Abstract
Organizations maintain process models that describe or prescribe how cases (e.g., orders) are handled. However, reality may not agree with what is modeled. Conformance checking techniques reveal and diagnose differences between the behavior that is modeled and what is observed. Existing conformance checking approaches tend to focus on the control-flow in a process, while abstracting from data dependencies, resource assignments, and time constraints. Even in those situations when other perspectives are considered, the control-flow is aligned first, i.e., priority is given to this perspective. Data dependencies, resource assignments, and time constraints are only considered as second-class citizens, which may lead to misleading conformance diagnostics. For example, a data attribute may provide strong evidence that the wrong activity was executed. Existing techniques will still diagnose the data-flow as deviating, whereas our approach will indeed point out that the control-flow is deviating. In this paper, a novel algorithm is proposed that balances the deviations with respect to all these perspectives based on a customizable cost function. Evaluations using both synthetic and real data sets show that a multi-perspective approach is indeed feasible and may help to circumvent misleading results as generated by classical single-perspective or staged approaches.
Highlights
The practical relevance of process mining is on the rise as event data is readily available due to advances in data monitoring and storage
We address the potential concern that a multi-dimensional approach may not be feasible to apply to real-life event logs, considering the longer computations that are required compared to a single-perspective approach
We compare results returned by the balanced approach with those returned by the non-balanced one from [15]
Summary
The practical relevance of process mining is on the rise as event data is readily available due to advances in data monitoring and storage. This paper focuses on conformance checking while considering multiple perspectives (i.e. control-flow, data, resources, time) at the same time. Deviations identified using conformance checking may, for example, point at users in a process using undesirable workarounds, activities that are often executed too late for a particular group of customers, or violations of the four-eyes principle for cases that follow a particular path. Up to this point, conformance checking techniques have almost exclusively focused on the control-flow perspective [1,9,26]. It refers to the value after the occurrence of t
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