Conformance checking: A state-of-the-art literature review

Conformance checking is a set of process mining functions that compare process instances with a given process model. It identifies deviations between the process instances' actual behaviour ("as-is") and its modelled behaviour ("to-be"). Especially in the context of analyzing compliance in organizations, it is currently gaining momentum -- e.g. for auditors. Researchers have proposed a variety of conformance checking techniques that are geared towards certain process model notations or specific applications such as process model evaluation. This article reviews a set of conformance checking techniques described in 37 scholarly publications. It classifies the techniques along the dimensions "modelling language", "algorithm type", "quality metric", and "perspective" using a concept matrix so that the techniques can be better accessed by practitioners and researchers. The matrix highlights the dimensions where extant research concentrates and where blind spots exist. For instance, process miners use declarative process modelling languages often, but applications in conformance checking are rare. Likewise, process mining can investigate process roles or process metrics such as duration, but conformance checking techniques narrow on analyzing control-flow. Future research may construct techniques that support these neglected approaches to conformance checking

Modeling Platform Ecosystems

Platforms facilitate the creation of complementary modulesby third parties and act as intermediaries between different groups of ac-tors. Due to the high degree of collaboration between actors, ecosystemsevolve around such platforms. As a result of digitalization, platform busi-ness models are becoming viable in more and more domains. Despite theincreasing variety of different platform ecosystems, means to clearly spec-ify them are scarce. This conceptual ambiguity impedes comparabilityof research and knowledge accumulation. To solve this problem, we pro-pose a domain-specific platform ecosystems modeling language (PEML)which builds on seminal platform ecosystem literature. We demonstratePEML by modeling two real-life platform ecosystems based on onlinecase studies. Our results support researchers and practitioners alike inclearly specifying platform ecosystems

Design Principles for Comprehensible Process Discovery in Process Mining

“Spaghetti-like” process models discovered through process mining are challenging to comprehend, especially, for inexperienced users. But, at the same time, they contain potential insights for decisionmakers. Designing process discovery techniques that work well in both aspects – being comprehensible and providing valuable information, for various data sets – is a challenging task in process mining. Therefore, we adopt metrics from various disciplines such as information theory, business process modelling, process mining, graph aesthetics, and cognitive load theory to define design principles for process discovery techniques in regards to model characteristics and visual layout principles. Each of the design principles includes a metric and reference value to ensure their testability and to provide quantitative orientation to designers. To assure that model comprehensibility does not come at the cost of losing essential information, we introduce an entropy-based measure as a boundary condition that expresses the amount of information a model encodes. We assess the effectiveness of the design principles in terms of their applicability in an experimental evaluation with synthetic and real-world event log data

Predictive Business Process Deviation Monitoring

Business processes run at the core of an organisation\u27s value creation and are often the target of optimisation efforts. Organisations aim at adhering to their optimised processes. However, deviations from the optimised process still occur and may potentially impede efficiency in process executions. Conformance checking can provide valuable insights regarding past process deviations, but it cannot identify deviations before they occur. Outcome-oriented predictive business process monitoring (PBPM) provides a set of methods to predict process outcomes, e.g., key performance indicators. We propose an outcome-oriented PBPM method for predictive deviation monitoring using conformance checking and deep learning to draw the most out of the two domains. By leveraging early intervention, the method supports the proactive handling of deviations, i.e., inserted and missing events in process instances, to reduce their potential harm. Our evaluation shows that the method can predict business process deviations with high predictive quality, particularly for processes with fewer variants