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.

First of all, let’s ground the discussion a bit by translating “smart order routing” to “rule-based message routing” since in this application “smart”  translates to “using rules” and “order” translates to “message”.    Basically, Mark (and other “new on the routing scene” stream processing players) argue that rule-based message routing is CEP.  I will argue that routing is not even close to CEP.  Here is why,

Let’s take a look at a router on the backbone of the global Internet.   A backbone router has very sophisticated software developed over many decades.   These routers run sophisticated, mature algorithms to determine how to route messages (packets) and use these algorithms to build complex routing tables. 

In addition, these routers process messages (packets) from countless sources and route messages (packets) to countless destinations.  Using some of the terms in early posts (above), there is a great “confluence of events” processed by routers.    Futhermore, there are normally quite complex authentication, authorization and other security parameters managed in a router, all in real time.   Routers do much more, but I don’t want to get too deep into routing in this post.

My point is that, without any doubt, global Internet routers process very “cloudy” “confluence of events” with much more sophistication than order routing applications.    However, we do not call Internet routing “CEP”, regardless of how many connections are processed or how much sophisticated processing occurs.  The reason is because the “C” in “CEP” defines a complexity that is at a higher abstraction than messaging and routing.

If you study the literature on CEP, some of which I posted recently, CEP was envisioned to solve complex event processing problems “on top of the routing layer” because the routing layer is a mature technology layer.  We can route, pure and simple.  Of course, we are always seeking faster, more scaleable and more secure routing. 

I admire some of the startups in the CEP/ESP/EP space for working hard to make money and for aggressively positioning their products and attempting to build market share.   However, issues surface when these same companies seem to believe they are the first companies to work in the event processing or message routing space and that they can define whatever they want as “complex event processing” as long as it benefits their sales targets.

There is no doubt that a router does much more sophisticated event processing than the new rule-based stream processing systems running continuous queries across streaming data.  There is no doubt that a router processes a complex “confluence of events”.   However, we don’t call routers “CEP”. 

We do not call routers “CEP” because CEP is about a higher level of knowledge processing.  CEP was created to detect the “complex events” that happen above the mediation and routing layer.     The literature and original examples on CEP are quite clear on this.

adaptive process management (n.) an element of resource and business process management, adaptive search and event processing. Sometimes referred to as “Level 4” event processing or process refinement.

application concept (n.) a definition of a set of properties that represent the data fields of an application entity. An application concept can describe relationships among themselves. For example, an order concept might have a parent/child relationship with an item concept. A department concept might be related to a purchase requisition concept based on the shared property, department_id. Application concepts can include an application state model.

application state modeler (n.) a UML-compliant application that allows you to model the life cycle of a concept instance — that is, for each instance of a given concept, you can define which states it will pass through and how it will transition from state to state. States have entry actions, exit actions, and conditions, providing precision control over the behavior of an event processing agent. Transitions between states also may have rules. Multiple types of states and transitions maximize the versatility and power of the application state modeler.

derived event (n.) an event that is created as a result of processing one or more other events.

complex event (n.) an event that is a situation-entity abstraction of two or more simple, derived or other complex events.

complex event processing (n.) CEP is a technology for extracting information from message-based systems. CEP is primarily an event processing concept that deals with the task of processing multiple events from an event cloud with the goal of identifying the meaningful events within the event cloud. CEP employs techniques such as detection of complex patterns of many events, event correlation and abstraction, event hierarchies, and relationships between events such as causality, membership, and timing, and event-driven processes.

event (n.) a instance of an event definition. It is an immutable object that represents a business activity that happened at a single point in time. Just as one cannot change the fact that a given activity occurred, one cannot change an event — events are immutable.

event aggregation (n.) the aggregation of simple, derived or complex events into higher levels of event abstractions.

event definition (n.) a set of properties related to a given activity that represents an important or interesting change of state in a human, system or computational activity. An event definition includes event properties such as event priority, event time to live (TTL), and a description of the payload, which is comprehensive information related to the activity that occurred. Events expire when the TTL has elapsed, unless the event processing agent has instructions to consume them prior to that time.

event channel (n.) a communications channel in which events are transmitted from event source to event receivers, typically received as electronic messages. Each channel can have multiple destination and. events can be configured to transmit to a default destination. JMS is an example of an event channel.

event cloud (n.) 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. 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.

event-driven (n.) the behavior of a human, system or computational entity whose execution or actuation is in response to events, typically received as electronic messages.

event-driven architecture (n.) an architectural style for distributed computing applications in which some of the components are event-driven and communicate by means of events.

event processing (n.) computing that performs operations on events, including modifying, creating and destroying events.

event-object (n.) an software object that represents an event, generally for the purpose of computer processing, that exhibits both encapsulation, inheritance and polymorphism.

event prediction (n.) computational activity where the impact of events, complex events, and situations caused by events identified, including both opportunity or threat. Sometimes referred to as “Level 2” event processing, impact assessment or predictive analytics.

event pre-processing (n.) computational activity where events are cleansed or normalized to produce semantically understandable data. Sometimes referred to as “Level 0” event processing.

event processing (n.) computational activities on events dealing with the association, correlation, and combination of event data and information from single and multiple event sources to achieve refined identity and situation estimates for observed event objects, and to achieve complete and timely assessments of opportunities, threats, and their significance. Event processing is characterized by continuous refinements of event estimates and assessments and by evaluation of the need for additional sources, or modification of the process itself, to achieve improved results.

event processing agent (n.) an EPA is a computational entity that performs event processing.

event processing network (n.) a set of event processing agents and a set of event channels connecting them.

event properties (n.) data representation of an event, typically by name-value pairs of type string, integer, real, boolean or a complex data type.

event refinement (n.) filter, identify and track events & make initial processing decisions based on association, correlation and state estimation. Sometimes referred to as “Level 1” event, or event-object, track and trace.

event stream (n.) a time-ordered sequence of events. An event stream may be bounded by a certain time interval or other contextual dimension (content, space, source, certainty), or be open ended and unbounded.

event stream processing (n.) a time-ordered sequence of events. An event stream may be bounded by a certain time interval or other contextual dimension (content, space, source, certainty), or be open ended and unbounded.

rule (n.) defines what triggers unusual, suspicious, problematic, or advantageous activity within an event processing agent and what the EPA does when it discovers these types of activities. Rules execute actions based on certain conditions on events, instances, or a combination of both. A rule includes a group of condition-rule statements and action-rule statements. The condition statements instruct the EPA what to look for in events, and action statements instruct the EPA how to respond when conditions are met. If all the conditions in a rule are satisfied by events or instances or both, the EPA fires the actions. The action might be to execute tasks, create an event instance, modify property values in an event instance, create and send an event, or something else.

rules engine (n.) a type of event processing agent that uses a declarative programming model to process events. Formally described as “an abstract structure that describes a formal language precisely, i.e., a set of rules that mathematically delineates a (usually infinite) set of finite-length strings over a (usually finite) alphabet“. Informally, it can be any system that uses rules, in any form, that can be applied to data to produce outcomes.

rule language (n.) is an artificial language that is used to control the behavior of an event processing agent. Rules languages, like human languages, have syntactic and semantic rules to define meaning.

situation refinement (n.) identify situations, or complex events, based on event clustering, event-event relationships and relationship analysis and context. Sometimes referred to as “Level 2” event processing.

simple event (n.) an event that is not an abstraction or composition of other events.

virtual event (n.) an event that is imagined, modeled or simulated.

The National Risk Register is intended to capture the range of emergencies that might have a major impact on all, or significant parts of, the UK. It provides a national picture of the risks we face, and is designed to complement Community Risk Registers, already produced and published locally by emergency planners. The driver for this work is the Civil Contingencies Act 2004, which also defines what we mean by emergencies, and what responsibilities are placed on emergency responders in order to prepare for them. Further information about the Act can be found on the UK Resilience website.

Seems like the greatest threat to national security is a flu pandemic.

In the last release of the GA code, Google added Event Tracking.   In Google-speak, events are actions that visitors take on a web page that do not generate new pageviews. Examples of these events are, interacting with a Flash player, a AJAX widget or an audio player. In the old GA, webbies could track event-data as a pageview.  However, because event tracking using crude pageviews is not very effective, GA added new functionality they refer to as Event Tracking.

There are 4 components in the GA events data model; Objects, Actions, Labels and Values. GA Objects are areas of web pages that visitors interact with, for example a video player or an Ajax widget.  The second part of the GA event tracking data model is Actions.  Actions are related to an Object, representing Actions that visitors perform on the Object.  Labels further describe Actions, associating context with Actions.   Last, but not least, Values are quantities associated with Labels.

Notice how Google defines this event processing model as Event Tracking.   Similar to the reference architecture we described in What is Complex Event Processing?,  operations on single event objects are generally tracking-oriented, often referred to as Event Refinement  in the art-and-science of multisensor data fusion (MSDF). 

The GA event tracking model does not (yet) incorporate Situation Refinement, which in MSDF-speak, would be object-to-object processing, representing a higher level of interaction modelling.   

Can you provide examples where object-to-object interaction between various objects on a single web site represents a real-world situational (complex event) model? 

Taking this one step further, can you think of some examples where object-to-object interaction between various objects on different web sites represents a real-world situational model?

For example, in my earlier post Modelling Situations for Event Processing, we illustrated modelling in CEP by looking at an example situation, “airplane collision”. This complex event is composed of many objects than are not event-objects. In fact, depending on how you define “event” most of the components of the complex event, “airplane collision” are not events at all, but other situations or sub-states of the objects under observation, in this case an aircraft.

For example, the direction an airplane is flying is not necessarily an “event” per se. Also, the amount of fuel on the aircraft at any given moment in time is not necessarily an “event” either. The same holds true for other components that comprise the object we are modelling. In fact, again depending on how you define “event”, most of the states of the components that are critical to processing a complex event are not events at all, they are simply object-states.

Complex events are generally composed of objects and the state of the complex event is defined by the objects in the complex event determined by the states of the components of the objects in the model.

Another way to view this key point is that CEP is characterised as predicting outcomes (states) based on the relationship between the objects in the model which are, in turn, composed of the states of various components of each of the objects.

So, in a nutshell, what is important to complex event processing is not just processing events, but processing the relative state of objects that comprise model of the complex event.

Furthermore, if you are someone who defines “event” as simply a “change of state,” stay tuned in to the blog for another discussion in a future post; because the vast amount of state changes are not events per se; they are simply changes in states which may or may not have context and meaning in complex event processing.

Having said that, a complex event can be comprised of other events, including other complex events, that is why the notion of OO modelling and programming is very important in CEP.