An Overture to the 2007 CEP Blog Awards

Before announcing the winners of the 2007 CEP Blog Awards I thought it would be helpful to introduce the award categories to our readers.

I have given considerable thought to how to structure The CEP Blog Awards. This was not an easy task, as you might imagine, given the confusion in the event processing marketspace. So here goes.

For the 2007 CEP Blog Awards I have created three event processing categories. Here are the categories and a brief description of each one:

The CEP Blog Award for Rule-Based Event Processing

Preface: I was also inclined to call this category “process-based event processing” or “control-based event processing” and might actually do so in the future. As always, your comments and feedback are important and appreciated.

Rule-based (or process-based) event processing is a major subcategory of event processing. Rule-based approaches to event processing are very useful for stateful event-driven process control, track and trace, dynamic resource management and basic pattern detection (see slide 12 of this presentation). Rule-based approaches are optimal for a wide-range of production-related event processing systems.

However, just like any system, there are engineering trade-offs using this approach. Rule-based systems tend not to scale well when the number of rules (facts) are large. Rule-based approaches can also be difficult to manage in a distributed multi-designer environment. Moreover, rule-based approaches are suboptimal for self-learning and tend not to process uncertainty very well. Never the less, rule-based event processing is a very important CEP category.

The CEP Blog Award for Event Stream Processing

Stream-centric approaches to event processing are also a very important overall category of event processing. Unlike a stateful, process-driven rule-based approach, event stream processing optimizes high performance continuous queries over sliding time windows. High performance, low latency event processing is one of the main design goals for many stream processing engines.

Continuous queries over event streams are genenerally designed to be executed in milliseconds, seconds and perhaps a bit longer time intervals. Process-driven event processing, on the other hand, can manage processes, resources, states and patterns over long time intervals, for example, hours and days, not just milliseconds and seconds.

Therefore, event stream processing tends to be optimized for a different set of problems than process-based (which I am calling rule-based this year) event processing. Similar to rule or process-based approaches, most current stream processing engines do not manage or deal with probability, likelihood and uncertainty very well (if at all).

The CEP Blog Award for Advanced Event Processing

For a lack of a better term, I call this category advanced event processing. Advanced event processing will more-than-likely have a rule-based and/or a stream-based event processing component. However, to be categorized as advanced event processing software the software platform must also be able to perform more advanced event processing that can deal with probability, fuzzy logic and/or uncertainty. Event processing software in this category should also have the capability to automatically learn, or be trained, similar to artificial neural networks (ANNs).

Some of my good colleagues might prefer to call this category AI-capable event processing (or intelligent event processing), but I prefer to call this award category advanced event processing for the 2007 awards. If you like the term intelligent event processing, let’s talk about this in 2008!

Ideally, advanced event processing software should have plug-in modules that permit the event processing architect, or systems programmer, to select and configure one or more different analytical methods at design-time. The results from one method should be available to other methods, for example the output of a stream processing module might be the input to a neural network (NN) or Bayesian Belief (BN) module. In another example pipeline operation, the output of a Bayesian classifier could be the input to a process or rule-based event processing module within the same run-time environment.

For all three categories for 2007, there should be a graphical user interface for design-time construction and modeling. There should also be a robust run-time environment and most, if not all, of the other “goodies” that we expect from event processing platforms.

Most importantly, there should be reference customers for the software and the company. The CEP Blog Awards will be only given to companies with a proven and public customer base.

In my next post on this topic, I’ll name the Awardees for 2007. Thank you for standing by. If you have any questions or comments, please contact me directly.

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