Improving conformance checking in process modelling: a multiperspective algorithm

Business process model and notation (BPMN) is a popular notation used for process modelling mainly due to its high expressiveness. However, BPMN has shortcomings when dealing with specific domains (namely Hazard Analysis and Critical Control Points systems), struggling to model activity duration, quality control points, activity effects and monitoring nature. To tackle these limitations, the business process model and notation extended expressiveness (BPMN-E-2) was proposed. In this paper, a multiperspective conformance checking algorithm is developed focusing on detecting non-conformity between an event log and a process model, regarding the information provided by the new elements within BPMN-E-2. The proposed algorithm follows a two-step approach that starts by converting the model into a directly follows model (annotated with conformance rules), which is then used in a second phase to perform conformance checking effectively. This modular approach allows to apply the proposed algorithm to other process model notations than BPMN-E-2. An event log clustering technique was also developed to downsize large-event logs without compromising data relevance. In this way, both the multiperspective algorithm and the log-downsize clustering technique here proposed are a key contribution to improve conformance checking in process modelling, as evinced in the proof-of-concept provided.

Automated summary

BPMN-E-2 was proposed to address the limitations of BPMN in specific domains. A multiperspective conformance checking algorithm and an event log clustering technique were developed to improve the accuracy and efficiency of conformance checking in process modelling. These contributions are significant for enhancing the conformance checking process.