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.

He’s a bit indignant that so much energy goes to sporting events like the Olympics rather than more important news that isn’t getting reported around the world.

This is in a year when tons of journalists are getting laid off.

This is in a year when there are tons of stories around the world that aren’t getting reported on.

Could we take half of those photographers and send them to Russia, for instance

Reminds me of a feeling I had back in college as an undergrad student studying social sciences and humanities, about the way my friends who were physicists interacted with the world. They were so awed by the stars, Mars, astrophysics, and it seemed to me interesting but altogether unimportant. They argued they may find something outside our planet that could help solve Earth-bound problems like disease, or find the origins of earth and humanity — but really they were doing it because they loved it. One of my friends had a good argument, though — there are enough people right now that we can specialize in what we care about, and there will still be others covering other topics. He could be a physicist and look into the universe’s origin, while I studied social interaction and writing, and our other friends looked into solving cancer or eradicating invasive plants in the native wetlands. We have to specialize, and there are enough of us to do it too.

I think it’s the same way in journalism — whether it’s sports, celebrity journalism, or coverage of politics and war, there are a lot of opportunities right now for journalists. Of course the business model is changing, and some old-schoolers won’t know how to roll with that, but generations change slowly; we’re learning.

Also, the Olympics is seen as more than a sporting event, it’s also a symbol of world competition and cooperation too — a way for countries to come together and share entertainment globally. I think that’s worth covering.

In the second post, Robert Scoble says there are plenty of great journalists but the public doesn’t care. In some ways I have to agree with that, but I don’t think it’s negative, necessarily. I had a conversation with someone the other day about world news reportage. He says, “I was just reading this story, but what does it matter to me if there’s a flood in some city in another country I’ll never visit and some farmer lost his sheep?” World news is only important when it’s relevant, so it’s no wonder that many people don’t care — if they don’t know much about the area, and it doesn’t affect them, they have no incentive to give it full attention. You can call that apathy, but I think it’s an important selectivity skill that humans have. We have to choose what to give priority to, so if nothing stands out as being particularly important, we just ignore it or gloss over it. Human nature…

Also I think the common person today just gets desensitized and doesn’t know where to turn their energy, when surrounded by so many crises. Either you focus on one specialty and do your best to work toward one cause in your life — and maybe that’s just in the course of your daily work — or you become a complete Attention-Deficit-Disorder case and bounce from one problem to the next, without knowing how to solve anything. That just causes a sense of bewilderment, despair, and either that bogs you down or eventually you get desensitized.

There’s a commenter on Scoble’s blog, Spencer, who talks about this generation’s apathy. There are so many people who want to blame today’s generation or the young generation for this “apathy” that they sense. But I see it as a survival mechanism that arises from the way information flows these days. We’re surrounded by crises, everyone wants us to know about them — the water shortage, global warming, death in Iraq, the national deficit. Okay, crisis, I get it. But no one gives a real clear idea on what any individual is really supposed to do to solve the problem. You can’t get involved with one global cause, without ignoring all the others, and if you do get involved it’s likely to become your life’s purpose. Most people are concerned with other things — their families, their work, personal development, their homes and futures, and really that’s enough to take up all their time.

I’m always amazed when I read about the early unionists. Emma Goldman for example, the activist who pushed for the 8-hr workday, and campaigned for free love in the early 1900s when women were still wearing corsets, used to work 16 hour factory days as a seamstress, then lead meetings late into the night. Today we lead cushy lives comparatively–8 hour days, plus commute and lunch, family time, dinner time, gym maybe, sleep… but it still doesn’t seem like we ever have enough energy and time.

What Emma had that most people today don’t, is a community living in the same conditions as herself, with clear goals about what they were campaigning for, and a cause that affected their own daily lives. Today, unionism and local activism is in much shorter supply, in part due to the many people who work fairly comfy desk jobs, and the problem that everyone has his own specialization, works in a cubicle, does his or her own thing. The problems we’re facing today in terms of global warming, global water shortage, aren’t the same kinds of problems that activists have fought for in the past, and there’s no clear road map for how to solve them. Our leaders sure aren’t leading the way.

What we do have, at least, is the Olympics, which is an age old symbol of international cooperation, play and competition…so, uh, go sports! As for full disclosure, I don’t actually have a TV and haven’t watched the Olympics in many years, but I do try taking short showers–does that help?

This use case is a clear example of a subfunction of complex event processing, folks in the mult-sensor data fusion field (and here at The CEP Blog) refer to as event (object) refinement, sometimes called “track and trace.”

The reason we call this processing function “event (object) refinement” is that, in the way the use case was described in the press release, the medical staff are basically tracking body temperature and comparing it to a key indicator to generate an alarm, in this case “body temperature too high.”   This is a simple event, not complex, because the level of inference is quite very low in an overall knowledge hierarchy.

For example, we cannot infer from the alarm that “body temperature too high” is caused by a previous medical condition.  There is no causality at this stage of the game.   We cannot infer from the alarm that the walker has embarked up a steep hill, and the body temperature is expected to exceed a key indicator for a period of time.

Looking at another complex event model,  the system does not (yet) combine all of the body temperatures of the entire group of walkers, correlated by the situation of an approaching thunderstorm, and infer that the walkers have increased their pace because they don’t want to be caught in a driving rainstorm with high winds.

In other words, tracking a single object like “body temperature” is a basic-step in a CEP application, but not really a CEP application yet, because to really be a complex event, there should be some inference of higher knowledge, or estimated situation.    For example, tracking and tracing the position of an aircraft is good data, but being able to infer the complex situation “potential mid-air crash” between two airplanes is better (defining a complex event vs simply tracking state changes).

Steam processing engines are well suited for track and trace processing of individual event objects, like a walker’s body temperature, or a similar temperature monitoring application from a network device, as demonstrated by the Apama use case.  Tracking events such as “temperature in an object reaches critical threshold” have been going on for decades, in your network, in your car,  in your washing machine, in as spacecraft, just about everywhere we sense-and-respond to temperature changes.

The real marvel of this application was not the event processing on the back end, but in the sensor network, comprised of the human body, an RFID sensor, and a transmission network to a centeralized data collection facility.