Originally, researchers in CEP were not trying to solve a problem of streaming data or streaming events.   Often we read this mischaracterization by folks in the database/streaming domain, as they were focused on the low latency processing of streaming events.   A natural extension of this research has been stream processing software (often called “engines”) that process streaming data with continuous queries, for example market data feeds for algo-trading or best market order execution.  This mischaracterization is partly responsible for why we see many order processing applications in market data stream processing mislabled as “complex event processing” applications.

The genesis of complex event processing was not the stream processing need for “feeds and speed” but the processing capability to solve what can be characterized as the “problem of asymmetric capabilties”.   The term “asymmetric” has been used in the military domain. For example we often hear the term “asymmetric warfare.”  However, in general the concept of “asymmetrical processing capablities” is the true genesis for CEP and related processing concepts and domains.   It is this genesis that distinguishes CEP from EDA, SOA, SOR, and so many other technology oriented concepts.

In order to illustrate what I mean by “asymmetrical processing capablities” we will take the example of the evolution of rocketry.    In the early days, scientists learned how to make rockets, I assume with gunpowder and similar chemical compounds to launch rockets.   Over many years the application of rocketry advanced much faster than the ability to understand the situations created in the sky.    In other words, folks could fill the skies with rockets long before they had the capability to track and identify (or sense and respond to)  the rockets in real time.

Therefore, the concept of “asymmetrical processing capablities” is the situation where there is a capability, such as “launch a rocket, sense-and-respond,” that is asymmetric in nature.    In other words, the capability to detect multiple rocket launches creates an asymmetric situation where it is easy to launch rockets, but hard to detect and defend against those launches.

The same concept can be applied to everyday air travel.   If we could only fly airplanes, but did not have the capability to track the planes, understand situations in airspace, and then respond to changing situations, air travel would be quite difficult.   Lucky for us, the global traveller, there is symmetry in the capabilities to build and fly aircraft and the capabilities to detect, track and follow the evolving situations in the sky.

The genesis of CEP was to solve the problem of asymmetry in cyberspace, or if you prefer, distributed data networks.   The folks who identified, early on,  the problems associated with asymmetry in cyberspace were folks working the the field of network and security management.    This is because there has been, and is currently, a great asymmetry between the capablities to “launch a process or transaction” in cyberspace and the capabilties to detect and track what is going on in the same domain.

In my next post on this topic, we will go into some details of this asymmetry and review the first CEP projects from Stanford University in the context of asymmetric processing capabilities in cyberspace.

Even thought TIBCO’s BusinessEvents does not yet support Bayesian Classifiers, Artificial Neural Networks and other advanced decision support algorithms, it is just a matter of time before TIBCO will add these advanced features “out of the box”.  On the other hand, the extensible nature of TIBCO’s BE makes it possible to add probabalistic computing functionality, however this requires quite a lot of programming and integration work.

When I see a great release like this for TIBCO, it makes me a little nostalgic for “the good old days” travelling the world in the front of the aircraft for TIBCO.   TIBCO has a rich and diverse customer base.  This customer base includes financial services companies; however, TIBCO is much less dependent on financial services than other event processing companies.   So, with TIBCO you not only get great technology, but rock-solid stability in an unstable and uncertain business world.

As a side note, an S&P analyst recently downgraded TIBCO’s stock (TIBX), primarily due to chao in the financial services sector.    Because of TIBCO’s global reach and stability, plus forward vision, advanced technologies and many years of commericial success, the S&P downgrade will create a buying opportunity for TIBCO stock.

Madison Wi-Fi network sold to Atlanta firm: Xiocom purchases Mad City Broadband, a firm that has suffered significant criticism over the performance of its Wi-Fi network in Madison, Wisc. The press release from Xiocom (some quoted in the Badger Herald article) are a bit over the top about a network that reportedly has few users, inconsistent performance, and covers only a fraction of the city.

The following excellent discussion is reposted from Streaming SQL approaches insist in ignoring causality by PatternStorm.

The recent paper “Towards a Streaming SQL Standard” by Oracle and Streambase unifies and generalizes two different execution models of Streaming SQL: Oracle’s and StreamBase’s.

While it’s true that the generalization succeeds in overcoming the unability of both execution models of producing correct results for astonishing simple queries (showing evidence of the actual limitations of these two Streaming SQL languages) it is also true that the generalization is closer to being overly complex than natural and intuitive.

The root cause behind the actual limitations of these two Streaming SQL languages is that their execution models “hardcode” the way events can be related to each other: in the Oracle case events are partially ordered by timestamp, in the StreamBase case events are totally ordered by time of arrival. These design decisions (natural in a stream oriented lamguage) have strong implications on what queries can be answered correctly, particularly when these queries involve joins of derived streams.

The generalization, of course, mainly consists in providing a new operator that allows the user to establish custom ordering relationships among the events (the SPREAD operator), which is good news but takes us to the fundamental issue: event processing cannot be reduced to stream processing, that is, to the processing of events that are totally or partially ordered by a pre-defined relationship (as Oracle and StreamBase actual implementations do), on the contrary, no particular ordering can be assumed because the user needs to be able to order the events in different ways in order to solve different problems. This is what event processing is about and the paper provides evidence that Streaming SQL approaches have found the need to move towards that direction and are having trouble in their way.

For instance, one of the queries used in the paper as an example of a query that StreamBase cannot solve (but Oracle can) is the following: correlate the stream that contains the total number of cars on the road for each time interval with the stream that contains the total average speed of the cars on the road for each time interval in order to detect the situation where the avergae speed is below 45 and the total number of cars is two or more. This query can be very easily and more robustly solved if you order the events by causality rather than by time, that is, if you have each position report update the average speed stream and the total number of cars stream and then you causally relate each position report to the new average speed event and the new total number of cars event that it generates; then the query is just a matter of detecting all report speeds that are causally related both to an average speed event below 45 and a total number of cars event of two or more (notice that this approach is more robust than Oracle’s time-based one because it works without requiring derived streams to be synchronized with the report speed stream)

Conclusions:

• Event Processing is a generalization of Stream Processing (as the paper shows)
• Event Processing requires providing the ability to the user of creating custom relationships among events and then define patterns/queries using those custom relationships.
• Causality is more often than not a more robust and easier criteria to order events than time or order of arrival.
• Event Processing Languages should support causality.

Regards,
PatternStorm