Adaptive Approximate Querying of Large Sparse Binary Data Sets via Probabilistic Model Averaging

In earlier work we introduced and explored a variety of dierent probabilistic models for the problem of answering selectivity queries posed to large sparse binary data sets. These models can be directly scaled to hundreds or thousands of dimensions, in contrast to other approximate querying techniques (such as histograms or wavelets) that are inherently limited to relatively small numbers of dimensions