The Infant, the Elephant and the Intelligent Event

Fellow blogger Opher Etzion, replies to  On Elephants and Analytics with On Unicorn, Professor and Infant.   Opher is kindly giving us another metaphor to consider, the Infant and the Profession, since we are both big fans of big gentle elephants, babies and our universities.  

Opher and I agree that Infants are not Professors, and we also agree that CEP is in its Infancy and there is overhype by folks often implying CEP is a Professor.     So it seems we all have a huge elephant in the room with an Infant Professor hanging on the end of a wildly swinging Elephant’s trunk!

To keep the blogopoints interesting, I should point out that with all this agreement and Kumbaya campfire singing, there are a couple of things I do disagree with in Opher’s amusing counterpoint. 

First of all, Opher uses the well know debate technique of falsely attributing some easily refutable discussion point and then offering a slam dunk counterpoint.   He does this in this clever, but completely inaccurate Opher quote,

 “I [Opher] respectfully disagree with Tim … in his claim that what has been done until today is just hype and hence totally worthless…”

Folks reading my blog know that I have never said “what has been done until today is … totally worthless.”    This is a misfortunate misquote.  Shame on you Opher!  

What I said, easily read in the blog, was that CEP is overhyped and that most of the self-described CEP software on the market today does not live up to the inflated claims we read and hear from CEP software vendors, the analysts and reporters they influence.

The second counterpoint that I find interesting is Opher’s consistent attempt to redress the dramatic lack of capability and analytics in current generation self-described CEP software by repositioning CEP as “intelligent event processing” (IEP) as he is continues in On Intelligent Event Processing.   

Perhaps Opher will be successful in repositioning the vast majority of the original CEP problem space as IEP.   This is a interesting slippery slope, in my opinion.   The new positioning that Opher is offering is that when “event processing” has advanced analytics, it is not CEP anymore, it becomes IEP because CEP is really “Simple Event Processing” (SEP) – event processing with little to no analytical capability.

I don’t know about most of our readers, but all this positioning and repositioning to match the capabilities, or lack of capabilities, in the current portfolio of self-described CEP software vendors is fascinating.

Here is the next logical question is:

What is the difference between a “Complex Event” and an “Intelligent Event” ?

This could get quite interesting, so stay tuned!

The Predictive Battlespace

Friend and colleague Don Adams, CTO World Wide Public Sector, TIBCO Software, explains how CEP can be used to sense, adapt and respond to complex situations in The “Predictive” Battlespace: Leveraging the Power of Event-Driven Architecture in Defense. 

Probabilistic Complex Event Triggering

Here is an interesting paper, Probabilistic Complex Event Triggering, Daisy Zhe Wang, Eirinaios Michelakis, and Liviu Tancau, Computer Science Division, University of California at Berkeley, circa 2005.

One of the first things I noticed about the paper was the discussion of probability in the content of complex event processing, including Hidden Markov processes, Bayesian Belief Networks, and inference models.  

The second thing I noticed was that David Luckham’s work on CEP at Stanford was not referenced anywhere in the Berkeley paper.

 

Is CEP a Service or a Process? Reloaded

In Is CEP a Service or a Process? Paul Vincent of TIBCO blogs that any classification of CEP depends on the application, concluding that CEP is both a process and a service. 

Well (sorry Paul!), I disagree.  CEP is neither a process nor a service; CEP is a concept architecture for processing complex events.   (I have advocated a CEP functional reference architecture, as most readers know.)

To illustrated this point, let’s take a quick look at another functional reference architecture (or, if you perfer, a conceptual architecture), distributed computing.

Is distributed computing a service or a process?

Of course, it is neither a process nor a service, distributed computing is a generic architectural pattern (or style) for processing distributed data, generally across a network.

The same question can be asked of SOA. 

Is SOA a process or a service?

Again, the answer is almost identical. 

SOA is an architectural style (subclass) of distributed computing.

Now, is CEP a product or a service?

CEP is an architectural style (or pattern) for processing complex events.

CEP is neither a process nor a service. 

On the other hand, there are component of a CEP solution that can be represented as a stand alone process or a service.   The same can be said of EAI, SOA, and other subclasses of distributed computing architectural styles and patterns.

Open Service Event Management

One of the benefits of working in different countries is to get the perspectives of various client’s event processing problems.    Of interest to event processing professionals, companies are moving away from expensive software solutions and increasingly moving toward experimenting with economical and open software packages to solve complex problems.   

Recently, I was talking with a client about their experience with commercial security event management (SEM) solutions, for example ArcSight.   In his opinion, ArcSight was not an economically viable solution for his company, so he recommended I take a look at Open Service Event Management (OSEM). 
 
OSEM helps organizations collect, filter, and send problem reports for supported systems (ProLiant and Integrity) running compatible agents.   OSEM automatically sends service event notifications when system problems are detected.

I have not had a chance to look under the hood of OSEM and see how it can be used to collect and send events to emerging rule-based event processing engines.    However, this looks like an interesting lab project and I would like to hear from readers who have experimented with this systems architecture.

Clouding and Confusing the CEP Community

Ironically, our favorite software vendors have decided, in a nutshell, to redefine Dr. David Luckham’s definition of “event cloud” to match the lack-of-capabilities in their products.  

This is really funny, if you think about it. 

The definition of “event cloud” was coordinated over a long (over two year) period with the leading vendors in the event processing community and is based on the same concepts in David’s book, The Power of Events. 

But, since the stream-processing oriented vendors do not yet have the analytical capability to discover unknown causal relationship in contextually complex data sets, they have chosen to reduce and redefine the term “event cloud” to match their product’s lack-of-capability.  Why not simply admit they can only process a subdomain of the CEP space as defined by both Dr. Luckham and the CEP community-at-large? 

What’s the big deal?   Stream processing is a perfectly respectable profession!

David, along with the “event processing community,” defined the term “event cloud” as follows:

Event cloud: a partially ordered set of events (poset), either bounded or unbounded, where the partial orderings are imposed by the causal, timing and other relationships between the events.

Notes: Typically an event cloud is created by the events produced by one or more distributed systems. An event cloud may contain many event types, event streams and event channels. The difference between a cloud and a stream is that there is no event relationship that totally orders the events in a cloud. A stream is a cloud, but the converse is not necessarily true.

Note: CEP usually refers to event processing that assumes an event cloud as input, and thereby can make no assumptions about the arrival order of events.

Oddly enough, quite a few event processing vendors seem to have succeeded at confusing their customers, as evident in this post, Abstracting the CEP Engine, where a customer has seemingly been convinced by the (disinformational) marketing pitches  – “there are no clouds of events, only ordered streams.”

I think the problem is that folks are not comfortable with uncertainty and hidden causal relationships, so they give the standard “let’s run a calculation over a stream” example and state “that is all there is…” confusing the customers who know there is more to solving complex event processing problems.

So, let’s make this simple (we hope). referencing the invited keynote at DEBS 2007, Mythbusters: Event Stream Processing Versus Complex Event Processing.

In a nutshell…. (these examples are in the PDF above, BTW)

The set of market data from Citigroup (C) is an example of multiple “event streams.”

The set of all events that influence the NASDAQ is an “event cloud”.

Why?

Because a stream  of market data is a linear ordered set of data related by the timestamp of each transaction linked (relatively speaking) in context because it is Citigroup market data.    So, event processing software can process a stream of market data, perform a VWAP if they chose, and estimate a good time to enter and exit the market.  This is “good”.

However, the same software, at this point in time, cannot process many market data feeds in NASDAQ and provide a reasonable estimate of why the market moved a certain direction based on a statistical analysis of a large set of event data where the cause-and-effect features (in this case, relationships) are difficult to extract.  (BTW, this is generally called “feature extraction” in the scientific community.)

Why?

Because the current-state-of-the-art of stream-processing oriented event processing software do not perform the required backwards chaining to infer causality from large sets of data where causality is unknown, undiscovered and uncertain.

Forward chaining, continuous query, time series analytics across sliding time windows of streaming data can only perform a subset of the overall CEP domain as defined by Dr. Luckham et al.

It is really that simple.   Why cloud and confuse the community?

We like forward chaining using continuous queries and time series analysis across sliding time windows of streaming data. 

• There is nothing dishonorable about forward chaining using continuous queries and time series analysis across sliding time windows of streaming data.   
• There is nothing wrong with forward chaining using continuous queries and time series analysis across sliding time windows of streaming data. 
• There is nothing embarrassing about forward chaining using continuous queries and time series analysis across sliding time windows of streaming data. 

Forward chaining using continuous queries and time series analysis across sliding time windows of streaming data is a subset of the CEP space, just like the definition above, repeated below:

The difference between a cloud and a stream is that there is no event relationship that totally orders the events in a cloud. A stream is a cloud, but the converse is not necessarily true.

It is really simple.   Why cloud a concept so simple and so accurate?

Scheduling Agents with Rules Engines

Paul Vincent of TIBCO talks about agents in his post, CEP and Agents…

At the core, TIBCO’s BusinessEvents is RETE-based rules engine and rules engines are well suited for scheduling problems.  This makes perfect sense, since many of TIBCO’s customers deploy BusinessEvents in scheduling-oriented, not detection-oriented, solutions.

It begs to be pointed out, however, that scheduling is only one component of a CEP architecture. 

Normally, the scheduling component of a distributed event processing architecture manages the intelligent scheduling of the sharing of data between distributed agents that are running a variety of analytics.

Simply stated, all agents are not rules engines; however, rules engines are often used to schedule the cooperation between analytical agents in a distributed agent-based architecture.