Estimating Aggregates over Multiple Sets

Edith Cohen, Haim Kaplan

Abstract: Many datasets, including market basket data, text or hypertext documents, and measurement data collected in different nodes or time periods, are modeled as a collection of sets over a ground set of (weighted) items. We consider the problem of estimating basic aggregates such as the weight or selectivity of a subpopulation of the items. We extend classic summarization techniques based on sampling to this scenario when we have multiple sets and selection predicates based on membership in particular sets.