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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. | ||
| - | * '''Scalability''': Unlike other benchmarks that considerer scalability only as a variation of the benchmark with more data and more users, in BiCEP we would like scalability to be a first-class metric. That is, while it is useful to compare systems at different scale levels, it is also very interesting to assess how well a given system scales. For example, CEP engines can use some of the new techniques (e.g., [http://aws.amazon.com/ec2 | + | * '''Scalability''': Unlike other benchmarks that considerer scalability only as a variation of the benchmark with more data and more users, in BiCEP we would like scalability to be a first-class metric. That is, while it is useful to compare systems at different scale levels, it is also very interesting to assess how well a given system scales. For example, CEP engines can use some of the new techniques (e.g., [http://aws.amazon.com/ec2 Amazon Elastic Compute Cloud]) that allow a system to grab hardware resources on demand makes. We are planning scalability experiments along three directions: i) scale-up: increase the system and increase the load, ii) speed-up: increase the system and maintain the load, and iii) load-up: maintain system but increase the load. |
* '''Adaptivity''': Typically, systems are benchmarked after they are “warmed-up” and in a steady state. However, while it seems that there will be periods where CEP systems are in steady states, it also appears likely that, due to the very unpredictable nature of the real-world events being processed by CEP engines, there will be frequent disruptive moments, when the system should adapt its query processing to be more efficient. | * '''Adaptivity''': Typically, systems are benchmarked after they are “warmed-up” and in a steady state. However, while it seems that there will be periods where CEP systems are in steady states, it also appears likely that, due to the very unpredictable nature of the real-world events being processed by CEP engines, there will be frequent disruptive moments, when the system should adapt its query processing to be more efficient. | ||
* '''Computation Sharing''': Many CEP applications process tens, hundreds, millions of similar queries concurrently. For example, a CEP engine in a financial trading company may be processing thousands of rules for each stock ticket: many customers may be monitoring the same stock but each customer may have slightly different buy or sell values. If the CEP engine can devise query processing techniques such that different queries are able to share computation, then the scalability potential of the system is greatly improved. | * '''Computation Sharing''': Many CEP applications process tens, hundreds, millions of similar queries concurrently. For example, a CEP engine in a financial trading company may be processing thousands of rules for each stock ticket: many customers may be monitoring the same stock but each customer may have slightly different buy or sell values. If the CEP engine can devise query processing techniques such that different queries are able to share computation, then the scalability potential of the system is greatly improved. | ||
* '''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. | ||
| - | Read more about CEP from our [http://www.dagstuhl.de | + | 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 FP6 Marie Curie International Reintegration Grant. | The project started in September 2007 and is funded by an FP6 Marie Curie International Reintegration Grant. | ||
