Event and Pattern Detection over Streams
Complex event processing (CEP); Event stream processing (ESP)
An event is a basic unit of information in streaming data. An event pattern is a combination of events correlated over time. Event pattern detection is an important activity in complex event processing. In this setting, the matches to the event patterns are referred to as complex events.
In the early 1990s, a set of pioneering work in event systems, such as SNOOP  and ODE , set out to define query languages for expressing event patterns. In these proposals, the data model for expressing events is not fixed. More recently, the approaches proposed by Cayuga [1, 5, 6] and SASE  for event pattern detection align more closely to relational query processing, in that each event is modeled by a relational schema, and some of the operators for expressing event pattern queries are drawn from relational algebra. Regardless of the data model for events, these systems all use some...
- 1.Brenna L, Demers A, Gehrke J, Hong M, Ossher J, Panda B, Riedewald M, Thatte M, White W. Cayuga: a high-performance event processing engine. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 2007. p. 1100–2.Google Scholar
- 3.Chakravarthy S, Krishnaprasad V, Anwar E, Kim SK. Composite events for active databases: semantics, contexts and detection. In: Proceedings of the 20th International Conference on Very Large Data Bases; 1994. p. 606–17.Google Scholar
- 4.Chandrasekaran S, Cooper O, Deshpande A, Franklin MJ, Hellerstein JM, Hong W, Krishnamurthy S, Madden SR, Raman V, Reiss F, Shah MA. Telegraph CQ: continuous dataflow processing for an uncertain world. In: Proceedings of the 1st Biennial Conference on Innovative Data Systems Research; 2003.Google Scholar
- 5.Demers A, Gehrke J, Hong M, Riedewald M, White W. Towards expressive publish/subscribe systems. In: Advances in Database Technology, Proceedings of the 10th International Conference on Extending Database Technology; 2006. p. 627–44.Google Scholar
- 6.Demers A, Gehrke J, Panda B, Riedewald M, Sharma V, White W. Cayuga: a general purpose event monitoring system. In: Proceedings of the 3rd Biennial Conference on Innovative Data Systems Research; 2007. p. 412–22.Google Scholar
- 7.Fabret F, Jacobsen HA, Llirbat F, Pereira J, Ross KA, Shasha D. Filtering algorithms and implementation for very fast publish/subscribe. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 2001. p. 115–26.Google Scholar
- 8.Gehani NH, Jagadish HV, Shmueli O. Composite event specification in active databases: model and implementation. In: Proceedings of the 18th International Conference on Very Large Data Bases; 1992.p. 327–38Google Scholar
- 10.Motwani R, Widom J, Arasu A, Babcock B, Babu S, Datar M, Manku GS, Olston C, Rosenstein J, Varma R. Query processing, approximation, and resource management in a data stream management system. In: Proceedings of the 1st Biennial Conference on Innovative Data Systems Research; 2003.Google Scholar
- 11.Ramakrishnan R, Donjerkovic D, Ranganathan A, Beyer KS, Krishnaprasad M. SRQL: sorted relational query language. In: Proceedings of the 10th International Conference on Scientific and Statistical Database Management; 1998. p. 84–95.Google Scholar
- 12.Sadri R, Zaniolo C, Zarkesh AM, Adibi J. Optimization of sequence queries in database systems. In: Proceedings of the 20th ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems; 2001. p. 71–81.Google Scholar
- 13.Seshadri P, Livny M, Ramakrishnan R. SEQ: a model for sequence databases. In: Proceedings of the 11th International Conference on Data Engineering; 1995.p. 232–9.Google Scholar
- 14.Wu E, Diao Y, Rizvi S. High-performance complex event processing over streams. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 2006. p. 407–18.Google Scholar