Event Soup and The Story of Amaldo

Continuing with our discussion on Complex Systems and CEP, let’s turn our attention, momentarily, to more scientific, or perhaps philosophical, discussions. Let’s review a bit of chaos theory via the Lorenz effect and talk about the “event soup”, a phrase I shamelessly coined in On the Maturity of CEP.
Edward Lorenz was using a computer model [...]

CEP in the 1960s: Air Traffic Control

Professor Luckham wrote about CEP and the future of global Air Traffic Control (ATC) in The Future Event Driven World: Global Air Traffic Management.   One of the first commercial applications of complex event processing was in the early 1960s in the field of commercial aviation, for example see the history of Air Traffic Control.
Although experimental [...]

Complex Systems and CEP

A complex system is defined as a system composed of related components that as a whole exhibit one or more properties not obvious from the easily observed properties of the individual parts.  This is certainly true of the CEP notion of the “event cloud” in network systems.   A modern energy or telecommunications network is [...]

Quintessential Event Processing: Signature Versus Anomaly Detection

Detection experts understand that the optimal detection design and architecture is generally a combination of both signature and anomaly detection engines.   In event processing, signature detection involves the real-time pattern matching analysis of events.   A core advantage of signature detection is that basic pattern matching models are easy to understand and develop [...]

CEP as a Service (CEPaaS) with MapReduce on Amazon EC2 and Amazon S3

Just as I was starting to worry that complex event processing community has been captured by RDBMS pirates off the coast of Somalia, I rediscovered a new core blackboard architecture component, Hadoop.
Hadoop is a framework for building applications on large commodity clusters while transparently providing applications with both reliability and data motion.  Hadoop implements  [...]

CEP by Apache Mahout via the Google MapReduce Framework

MapReduce is a software framework implemented in C++ with interfaces in Python and Java introduced by Google to support parallel computations over large (multiple petabyte) data sets on clusters of computers.  The Apache  Hadoop project is a free open source Java MapReduce implementation.  Mahout is an Apache project, based on Hadoop, with an objective to [...]

More on The Value of (Production) Rules

Paul Vincent of TIBCO wrote an outstanding post, The Value of (Production) Rules … Paul correctly notes:
In summary, of course,  event-processing rules, event-driven rules, and rules for business decisions can all overlap, depending on the application.
By coincidence, I was reading an excellent paper recently, Reactive Rules on the Web. In this Springer-Verlag paper, the distinguished [...]

Should We Simply Rename CEP BRMS?

Seemingly inundated with blog posts about CEP and BRMS, it seems we should simply rename the current CEP space, BRMS.  All of the current self-described CEP products on the market today are rules-engines, this includes the continuous query stream processors and the RETE engines.  Furthermore, a quick review of Wikipedia says BRMS is, as follows [...]

Will Commercial CEP Engines Replace Algorithmic Trading Platforms?

Sometimes I think Marc Adler is reading my mind, and I wonder how he does it.  I have been thinking for weeks about writing a detailed post about why Algorithmic Trading is not Complex Event Processing; but then Marc pens the thoughtful, Do You Really Need a Commercial CEP Engine?
In his post, Marc [...]

Lessons on Rules from the Pinewood Derby

Remember all the fun we had when we carved out race cars for our Pinewood Derby? For those of you are were not apart of this great American Cub Scout culture, the pinewood derby is a racing event for Cub Scouts in the Boy Scouts of America. Cub Scouts, with the help of [...]

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