The AristaFlow meta model as illustrated in Fig. 1 does not look very fancy at first glance. However, its “ingredients” were carefully selected and complement each other. Thus the process meta model is very helpful with respect to formal verification, ad-hoc changes, and process schema evolution.

 

 

Figure 1: AristaFlow Process Meta Model

The strength of the AristaFlow meta model is the underlying theory which supports both correctness by construction and efficient consistency checks. This theory precisely defines correctness criteria for the AristaFlow meta model (e.g., absence of deadlocks, no isolated nodes, all data flows correct under all possible executions). It further defines a complete set of process change operations with pre-/post-conditions which ensure that, if the desired change satisfies the pre-conditions, the resulting process schema will again be correct.

The AristaFlow change operations, for example, allow to serially insert an activity between two process nodes, to insert it in parallel or between two process node sets, to move activities, to delete activities, and so forth.All these operations obey that data flow correctness is not violated.

Another important property of the AristaFlow process meta model is that it incorporates not only the information on the current state at the instance level, but also information on how this state was reached. This allows to quickly decide whether a desired ad-hoc change can be granted or whether it affects an already passed region of the process instance. The latter could (among other things) cause data flow problems and is therefore prohibited (with some exceptions concerning loops).

The block structuring of the process meta model has been motivated by three aspects: First, experiments have shown that they are easier to handle and to understand for users when compared to unstructured process models. Second, it allows to restrict the area in the graph which has to be analyzed in the context of ad-hoc changes. This, in turn, helps to speed up the required analyses.Third, it significantly simplifies the resulting structural adaptations of the process schema.