While counts of individual attribute values may be stored in some data set profiles, there are too many combinations of attributes for it to be practical to store counts for each combination. In this paper, we develop the notion of storing a "label" of limited size that can be used to obtain good estimates for these counts. A label, in this paper, contains information regarding the count of selected attribute-value combinations (which we call "patterns") in the data. We define an estimation function, that uses this label to estimate the count of every pattern. We present the problem of finding the optimal label given a bound on its size and propose a heuristic algorithm for generating optimal labels. We experimentally show the accuracy of count estimates derived from the resulting labels and the efficiency of our algorithm.