On "CEP and Big Data 2" - comments on Philip Howard's observations.

Philip Howard from Bloor Research has posted some observations on his Blog entitled "CEP and Big Data 2".   Here are some comments (actually nothing new - just summarizing things I have written about before).
Philip deals with three issues:

• whether the name CEP is appropriate or should be changed? 
• who should be credited as the pioneer of this area?   
• whether CEP implies real-time processing?  
•  who are the CEP big data platforms?

Here are summary of my views on each of this topics.

The name "Complex Event Processing"

Exactly four years ago I posted on this Blog an explanation about - "why I prefer to use the name event processing without any prefix, infix or suffix".   My particular dislike of the term "complex event processing" stems from the ambiguity in the name - some people (including David Luckham who coined this term) view it as processing of complex events, some interpret it as complex processing of events, and then debate of when something is complex enough, and what type of complexity is needed  to qualify as CEP.  Moreover some of the vendors use this term for products that are neither of the two options.   I think that two words is enough for the name of a discipline, examples: information retrieval, machine learning, image processing and much more....  Thus, from my point of view the term "event processing" subsumes all other terms like complex event processing, business event processing, event stream processing and more.

Who gets the pioneering credit

Philip as a good UK patriot wonders why the Wikipedia value about Wikipedia and other sources gives credit to David Luckham and forget the Apama work that came from Cambridge UK.    Looking at Wikipedia, it has one mention of David, as well as other references (like our EPIA book). It indeed does not mention Apama or any paper by John Bates, but being a Wikipedia, anybody can suggest additions.   

David Luckham had major influence on this area, since he was the first one who published a full book and exposed the young area to the general public.    An article in IEEE Computer, published in 2009,  made some investigation of the history of that area and determined that in the 1990-ies there were four parallel projects that can be classified as starting points in this area:  David Luckham's project in Stanford,  John Bates' project in Cambridge (UK, not Boston), Mani Chandy in Cal Tech,  and our Amit project in IBM Haifa Research Lab.    I share Philip's view that John Bates should have full credit as one of the pioneers, and still view David Luckham as the "elder statesman" of the community.

Is CEP necessarily associated with real-time?

I have written several times about this topic, last time in response to Chris Carlson, to whom Philip also responds.   There is some abuse of the term real-time in the industry, while its meaning is "within time constraints", many people interpret it as "with very low latency".   This is not the same,  anyway, event processing is a functionality with applications that require very low latency, applications which require to react within real-time constraints (which can be: 2 hours), some require both, and some require none.

Who are the CEP big data platforms?

I have taken upon myself the limitation not to state opinions on commercial products within this Blog  - leaving  it to analysts.   Thus will make one comment.  There is distinction between two types of software entities - 

which is sometimes confused in the language used by people.

• Event Processing Platform is a software that enables the creation of event processing network, handle the routing of events among agents, management, and other common infrastructure issues.
• Event Processing Engine is a software that enables the creation of the actual function - in the EPN term implementing agents.

This is similar to the difference between an application server and a single component (programming in the small vs. programming in the large).    Some of the available platforms for "event processing for big data" provide the first one -- it gives infrastructure, but not implementing any type of functionality, but enabling developers to create their own functionality, thus they don't do full-fledged event processing.   Seems that many people classify both under the same classification  (of course there are products that do both). 

More on big data and event processing

Philip Howard, one of the analysts who follows the event processing areas for many years, recently wrote about "CEP and big data".    emphasizing the synergy of data mining techniques on big data as a basis to create real-time scoring based on predictive model created by data mining techniques, his inspiration for writing this piece was reviewing the Red Lambda product.   It is certainly true that creation of event processing patterns off-line using mining techniques and then tracking this event patterns on-line using event processing is a valid combination, although the transfer from the data mining part to the event processing part typically requires some more work (in most cases also involves some manual work).    In general getting a model built in one technology to be used by another technology is not smooth, and require more work.  
The synergy between big data and event processing has more patterns of use -- as  big data in many cases is manifested in streaming data that has to be analyzed in real-time,  Philip mentions Infosphere Streams, which is the IBM platform to manage high throughput streaming data.   The data mining on transient data as a source for on-line event processing, and the real-time processing of high throughput streaming data, are orthogonal topics that relate to two different dimensions of big data, my posting about the four Vs summarizes those dimensions.   

On event processing as a technology and as a business

Philip Howard, an analyst who covers event processing for several years now, has posted a blog entitled: what's happening with event processing, observing that event processing is getting integrated with other areas such as: BPM, data integration and more. This is not a new phenomenon; in the EPIA book, we mention that among the event processing trends is moving from standalone to integrated even embedded, and this trend is evident with the evolution of event processing as a start-up universe, to having bigger software vendors as dominant forces. However - will event processing as technology going to disappear? I don't think so. There is common functionality among event processing utilization in various industries, applications, and hosting technologies, in all of them there are functions of filtering, event transformation, aggregation, pattern detection, and routing. It is not cost-effective to re-invent the wheel for each individual use (although there are variations). This is a similar situation to databases; database can be used for various reasons, and also be embedded with various other technologies and products (e.g. application servers, BPM, system management products, messaging systems - all use databases), while there are also variations, it is not the case that each of these areas develop database technology in an ad-hoc fashion. Thus, I see event processing continuing to evolve as a technology, and having both research and development activities that build generic event processing tools. From the business point of view, there will always be some niche for event processing stand-alone applications, but as Philip writes, most of the market will indeed be in the integrated area, this fact already reflects on the event processing technology in terms of need for standards, interoperability features, and ability to have embeddable collection of building blocks and components. More about this - later.