  Within process mining, a relevant activity is conformance checking. Such
activity consists of establishing the extent to which actual executions of a
process conform the expected behavior of a reference model. Current techniques
focus on prescriptive models of the control-flow as references. In certain
scenarios, however, a prescriptive model might not be available and,
additionally, the control-flow perspective might not be ideal for this purpose.
This paper tackles these two problems by suggesting a conformance approach that
uses a descriptive model (i.e., a pattern of the observed behavior over a
certain amount of time) which is not necessarily referring to the control-flow
(e.g., it can be based on the social network of handover of work).
Additionally, the entire approach can work both offline and online, thus
providing feedback in real time. The approach, which is implemented in ProM,
has been tested and results from 3 experiments with real world as well as
synthetic data are reported.
