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Available viahttp://dbpubs.stanford.edu/pub/2003-69
Submitted on 5th of November 2003
Author Babu, Shivnath; Motwani, Rajeev; Munagala, Kamesh; Nishizawa, Itaru; Widom, Jennifer
Title Adaptive Ordering of Pipelined Stream Filters
Date of publication 2003
Published in Proc. of ACM Intl. Conference on Management of Data (SIGMOD), 2004
Citation Babu, Shivnath; Motwani, Rajeev; Munagala, Kamesh; Nishizawa, Itaru; Widom, Jennifer. Adaptive Ordering of Pipelined Stream Filters, Proc. of ACM Intl. Conference on Management of Data (SIGMOD), 2004
Number of pages 13
Language English
Project STREAM
Type Technical Report
Subject group Data Streams; Query processing; Miscellaneous
Abstract We consider the problem of pipelined filters, where a continuous stream of elements is processed by a set of commutative filters. Pipelined filters are common in stream applications and capture a large class of multiway stream joins. We focus on the problem of ordering the filters adaptively to minimize processing cost in an environment where stream and filter characteristics vary unpredictably over time. Our core algorithm, A-Greedy (for Adaptive Greedy), has strong theoretical guarantees: If stream and filter characteristics were to stabilize, A-Greedy would converge to an ordering within a small constant factor of optimal. (In experiments A-Greedy usually converges to the optimal ordering.) One very important feature of A-Greedy is that it monitors and responds to selectivities that are correlated across filters (i.e., that are nonindependent), which provides the strong quality guarantee but incurs run-time overhead. We identify and study a three-way tradeoff among provable convergence to good orderings, run-time overhead, and speed of adaptivity. We develop a suite of variants of A-Greedy that lie at different points on this tradeoff spectrum. We have implemented all our algorithms in a Data Stream Management System and a thorough performance evaluation is presented.
Contact address shivnath@stanford.edu
Sponsored by NSF, NIH
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