Compliance Monitoring of Multi-Perspective Declarative Process Models

Abstract

Checking the compliance of a business process execution with respect to a set of regulations is an important issue in several settings. A common way of representing the expected behavior of a process is to describe it as a set of business constraints. Through monitoring facilities, it is possible to continuously determine the state of constraints on the current process execution, and to promptly detect violations at runtime. A plethora of studies has demonstrated that in several settings business constraints can be formalized in terms of temporal logic rules. However, in most of the existing works, the process behavior is mainly modeled in terms of control-flow rules, neglecting other equally important perspectives like data or time. In this paper, we overcome this limitation by presenting a novel monitoring approach based on MP-Declare, a multi-perspective version of the declarative process modeling language Declare. The approach has been implemented in the process mining tool ProM and has been experimented using artificial and real-life event logs.

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