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The goal of project BiCEP is to study the performance and develop benchmarks for Complex Event Processing systems. CEP metrics should include the following metrics (some obvious, others not quite):
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Although we are still in the design stage (analysing use cases) we already identified some characteristics we believe a CEP benchmark should measure. Some are obvious, others not quite:
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* '''Sustainable throughput''': the steady-state number of events per unit of time that a (warmed-up) CEP engine can process while performing query processing. Even within the same system, sustainable throughput can vary widely depending on the amount of work to be done during query processing.
* '''Sustainable throughput''': the steady-state number of events per unit of time that a (warmed-up) CEP engine can process while performing query processing. Even within the same system, sustainable throughput can vary widely depending on the amount of work to be done during query processing.
* '''Response time''': the time since the last event of some event pattern is fed into the system until the system notifies the event pattern detection.
* '''Response time''': the time since the last event of some event pattern is fed into the system until the system notifies the event pattern detection.
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* '''Similarity search and precision and recall''': As far as we know, no CEP engine uses any kind of similarity search: the patterns being searched are always precisely specified by a query language. Thus, we expect no false positives and no false negatives. However, if CEP users demand more and more complex patterns, we expect CEP engines to start using similarity search. If similarity search is used, then CEP engines may occasionally produce incorrect results by way of false positives and false negatives. We also expect false positives and false negatives if CEP engines use past events to forecast real-world future events.  
* '''Similarity search and precision and recall''': As far as we know, no CEP engine uses any kind of similarity search: the patterns being searched are always precisely specified by a query language. Thus, we expect no false positives and no false negatives. However, if CEP users demand more and more complex patterns, we expect CEP engines to start using similarity search. If similarity search is used, then CEP engines may occasionally produce incorrect results by way of false positives and false negatives. We also expect false positives and false negatives if CEP engines use past events to forecast real-world future events.  
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Read more about CEP from our [http://www.dagstuhl.de Dagshtul] project presentation [http://drops.dagstuhl.de/opus/volltexte/2007/1143/pdf/07191.BizarroPedro.ExtAbstract.1143.pdf paper].
 
The project started in September 2007 and is funded by an [http://cordis.europa.eu/mariecurie-actions/irg/home.html FP6 Marie Curie International Reintegration Grant].
The project started in September 2007 and is funded by an [http://cordis.europa.eu/mariecurie-actions/irg/home.html FP6 Marie Curie International Reintegration Grant].
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<big>'''Publications'''</big><BR>
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#[http://www.dagstuhl.de Dagshtul] project presentation [http://drops.dagstuhl.de/opus/volltexte/2007/1143/pdf/07191.BizarroPedro.ExtAbstract.1143.pdf paper].

Revision as of 17:27, 14 July 2009

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