On event model and decision model

In the article about "event model" by Luckham and Schulte, on which I have reported last week,   the term "decision model" was referenced as one of the complementary models to an event model.  This has created some discussion within the DMN LinkedIn group, the discussion was created by Paul Vincent who is one of the persons behind the DMN standard work.   I am following the DMN work, though not in an active way, and participated in the kick-off meeting in March 2011,  and also written on "decision models and event processing"  following the talk on "The Decision Model" by KPI at the same event. 

In order to view the relationship between "event model" and "decision model", we need first to understand what is a decision that a decision model is modeling 

 The DMN presentation defines decision as "A Decision is the act of applying decision logic to one or more inputs and produce one output".    This can relate to modeling a collection of business rules, which gets a request with an associated input and returns a result  ("concluded fact") or to a result of some quantitative model (such a scoring model or optimization model). 

In comparison event model is modeling event-driven logic, which is based on deriving situations or derived events based on filtering, transformation, aggregations, and pattern detection over a collection of input events.   

The question is what is the relationship between these two terms?  

we can start by looking at the relationship between business rules and event processing,  there have been long discussions about it in the past, the position I expressed was that related to a discussion with James Taylor in 2008, stated that event processing and decision management are different issues.  A decision may not be related to event, and an event processing may not trigger any decision.  However, there are also relations between them.   

In my recent presentations about event-driven applications I am using the "4 D" that coined by Jeff Adkins: 

 I found out that this is an easy way to explain both IT and business persons who are not familiar with event-based systems what it is in a nutshell.      Referring to this picture, an event model deals with the "detect" and "derive" phases.    The derived event may also by itself denote a decision (since it is obvious what to do if this situation is detected and does not require further decision),  or that the detection of this situation triggers a decision that can be modeled by a decision model (note that the situation may trigger a task, and then the decision may be embedded inside a business process model).  In the example referred to in the slide above,  the  "derive" phase detects that a traffic jam is coming, and the "decide" phased determines how to reset the traffic light policies in order to reduce the traffic jam -- in this case they are complimentary.

Another interesting point is that "decision" in the common interpretation is "request driven",  I have recently written about the distinction between "event driven" and "request driven".   One may claim that a request is an event (which is sometimes semantically doubtful), or that there is always some event that causes a person or a system to make a request (may be metaphysically true, but the event is not explicitly exposed).  However, request-driven is taking an action by request (a passive approach), while event-driven is taking an action without a specific request, due to either occurrence of event or detection of situation (an active approach).    An event can take 

Bottom line:  decision models and event model are complementary models.   I'll discuss similarities and differences in the content of what is modeled in a later phase. 

More on decision model and event processing

I have written before about the "Decision Model" book, when Larry Goldberg visited us in HRL. 
I met Larry again this week in the OMG meeting, and also had dinner with him and Michael Grohs from KPI  in Cary, NC 
(I am now staying nearby in Durham -- almost packed to go start the long journey back).


From their testimony it seems that the decision model is catching quite fast, and hundreds of organizations are using it already in one way or another.  In the OMG meeting they brought their flagship customer Freddie Mac representative, who reported a big success,  there were already several vendors implementing it to be executable.   The benefit is the simplicity and riding on the "spreadsheet" table-like thinking with their methodology.    They are now working on extending the model which started with partial coverage but is evolving,  e.g. they are adding concepts of views and contexts.


Our challenge in event processing is larger, since the complexity of operations is such that I am not sure can be easily expressed in tables, but the seek for abstractions that will enable business analysts and semi-technical users to construct systems is still there.     This is one of our activity areas, which I hope to report progress at some time.   More - later.

Decisions in smarter systems

Arlington., Virginia,  Hayatt Hotel

I am here for the OMG technical meeting.      I have participated in (part of) the decision modeling day organized by Paul Vincent and Christian De Sainte Marie.  Their ultimate goal is to get to a standard on decision modeling, and they have issued proposal for RFP on that issue.  A good survey of that day can be found in James Taylor's Blog.  I sat near James, and he is blogging in real-time.    James himself gave an interesting keynote on the 
importance of decisions  


James concentrated on operational decisions and said in many of the organizations the role of computerized systems is to provide data (in various ways) to manual decision makers when they ask for it.    The smarter systems have larger portion of automated decisions, they are active rather than passive - determine when a decision is needed, and the decisions are measurable with quantitative metrics, so they can be evaluated.  


While James did not talk explicitly about event processing,  it is obvious that it has a significant role in his vision, it has several roles:

1.  determine when a decision is needed
2.  the automated decision itself is often context dependent and  the context can be determined by event processing context mechanism (temporal, spatial, event history related...)
3. the decision itself may depend on event pattern
4. Last but not least -- the extension of event processing to proactive computing coupling with the metric that measures the decision's result can trigger decision to mitigate undesired predicted deviation from the result,(I discussed this one with James during the reception in the evening).  

The EPTS virtual symposium - tomorrow.   A lot of logistics to get it running! 

The decision model - Larry Goldberg's talk

A few months ago I have written a review about "THE DECISION MODEL" book. Today I have hosted in the Haifa Research Lab one of the authors, Larry Goldberg, who gave us a live talk about the model. The decision model provides table oriented representation of rules, where a table designates a family of rules which share the same consequence (i.e. the "right hand side" assigns value to the same fact), tables are connected in a way that a fact that is an outcome of one table is an input for another table, this brings some order to rules, and also fits both inference rules or computational rules. I see strong benefit of using such structured way, and also possible to build an hybrid flow of "rule families" and "event processing networks". I think that there is a future there. I was involved in the past in some similar effort to provide some structure to data-driven rules, a short ACM SIGMOD RECORD paper is referenced here.

I'll have a short presentation in DEBS 2010 around the relations between event processing and business rules (in the fast abstract session).

Book review: The Decision Model

The last package from Amazon brought me the book entitled: "The Decision Model: A Business Logic Framework Linking Business and Technology" by Barbara von Halle and Larry Goldberg.

I have read a draft of the book before, at Barb's request, and wrote a review, from which one line was quoted on the back cover; I believe that the trend of modeling decisions and look at them in perspective of higher level abstractions will become more pervasive, and I view technologies like business rules, event processing and various analytics as building blocks in decision platforms that are going to be notable part of enterprise computing and managing much of the operational decisions. The book has three sections:

Section I puts the decision model in context, explains what is decision model, providing a background comparing decision models with data models, and positioning decision model in the SOA and BPM universe, it also explains the business value. This section is intended mostly for business users and managers that want to get an overview.

Section II explains the decision model in detail, discussing the structural, declarative and integrity principles, and comparing the decision model to the relational model, a motive repeating in previous books by Von Halle. There is even a chapter that is called "The decision model formally defined", but the formalization is in terms of explanations and tables, and not by formal writing, which I guess fits the target audience.

Section III is called "Commentaries" and is actually a collection of articles by the authors as well as by various people active in this space (John Zachman, James Taylor, Bruce Silver and more) discussing specific related issues such as: relations to enterprise decision management, standards, business decision maturity model.

Event based decisions and event processing are mentioned several times within the book, but are not thoroughly discussed. The focus is on facts and rules kind of terminology; a combined model that combines both rules and events is a natural extension, from the point of view of this decision model as well as from the point of view of event processing modeling. I have written before about decision agents, and since that time advanced on the thinking about such a decision agents framework. I'll revisit this issue in one of the following postings.

Bottom line -- the decision model book is a very good book to explain the book to various types of readers (the introduction maps the chapters of the book to the various types of users) and possible basis for both pragmatic foundations of rules technology, as well as a possible basis for a more formal basis for extended decision agents framework. More on this topic -- later.