ICICLES: Self-Tuning Samples for Approximate Query Answering

Approximate query answering systems provide very fast alternatives to OLAP systems when applications are tolerant to small errors in query answers. Current sampling-based approaches to approximately answer aggregate queries over foreign key joins suffer from the following drawback. All tuples in relations are deemed equally important for answering queries even though, in reality, OLAP queries exhibit locality in their data access. Consequently, they may waste precious real estate by sampling tuples that are not required at all or required very rarely. In this paper, we introduce icicles, a new class of samples that tune themselves to a dynamic workload. Intuitively, the probability of a tuple being present in an icicle is proportional to its importance for answering queries in the workload. Therefore, an icicle consists of more tuples from a subset of the relation that is required to answer more queries in the workload. Consequently, the accuracy of approximate answers obtained by using icicles is better than a static uniform random sample. We show, analytically, that for a certain class of queries reflected by the workload, icicles yield more accurate answers. In a detailed experimental study, we examine the validity and performance of icicles. Supported by a Microsoft Graduate Fellowship. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Very Large Data Base Endowment. To copy otherwise, or to republish, requires a fee and/or special permission from the Endowment. Proceedings of the 26th VLDB Conference, Cairo, Egypt, 2000.