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ZStream: a cost-based query processor for adaptively detecting composite events
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International Conference on Management of Data archive
Proceedings of the 35th SIGMOD international conference on Management of data table of contents
Providence, Rhode Island, USA
SESSION: Research session 5: large-scale data analysis table of contents
Pages 193-206  
Year of Publication: 2009
ISBN:978-1-60558-551-2
Authors
Yuan Mei  Massachusetts Institute of Technology, Cambridge, MA, USA
Samuel Madden  Massachusetts Institute of Technology, Cambridge, MA, USA
Sponsors
ACM: Association for Computing Machinery
SIGMOD: ACM Special Interest Group on Management of Data
Publisher
ACM  New York, NY, USA
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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.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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