Approximating a Data Stream for Querying and Estimation: Algorithms and Performance Evaluation

Obtaining fast and good quality approximations to data distributions is a problem of central interest to database management. A variety of popular database applications including, approximate querying, similarity searching and data mining in most applicationdomains, rely on such good quality approximations. Histogram based approximation is a very popular method in database theory and practice to succinctly represent a data distribution in a space efficient manner. In this paper, we place the problem of histogram construction into perspective and we generalize it by raising the requirement of a finite data set and/or known data set size. We consider the case of an infinite data set on which data arrive continuously forming an infinite data stream. In this context, we present the first single pass algorithms capable of constructing histograms of provable good quality. We present algorithms for the fixed window variant of the basic histogram construction problem, supporting incremental m...

• Type: Conference
• Year: 2002
• Where: ICDE
• Authors: Sudipto Guha, Nick Koudas
• Keywords: Data Set Size, Database, Finite Data Set, ICDE 2002, Infinite Data Streams