Pattern 10 (Collapse)

FLASH animation of Collapse pattern


Collapse (aka Aggregation) describes features to synthesize multiple model elements into a single one of more abstract nature, where the distinction among the constituent elements is no longer relevant. It implies information synthesis.


To simplify a process model for a specific audience.


Fig. 9b shows an example of Collapse between single control-flow elements from the model in Fig. 3b. Here tasks Check debts and Check liability, and all tasks in the lower path of the XOR-split, have been collapsed into a single task for performing the required checks, resp., a single task for approving and processing loans.


Decreases model size, and may also decrease diameter and average connector degree.


Simpler process models which focus on specific aspects relevant to a given audience are easier to understand by the latter [107].


[109] proposes an algorithm to incrementally simplify a process model by applying a set of reduction rules via which nodes with low relevance are gradually collapsed to their predecessor nodes. The order of the collapsed elements can be stored should the user wish to reverse the collapse effects. [36] uses a combination of Omission and Collapse to derive models for cross-organization collaboration. The approach contains two steps: aggregation and customization. In the first step, the provider selects those internal tasks that they wish to hide to a specific customer, and these tasks are collapsed accordingly. Afterwards, the customer specifies the tasks they are interested in and the remaining tasks are either collapsed with each other, or omitted from the model. Similarly, the Proviado approach [19], [20] applies a combi- nation of graph reduction (Omission) and graph aggregation (Collapse) techniques to obtain customized process views, based on a user query, e.g. "Show the tasks of a particular user only". Attribute values of aggregated tasks, e.g. data information, are also collapsed – a feature that is not available in other approaches, which only focus on control-flow. Process views of workflow models are also obtained in [73] by collapsing connected sets of tasks. The approach in [93], [94] proposes an algorithm for abstracting process models. It uses the model's RPST decomposition to identify fragments that are structurally suitable for collapse. Then transformation rules are applied to produce an abstracted fragment by collapsing its tasks. This algorithm lifts the limitations of another abstraction algorithm from the same authors [92]. The latter work aimed to collapse those tasks that are observed less often based on the identification of abstraction patterns (dead-end, sequential, block, and loop abstractions). As such, it was limited by the occurrence of these patterns in a model. [108] proposes an approach based on the notion of behavioral profiles [119]. A behavioral profile describing the behavior of a process model in terms of task order relations if first inferred from a process model. Then this profile is used together with an user-defined grouping of tasks to produce an abstract process model where selected tasks are collapsed based on the defined grouping of tasks. The degree of collapse is determined by the user. Collapse has also been applied in process mining [1]. [49] presents an algorithm in which both Collapse and Omission are used to identify non-essential details when extracting process models from logs. The algorithm applies a number of metrics (derivable from the logs) to establish the significance of and correlation between model elements, according to which process models are either collapsed or omitted.