On Event Processing Agents implies a “new” event processing reference architecture with terms like,
(1) simple event processing agents for filtering and routing,
(2) mediated event processing agents for event enrichment, transformation, validation,
(3) complex event processing agents for pattern detection, and
(4) intelligent event processing agents for prediction, decisions.
Frankly, while I generally agree with the concepts, I think the terms in On Event Processing Agents tend to add to the confusion because these concepts in On Event Processing Agents are following, almost exactly, the same reference architecture (and terms) for MSDF, illustrated again below to aid the reader.
The success of Service-Oriented Architecture (SOA) has created the foundation for information
and service sharing across application and organizational boundaries. Through the use of SOA,
organizations are demanding solutions that provide vast scalability, increased reusability of
business services, and greater efficiency of computing resources. More importantly,
organizations need agile architectures that can adapt to rapidly changing business requirements
without the long development cycles that are typically associated with these efforts. Event-Driven
Architecture (EDA) has emerged to provide more sophisticated capabilities that address these
dynamic environments. EDA enables business agility by empowering software engineers with
complex processing techniques to develop substantial functionality in days or weeks rather than
months or years. As a result, EDA is positioned to enhance the business value of SOA.
The purpose of this white paper is to describe the approach employed to overcome the significant
technical challenges required to design a dynamic grid computing architecture for a US
government program. The program required optimization of the overall business process while
maximizing scalability to support dramatic increases in throughput. To realize this goal, an
architecture was developed to support the dynamic placement and removal of business services
across the enterprise.
Multiple channels and types of events…
… executing in multiple Inference Agents (Event Processing Agents on an Event Processing Network)…
… where Events drive Production Rules with associated (shared) data…
… and event patterns (complex events) are derived from the simple events and also drive Production Rules via inferencing…
… to lead to “real-time” decisions.
Rob sees three key areas where rules can help:
Tighter warranty controls
Claims processing is improved because financial limits, detailed coverage types, materials return and more can be automated and rapidly changed when necessary. The rules also allow “what-if” testing and impact analysis.
Better built vehicles
The decision making is tracked very closely thanks to rules so you can analyze specific repair types, specific VINs and so on. More effective parts return and generally better information also contribute.
Lower cost repairs
Rules allow goodwill repairs, labor-only repairs and specific kinds of repairs to be managed very precisely. Rules-driven decisioning can reduce the variation of costs between dealers and help intervene, rejecting or editing claims that seem overly expensive. The ability of rules to deploy data mining and predictive analytics can also really help here.
- leave anything related to transport, communication to other layers- use this revised CEP to express and execute event-relevant logic, the purpose of which is to translate the ambient events into relevant business events- have these business events trigger business processes (however lightweight you want to make them)- have these business processes invoke decision services implemented through decision management to decide what they should be doing at every step- have the business processes invoke action services to execute the actions decided by the decision services- all the while generating business events or ambient events- etc.
Truviso continuously analyzes massive volumes of dynamic information—providing comprehensive visibility and actionable insights for any event, opportunity or trend on-demand. Truviso empowers decision-makers with continuous:
Analysis - always-on, game-changing analytics of streaming data
Visibility - dynamic, web-based dashboards and on-demand applications
Action - extensible, data-driven actions and alerts
Truviso's approach is based on years of pioneering research, leveraging industry standards, so it's fast to implement, flexible to configure, and easy to modify as your needs change over time.
Most BREs today are deployed as “decision services”, and are used in “stateless” transactions to make “decisions” as a part of a business process. A CEP application is instead processing multiple event streams and sources over time, which requires a “stateful” rule service optimized for long running. This is an important distinction, as a stateful BRE for long-running processes needs to have failover support - the ability to cache its working memory for application restarting or distribution. And of course long-running processes need to be very particular over issues like memory handling - no memory leaks allowed!