Efficient Support for Time Series Queries in Data Stream Management Systems

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Abstract

There is much current interest in supporting continuous queries on data streams using generalizations of database query languages, such as SQL. The research challenges faced by this approach include (i) overcoming the expressive power limitations of database languages on data stream applications, and (ii) providing query processing and optimization techniques for the data stream execution environment that is so different from that of traditional databases. In particular, SQL must be extended to support sequence queries on time series, and to overcome the loss of expressive power due to the exclusion of blocking query operators. Furthermore, the query processing techniques of relational databases must be replaced with techniques that optimize execution of time-series queries and the utilization of main memory. The Expressive Stream Language for Time Series (ESL-TS) and its query optimization techniques solve these problems efficiently and are part of the data stream management system prototype developed at UCLA.