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Because all service consumers are dependent on this structure, frequent changes to the interface can result in significant rework for everyone consumers as well as code changes in the rule service itself. Additionally, assisting each user-interface change introduces change dependency between all consumers and the service. There are ways to avoid this complexity by introducing more versatility in the service interface. However, each technique includes its own set of tradeoffs that must definitely be carefully considered.

There are three basic methods. To illustrate, envision a guideline service that determines if an ongoing company will accept a deal from a US customer. In the initial & most simple version, the business policy is to refuse a transaction from any customer under age 21. Age is the only decision criteria. In a future version of the same service, the decision criteria becomes more technical when it’s established that the threshold age group should vary based on the customer’s US condition of the home. In this approach the guidelines-service interface includes only the data required by the service to execute the implemented decision point.

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The structure of the data in the user interface is flattened with regards to the decision-point implementation and attribute titles represent the business decision requirements. The objects in the user interface can be used directly in the guideline BOM and the default verbalizations is going to be sufficient. No additional mapping of data is necessary. Marshaling/de-marshaling and transportation overhead is held to a minimum allowing very speedy service response. REST implementation becomes a viable option with a small quantity of specific input attributes related to your choice and low transportation overhead. This approach is fast to implement, execute, and easy to change in the initial development, but is the least versatile to future change deployed once.

Any future additional data requirement in the decision point will require an interface upgrade to support the change. This technique is best used to quickly develop a guideline service with speedy changes to the decision criteria while still in development. Inside our example to recognize customers below age 21, the user interface initially may only consist of a person identifier and the customer’s birthday. However our future implementation that observes the customer’s US State of residence will require a service interface change to add this attribute. Concurrent execution of old and new variations of the guideline service requires rigorous versioning of the WDSL and therefore the rule app / rule set.

In this process, the guideline service interface includes the entire business data set that may be used to perform the implemented decision point, even if most of the data is unobserved in the initial rule implementation. Quite simply, the caller passes everything it currently knows about the subject of the decision in the event future rules may need additional information.

This approach provides a more extensive data interchange but is limited by the quality of the info model. This process is mostly used in combination with vertical industries which have a well-defined and broadly accepted object model such as those described by standards systems such as ACORD, MISMO, EDI Standard formats, HL7, et al. The objects in the user interface typically contain several levels of hierarchical maps and data poorly to a flatter, business-oriented BOM attractive in business rule implementations. This commonly necessitates at the very least custom BOM verbalizations, and frequently mapping of the user interface object to a flatter and less normalized form; all increasing the right time and effort in rule implementation.

However, the benefit is that future rules are less inclined to require a user-interface change IF the business data model is complete, steady, and universally accepted. Because the interface is typically large, in the entire case of most SOAP web services and message-based architectures, marshaling/de-marshaling, transportation overhead, and remapping become the overriding performance constraints rather than rule-execution times. Using our example rule service implementing a choice point to identify customers below age 21, the original interface would consist of everything about a Customer tracked in the enterprise data model. This tends to include the customer’s birthday allowing age group to be produced.