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Available viahttp://dbpubs.stanford.edu/pub/2002-52
Submitted on 10th of November 2002
Author Babu, Shivnath; Srivastava, Utkarsh; Widom, Jennifer
Title Exploiting k-Constraints to Reduce Memory Overhead in Continuous Queries over Data Streams
Date of publication November 2002
Citation Babu, Shivnath; Srivastava, Utkarsh; Widom, Jennifer. Exploiting k-Constraints to Reduce Memory Overhead in Continuous Queries over Data Streams
Number of pages 28
Language English
Project STREAM
Type Technical Report
Subject group Data Streams; Query processing
Abstract Continuous queries often require significant run-time state over arbitrary data streams. However, streams may exhibit certain data or arrival patterns, or constraints, that can be detected and exploited to reduce state considerably without compromising correctness. Rather than requiring constraints to be satisfied precisely, which can be unrealistic in a data streams environment, we introduce k-constraints, where k is an adherence parameter specifying how closely a stream adheres to the constraint. (Smaller k's are closer to strict adherence and offer better memory reduction.) We present a query processing architecture, called k-Mon, that detects useful k-constraints automatically and exploits the constraints to reduce run-time state for a wide range of continuous queries. Experimental results show dramatic state reduction, while only modest computational overhead is incurred for our constraint monitoring and query execution algorithms.
Keywords Data Streams, constraints, query processing
Contact address shivnath@stanford.edu
Sponsored by This work was supported by the National Science Foundation under grants IIS-0118173 and IIS-9817799, and by a Stanford Graduate
Fellowship from Sequoia Capital.
Notes This technical report is an updated version of an earlier technical report of the same name, which appeared originally in November 2002. This version contains new material on constraint monitoring (Sections 1.4, 4.2, 5.2, and 6.2) and adds author Srivastava.
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