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.”
Various of EP applications are mentioned besides algorithmic trading are: external surveillance by regulators, risk management, auditing, market depth analysis and more.
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.
I believe that the general consensus among those who study this kind of thing, is that any decision made wholly by a computer is an operational decision, even if it affects the behavior/tasks of many people or sub-components. Online decisions, being a subset of automated decisions, would then be operational in nature.
The development of the Internet in recent years has made it possible and useful to access many different information systems anywhere in the world to obtain information. While there is much research on the integration of heterogeneous information systems, most commercial systems stop short of the actual integration of available data. Data fusion is the process of fusing multiple records representing the same real-world object into a single, consistent, and clean representation.
The “big elephant in the room” in the ongoing CEP dialog is that most of the current (CEP) software on the market is not capable of machine learning and statistical analysis of dynamic real-time situations. Software vendors have been promoting and selling business process automation solutions and calling this approach “CEP” when, in fact, nothing is new. There is certainly no “technology leap” in these systems, as sold today.
Most BREs today are deployed as “decision services”, and are used in “stateless” transactions to make “decisions” as a part of a business process. A CEP application is instead processing multiple event streams and sources over time, which requires a “stateful” rule service optimized for long running. This is an important distinction, as a stateful BRE for long-running processes needs to have failover support - the ability to cache its working memory for application restarting or distribution. And of course long-running processes need to be very particular over issues like memory handling - no memory leaks allowed!
The one, really big, difference between Complex Event Processing and traditional BRMS tools is that the former is loosely associated with EDA and decisions that are based on multiple events, whereas the latter is more associated with conventional request-reply SOA and automating decisions made in managed business processes.
Esper and NEsper enable rapid development of applications that process large volumes of incoming messages or events. Esper and NEsper filter and analyze events in various ways, and respond to conditions of interest in real-time.
NGramJ is a Java based library containing two types of ngram based applications. It's major focus is to provide robust and state of the art language recognition.
VisualApplets® is a hardware programming tool for FPGAs, based on the use of graphical pipeline-structuree objects. Image processing designs are arranged by the combination of operator modules, filter modules and transport links. The provided libraries c
The Robot-based Imaging Test-bed (RIT) is an open source toolkit designed to be used with a network of wireless robots with imaging cameras. The toolkit will provide the infrastructure for wireless networking, overhead camera localization, path planning,
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BI stands for Business Intelligence, which to some will sound suspiciously similar to Groucho’s famous comment. But in reality BI is more to do with providing the right “Business Information” to people who need it (i.e. business analysts), and there
Subjects covered will include forces, trigonometry, fractals, cellular automata, self-organization, and genetic algorithms. Examples will be demonstrated using Processing with a focus on object oriented programming.