A stratified approach to progressive approximate joins

Abstract

Users often do not require a complete answer to their query but rather only a sample. They expect the sample to be either the largest possible or the most representative (or both) given the resources available. We call the query processing techniques that deliver such results 'approximate'. Processing of queries to streams of data is said to be 'progressive' when it can continuously produce results as data arrives. In this paper, we are interested in the progressive and approximate processing of queries to data streams when processing is limited to main memory. In particular, we study one of the main building blocks of such processing: the progressive approximate join. We devise and present several novel progressive approximate join algorithms. We empirically evaluate the performance of our algorithms and compare them with algorithms based on existing techniques. In particular we study the trade-off between maximization of throughput and maximization of representativeness of the sample. Copyright 2008 ACM.