Many analysis and monitoring applications require the repeated execution of expensive functions over streams of rapidly changing data. These applications appear in fields as varied as finance, supply chain management, and power utility monitoring.While many of these applications can be expressed declaratively, current continuous query processors are not designed to optimize queries with expensive user-defined functions. Such optimizations are hindered by "black box" function interfaces, where the operator has no control over the processing inside each invocation. We are currently developing VAOs (Variable Accuracy Operators), a new class of operators that allow the query processor to speed up individual function calls. VAOs use a new function interface that exposes the trade-off between work and accuracy inherent inmany functions. Using this new interface, VAOs can eliminate unneeded work by running each function call to only the accuracy needed by the query. VAOs play a key role in our larger research agenda of optimizing queries with expensive functions, and we briefly describe this larger agenda as well.

Many analysis and monitoring applications require the repeated execution of expensive modeling functions over streams of rapidly changing data. These applications can often be expressed declaratively, but the continuous query processors developed to date are not designed to optimize queries with expensive functions. To speed up such queries, we present CASPER: the CAching System for PrEdicate Result ranges. CASPER computes and caches predicate result ranges, which are ranges of stream input values where the system knows the results of expensive predicate evaluations. Over time, CASPER expands ranges so that they are more likely to contain future stream values. This paper presents the CASPER architecture, as well as algorithms for computing and expanding ranges for a large class of predicates. We demonstrate the effectiveness of CASPER using a prototype implementation and a financial application using real bond market data.

This paper describes a tool, called Nodose, we have developed to expedite the creation of robust wrappers. Nodose allows non-programmers to build components that can convert data from the source format to XML or another generic format. Further, the generated code performs a set of statistical checks at runtime that attempt to find extraction errors before they are propogated back to users.