Subjects covered will include forces, trigonometry, fractals, cellular automata, self-organization, and genetic algorithms. Examples will be demonstrated using Processing with a focus on object oriented programming.
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!
Rule-processing is just a style of computation. Of course it is used in BRMS, but it is also used in CEP. CEP systems typically employ rules-based processing to infer higher-order events by matching patterns across many event streams within the event ‘cloud’. BRMS’s use rule processing to match patterns within data tuples representing business-orientated data. CEP systems may support the use of advanced analytics to manage predictive analysis, reasoning under uncertainty and other requirements in relation to the event cloud. Some of the better BRMS’s offer similar analytics in regard to processing business data.
The one, really big, difference between Complex Event Processing and traditional BRMS tools is that the former is loosely associated with EDA and decisions that are based on multiple events, whereas the latter is more associated with conventional request-reply SOA and automating decisions made in managed business processes.
Function answers the question --- what is being done?Technique answers the question -- how something being done?
Application answers the question --- what is the problem being solved?
ExamplesBusiness Activity Monitoring (BAM) is an application type, it solves the problem of controlling the business activities in order to optimize the business, deal with exceptions etc...Business Rules are type of technique --- which can be used to infer facts from other facts or rules (inference rules) , or to determine action when event occurs and condition is satisfied (ECA rules) and more (there are at least half a dozen types of rules, which are techniques to do something).Event Processing is really a set of functions which does what the name indicates -- process events --- processing can be filtering, transforming, enriching, routing, detect patterns, deriving and some more.
JT has posted his view on rules and decisions and how they relate. Given that James talks more about services than events, I thought it would be worth reviewing his post from both a Complex Event Processing and a TIBCO BusinessEvents event processing platform perspective.
”Decision Services:
Support business processes by making the business decisions that allow a process to continue.
Support event processing systems by adding business decisions to event correlation decisions (they are often called Decision Agents in this context).
Allow crucial and high-maintenance parts of legacy enterprise applications to be externalized for reuse and agility.
Can be plugged into a variety of systems using Enterprise Service Bus approaches.”
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.
Some definitions key on the question of the probability of encountering a given condition of a system once characteristics of the system are specified. Warren Weaver has posited that the complexity of a particular system is the degree of difficulty in predicting the properties of the system if the properties of the system’s parts are given. In Weaver’s view, complexity comes in two forms: disorganized complexity, and organized complexity. [2] Weaver’s paper has influenced contemporary thinking about complexity. [3]
The main characteristic to be aware of in these tools is that BE is primarily rule-based (using an embedded rule engine), whereas BW and iProcess are orchestration / flow engines. In BE we can use a state diagram to indicate a sequence of states which may define what process / rules apply, but this is really just another way of specifying a particular type of rules (i.e. state transition rules).
The main advantages to specifying behavior as declarative rules are:
Handling complex, event-driven behavior and choreography
Iterative development, rule-by-rule
The main advantages of flow diagrams and BPMN-type models are:
Ease of understanding (especially for simpler process routes)
Process paths are pre-determined and therefore deemed guaranteeable.
In combination these tools provide many of the IT capabilities required in an organization. For example, a business automation task uses BW to consolidate information from multiple existing sources, with human business processes for tasks such as process exceptions managed by iProcess. BE is used to consolidate (complex) events from systems to provide business information, or feed into or drive both BW and iProcess, and also monitors end-to-end system and case performance.
Free or low-cost sources of unstructured information, such as Internet news and online discussion sites, provide detailed local and near real-time data on disease outbreaks, even in countries that lack traditional public health surveillance. To improve public health surveillance and, ultimately, interventions, we examined 3 primary systems that process event-based outbreak information: Global Public Health Intelligence Network, HealthMap, and EpiSPIDER. Despite similarities among them, these systems are highly complementary because they monitor different data types, rely on varying levels of automation and human analysis, and distribute distinct information. Future development should focus on linking these systems more closely to public health practitioners in the field and establishing collaborative networks for alert verification and dissemination. Such development would further establish event-based monitoring as an invaluable public health resource that provides critical context and an alternative to traditional indicator-based outbreak reporting.