Many analysis applications in areas such as finance and energy monitoring require the repeated execution of expensive modeling functions over streams of rapidly changing data. These applications can often be expressed declaratively, but current continuous query processing technology does not provide adequate performance in the presence of these expensive functions. For important classes of real-valued functions evaluated in predicates, we use Taylor approximations to determine ranges of stream inputs for which the system knows the outcome of the predicates for potential query results. We show how to integrate this technique into a prototype continuous query processor. We then report on experiments for a financial application using real bond market data. The experiments show that our techniques significantly reduce the number of function calls compared to traditional memoization.
Details
Title
Continuous Query Processing with Real-Valued Functions
Usage Statement
Researchers may make free and open use of the UC Berkeley Library’s digitized public domain materials. However, some materials in our online collections may be protected by U.S. copyright law (Title 17, U.S.C.). Use or reproduction of materials protected by copyright beyond that allowed by fair use (Title 17, U.S.C. § 107) requires permission from the copyright owners. The use or reproduction of some materials may also be restricted by terms of University of California gift or purchase agreements, privacy and publicity rights, or trademark law. Responsibility for determining rights status and permissibility of any use or reproduction rests exclusively with the researcher. To learn more or make inquiries, please see our permissions policies (https://www.lib.berkeley.edu/about/permissions-policies).
