CEP is intelligent software that is essentially the next step in algorithmic trading – it sifts through market events looking for possible patterns and acts on them. A recent study into banks’ IT spending patterns by consultancy Aite Group, suggested that while budgets as a whole were likely to shrink by 5%, CEP investment remains on an upward trajectory. 36% of respondents to the survey intended to spend more on CEP this year than in 2008.
Adam Honore, senior analyst at Aite and author of the report, says: “We’re still bullish on the potential for CEP across financial services. Once one group successfully deploys a CEP application, word spreads and more technology groups look at CEP to help solve their issues.”
I don’t know whether you said that a CEP application must necessarily have a model. It may have, or it may not. A rule-based approach (in its general acceptation) is not considered as a model. In the AI terminology, rules are considered as “shallow knowledge”, while models are considered as “deep knowledge”. Shallow knowledge expresses the people’s experience, links symptoms to causes directly, while deep knowledge establishes the links using a model, and the model can be interpreted. Shallow knowledge is very helpful in many cases, and as deep knowledge it also allows detecting situations. Of course, the cooperation of both is desirable to build more powerful systems. I did a rapid search, and below are 3 entries for reference:
Rule-processing is just a style of computation. Of course it is used in BRMS, but it is also used in CEP. CEP systems typically employ rules-based processing to infer higher-order events by matching patterns across many event streams within the event ‘cloud’. BRMS’s use rule processing to match patterns within data tuples representing business-orientated data. CEP systems may support the use of advanced analytics to manage predictive analysis, reasoning under uncertainty and other requirements in relation to the event cloud. Some of the better BRMS’s offer similar analytics in regard to processing business data.
L. Stojanovic, S. Sen, J. Ma, and D. Riemer. Proceedings of the 6th ACM International Conference on Distributed Event-Based Systems, page 385--386. New York, NY, USA, ACM, (2012)
Y. Xu, N. Stojanovic, L. Stojanovic, and T. Schuchert. Proceedings of the 6th ACM International Conference on Distributed Event-Based Systems, page 379--380. New York, NY, USA, ACM, (2012)
J. Llinas, C. Bowman, G. Rogova, A. Steinberg, and F. White. In P. Svensson and J. Schubert (Eds.), Proceedings of the Seventh International Conference on Information Fusion (FUSION 2004, page 1218--1230. (2004)
W. White, M. Riedewald, J. Gehrke, and A. Demers. PODS '07: Proceedings of the twenty-sixth ACM SIGMOD-SIGACT-SIGART, page 263--272. New York, NY, USA, ACM, (2007)