Exploiting k-Constraints to Reduce Memory Overhead in Continuous Queries over Data Streams

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.