Lean Information Models for Composite Services

A business process information model (BPIM) is a model that is focused on the business events and the data+documents+messages needed as part of orchestrating the events into business processes by composing services. A central part of the creating the model is to keep the model as small as possible (like a "least common denominator" of the data), only passing the state of the process as part of the events. The data and business documents represent the state of an instance of a business process (what Jack van Hoof calls a dossier) and is all a business process step should need to know to be able to perform its actions.

It is important to recognize that the BPIM does not encompass all information that some part of a business process might ever want or need to know, it just needs to contain enough reference data. Neither does the BPIM have to contain data about business entites across all applications in your enterprise, i.e. the BPIM "customer" does not need to model the union of all the different customer fields from your CRM and ERP systems. This is why BPIM is different from a common information model (CIM) or the EAI Common Data Model [Hohpe/Woolf CDM pattern (355)].

The business process that need to know more about the referenced data of the dossier, will have to use services to look it up, as part of the service composition (orchestration). This is what is called the "pull model" in the Observer pattern [GoF] [Hohpe/Woolf Event Message (151)]. The business process can also augment the process state by calling services and adding data to the dossier. Thus, the mantra of making a slender model is "Ask For More".

Keeping the information model small is very important wrt to maintenance of the model, afterall the main purpose of the model is to mediate semantics in the service bus and its orchestrations of services. The number of mappings between the different elements (messages+dossiers and services) of a model will grow and change over time, thus having small model elements are crucial to keep the model maintainable. In addition, split your model into separate domains/bounded contexts and make context maps, as this makes the total number of mappings smaller.

The key maintenance advice is to keep the model elements small (smaller mappings) and the number of mappings between model elements to a minimum (less mappings).