Business Process Management is a boundaryspanning discipline that aligns operational capabilities and technology to design and manage business processes. The Digital Transformation has enabled human actors, information systems, and smart products to interact with each other via multiple digital channels. The emergence of this hyper-connected world greatly leverages the prospects of business processes -but also boosts their complexity to a new level. We need to discuss how the BPM discipline can find new ways for identifying, analyzing, designing, implementing, executing, and monitoring business processes. In this research note, selected transformative trends are explored and their impact on current theories and IT artifacts in the BPM discipline is discussed to stimulate transformative thinking and prospective research in this field.

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Robotic Process Automation (RPA) is a fast-emerging process automation technology suited for high-volume, repetitive, and rule-based tasks. The promises of rising RPA vendors and the lack of documented track records leave researchers and practitioners with the challenge of positioning the term and assessing RPA's true potential. To objectively discuss the strengths and weaknesses of this technology, we conduct a literature review, a practical implementation of an RPA solution, and an interview with an industry expert. We reveal that the current literature primarily focuses on economic factors. This paper, therefore, adds various social and technical aspects to the discussion. Most importantly, robustness and stability pose technical challenges for successfully implementing RPA. Further research directed at error handling and maintenance of software robots is required to support the successful implementation of RPA.

Predictive business process monitoring (PBPM) deals with predicting a process's future behavior based on historical event logs to support a process's execution. Many of the recent techniques utilize a machine-learned model to predict which event type is the next most likely. Beyond PBPM, prescriptive BPM aims at finding optimal actions based on considering relevant key performance indicators. Existing techniques are geared towards the outcome prediction and deal with alarms for interventions or interventions that do not represent process events. In this paper, we argue that the next event prediction is insufficient for practitioners. Accordingly, this research-in-progress paper proposes a technique for determining next best actions that represent process events. We conducted an intermediate evaluation to test the usefulness and the quality of our technique compared to the most frequently cited technique for predicting next events. The results show a higher usefulness for process participants than a next most likely event.

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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 ("asis") 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. CCS CONCEPTS • Applied computing → Business process management; Business process modeling; Business intelligence; • Information systems → Data mining.

We investigate the renormalizability and the gauge (in)dependence of a pure Yang-Mills theory in a class of linear gauges interpolating between covariant and noncovariant ones.