Predicate result range caching for continuous queries

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.

[1]  R. Cooper.  Bond Markets , 2019, Derivatives.

[2]  Sunil Prabhakar,et al.  Evaluating probabilistic queries over imprecise data , 2003, SIGMOD '03.

[3]  Jennifer Widom,et al.  Adaptive filters for continuous queries over distributed data streams , 2003, SIGMOD '03.

[4]  R. Motwani,et al.  Query Processing, Approximation, and Resource Management in a Data Stream Management System , 2003, CIDR.

[5]  Frederick Reiss,et al.  TelegraphCQ: Continuous Dataflow Processing for an Uncertain World , 2003, CIDR.

[6]  Michael Stonebraker,et al.  Monitoring Streams - A New Class of Data Management Applications , 2002, VLDB.

[7]  Peter Jaeckel,et al.  Monte Carlo methods in finance , 2002 .

[8]  Eric Fournié,et al.  Monte Carlo Methods in Finance , 2002 .

[9]  L. Clewlow,et al.  Energy Derivatives: Pricing and Risk Management , 2000 .

[10]  Surajit Chaudhuri,et al.  Optimization of queries with user-defined predicates , 1996, TODS.

[11]  Hector Garcia-Molina,et al.  Filtering with Approximate Predicates , 1998, VLDB.

[12]  Les Clewlow,et al.  Implementing derivatives models , 1998 .

[13]  Jeffrey F. Naughton,et al.  Query execution techniques for caching expensive methods , 1996, SIGMOD '96.

[14]  Richard Stanton.  Rational Prepayment and the Valuation of Mortgage-Backed Securities , 1995 .

[15]  Guido Moerkotte,et al.  Optimizing disjunctive queries with expensive predicates , 1994, SIGMOD '94.

[16]  Michael Stonebraker,et al.  Predicate migration: optimizing queries with expensive predicates , 1992, SIGMOD Conference.

[17]  Goetz Graefe,et al.  Query evaluation techniques for large databases , 1993, CSUR.

[18]  Frank J. Fabozzi,et al.  Bond Markets, Analysis and Strategies. , 1989 .

[19]  David Maier,et al.  Indexing in an Object-Oriented DBMS , 1986, OODBS.

[20]  Gerald J. Sussman,et al.  Structure and interpretation of computer programs , 1985, Proceedings of the IEEE.

[21]  John E. Dennis,et al.  Numerical methods for unconstrained optimization and nonlinear equations , 1983, Prentice Hall series in computational mathematics.

[22]  J. Douglas Faires,et al.  Numerical Analysis , 1981 .

[23]  J. Miller.  Numerical Analysis , 1966, Nature.