ZStream: A cost-based query processor for adaptively detecting composite events
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Madden_Zstream.pdf
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1.19 MB
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Author(s)
Mei, Yuan
Madden, Samuel R.
Date Issued
2009
Journal
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data (SIGMOD '09 )
Publisher
Association for Computing Machinery (ACM)
Citation
Yuan Mei and Samuel Madden. 2009. ZStream: a cost-based query processor for adaptively detecting composite events. In Proceedings of the 2009 ACM SIGMOD International Conference on Management of data (SIGMOD '09), Carsten Binnig and Benoit Dageville (Eds.). ACM, New York, NY, USA, 193-206.
Version
Author's final manuscript
Abstract
Composite (or Complex) event processing (CEP) systems search sequences of incoming events for occurrences of user-specified event patterns. Recently, they have gained more attention in a variety of areas due to their powerful and expressive query language and performance potential. Sequentiality (temporal ordering) is the primary way in which CEP systems relate events to each other. In this paper, we present a CEP system called ZStream to efficiently process such sequential patterns. Besides simple sequential patterns, ZStream is also able to detect other patterns, including conjunction, disjunction, negation and Kleene closure.
Unlike most recently proposed CEP systems, which use non-deterministic finite automata (NFA's) to detect patterns, ZStream uses tree-based query plans for both the logical and physical representation of query patterns. By carefully designing the underlying infrastructure and algorithms, ZStream is able to unify the evaluation of sequence, conjunction, disjunction, negation, and Kleene closure as variants of the join operator. Under this framework, a single pattern in ZStream may have several equivalent physical tree plans, with different evaluation costs. We propose a cost model to estimate the computation costs of a plan. We show that our cost model can accurately capture the actual runtime behavior of a plan, and that choosing the optimal plan can result in a factor of four or more speedup versus an NFA based approach. Based on this cost model and using a simple set of statistics about operator selectivity and data rates, ZStream is able to adaptively and seamlessly adjust the order in which it detects patterns on the fly. Finally, we describe a dynamic programming algorithm used in our cost model to efficiently search for an optimal query plan for a given pattern.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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Creative Commons Attribution-Noncommercial-Share Alike 3.0
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DOI of Published Version
https://doi.org/10.1145/1559845.1559867
