Event-driven SOA

The prototypical SOA 2.0 paradigm contains four essential elements:

1. multiple low-level system events that, separately, do not appear to have any relationship, but through pattern detection by comparing these many events some unusual or less obvious correlation becomes clear;
2. some amount of data enrichment by infusion of related information to each event to more clearly illustrate how the many events are related;
3. a trigger condition which when not met, the business-level event is not created, but when the trigger condition is met, the higher-level business event is created;
4. some human or automated process that is invoked when the trigger event is reached.

SOA 2.0 Web Services can be composed in two ways: orchestration and choreography. In orchestration, a central process takes control over the involved web services and coordinates the execution of different operations on the web services involved in the operation. The involved SOA 2.0 services do not know (and do not need to know) that they are part of a composition or a higher business process. Only the central coordinator of the orchestration knows this, so the orchestration is centralized with explicit definitions of operations and the order of invocation of SOA 2.0 services.

Choreography on the other hand does not rely on a central coordinator. Rather, each SOA 2.0 service involved in the choreography knows exactly when to execute its operations (based on defined trigger criteria) and whom to interact with. Choreography is a collaborative effort focused on exchange of messages. All participants of the choreography need to be aware of the business process, operations to execute, messages to exchange, and the timing of message exchanges.

BPEL follows the orchestration paradigm. Choreography is covered by other standards, such as WSCI (Web Services Choreography Interface) and WS-CDL (Web Services Choreography Description Language).

Causal relationships are inherent in the world around us and are intrinsic to our decision making. The human intelligence processes and gathers these relationships faster than current artificial computational capability can. One of the fundamental obstacles in artificial intelligence is the absence of an automated ability to relate events together as when a human uses human intuition.

Using a Causal Vector Engine, the perception of causality can be enhanced under appropriate spatiotemporal conditions based on structural and temporal rules written into the engine. Perception of complex causal semantics, such as additive, mediated, and bidirectional causalities need to be coded so that the engine can distinguish between events that are related and those that only appear to be related but, in fact, are not.

The engine uses preponderant causal vector rate-of-change propagation to code the relationship among the events and establishes a partial order in which it validates the causality perceived between multiple occurrences. The engine plays and replays the event sequence in different temporal order to infer what could be related topological connections and compares these replays to rules preprogrammed by an analyst.

Multiple low-level system events are processed by the Causal Vector Engine and compared against these rules to trigger higher-level Business Events. It does this through a Causality Vector Engine (CVE) console application which displays events in real-time to business analysts. Where streams of events can be observed as they occur, much like a stock ticker, the CVE console app has several windows that list the same events in different contexts, so the business analysts can see what the CVE is doing with the relationships between them.

The Sequential window shows events in date-timestamp order, one or more other windows in various orders as the CVE works through the list of rules and creates implied relationships between the events. Various buttons and controls exist in the console application that enable the business analysts to create relationships between events on-the-fly and define rules that respond to these relationships.

Business analysts can infuse additional defining detail through an SQL query statement attached to a rule or event context. The CVE app works much like a modern-day stock trading application that mutual funds managers use to manage risk. An example of a CVE application and engine can be seen in SILK.[2]

Most enterprise service bus (ESB) implementations contain a facility called "mediation". For example, mediation flows are part of the WebSphere enterprise service bus intercept. Mule also supports mediation flows. Mediation flows modify messages that are passed between existing services and clients that use those services. A mediation flow mediates or intervenes to provide functions, such as message logging, data transformation, and routing, typically the functions can be implemented using the Interception Design Pattern.[3]

As messages pass through the ESB, the ESB enriches the messages destined for a channel that is monitoring for a high-level business event. That is, for each message, the ESB may query a database to obtain additional information about some data entity within the message. For example, based on Customer ID, the ESB mediation flow could get the zip code that the customer resides in. Or, based on IP address of the originating request by the end-user, the ESB mediation flow could lookup what country, state or county that IP address is in.

These examples represent data enrichment, the concept of adding additional value to existing data, based on the intent of the high-level business event to eventually be triggered.

An ESB mediation flow is one of the component types in a Service Component Architecture (SCA). Like any SCA component, the program accesses a mediation flow through exports that it provides, and the mediation flow forwards messages to other external services via imports. Special kinds of imports and exports for JMS, called JMS bindings, enable developers to specify the binding configuration and write data handling code. The mediation flow consists of a series of mediation primitives that manipulate messages as they flow through the bus.

Once the developers have coded the custom binding for both export and import, they can start to focus on the mediation flow component. In the WebSphere Integration Developer assembly editor, this is done by the JMS Custom Binding Mediation Component where each operation on the flow component's interface is represented by a request and a response.

Service Data Objects (SDO) framework provides a unified framework for data application development. With SDO, developers do not need to be familiar with any specific API in order to access and utilize data. Through SDO, developers simply work with data from multiple data sources, such as relational databases, entity EJB components, XML pages, Web services, the Service Component Architecture, and JavaServer Pages pages.

Mediation flows are entirely independent from the bindings that are used in the imports and exports. In fact, the purpose of having a conversion into an SDO DataObject instance outside of the flow implementation is because mediation flows can then be built without knowledge of the protocol and format with which messages are sent to and from the mediation module.

A business-level trigger condition enables the SOA 2.0 architecture to establish real-time customer intelligence, marketing automation and customer loyalty solutions, among other features. Business objects model real-world entities in the architecture such as customers, accounts, loans, and travel itineraries. When the state of one of these objects changes, and a monitoring agent notices this change is significant (when compared to the list of criteria to monitor), an event is created and passed to other monitoring agents.

For example, the detection of an actual business problem or opportunity could lead to increased revenue. If a customer cancels an order, extra manufacturing capacity could reduce the profitability of the production run. A SOA 2.0 event could notify marketing department to create a special sales campaign that would resell the excess capacity, thereby recapturing the original profitable cost-per-unit.

Automatic monitoring of events in operational business process activities as processes execute to see if any immediate action needs to be taken either inside or outside the enterprise. These monitoring agents continually test for specific business conditions and changes in business operations. If necessary, the agents alert people, make recommendations, send messages to other applications or invoke whole business processes when such conditions or changes occur..

A triggered business process should directly support revenue growth with cost containment, responsiveness to business conditions, or ability to pursue new market opportunities. Resulting business processes could also measure operational progress toward achieving goals, control operational costs by communicating just what is needed to just who needs to know, or report performance status of key processes to key decision makers.

For example, you could construct a CRM event from an "abandoned shopping cart" message (parsing the transaction, customer ID, and time), using other filters to extract the value of goods in the cart and tapping the correlation capabilities of the system to add causal indicators such as whether the commerce site was suffering performance problems. Your CRM event might also include customer value or rank from the customer database...

For another example, based on the types of independent service calls received, the SOA 2.0 platform could identify a product defect by detecting the underlying pattern of the separate complaints, then triggering an alert to engineering or production of the possible defect.

Domain algorithms, such as fever/flu/infection domain logic in the cited reference, are used to derive SQL code that applies the selected business rules to the use case. Using CVEs in SOA environments improve business agility because the application of SOA 2.0 principles identify business opportunities that would have otherwise been missed or identified much later.[6]

Functional magnetic resonance imaging (fMRI) using Granger causality analysis (GCA) detects causal effects among brain regions. The results of one sample test demonstrated positive causal effect between rFIC and the dorsal anterior cingulate cortex (dACC).[7]

The Oracle CVE Analytical Engine uses a set of theoretical models, each of which evaluates some or all of the data. When a business analyst configures causal factors, he/she specifies criteria indicating which models should consider which causal factor.[8]