Blockchain technology offers a sizable promise to rethink the way interorganizational business processes are managed because of its potential to realize execution without a central party serving as a single point of trust (and failure). To stimulate research on this promise and the limits thereof, in this article, we outline the challenges and opportunities of blockchain for business process management (BPM). We first reflect how blockchains could be used in the context of the established BPM lifecycle and second how they might become relevant beyond. We conclude our discourse with a summary of seven research directions for investigating the application of blockchain technology in the context of BPM.

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The behavioural comparison of systems is an important concern of software engineering research. For example, the areas of specification discovery and specification mining are concerned with measuring the consistency between a collection of execution traces and a program specification. This problem is also tackled in process mining with the help of measures that describe the quality of a process specification automatically discovered from execution logs. Though various measures have been proposed, it was recently demonstrated that they neither fulfil essential properties, such as monotonicity , nor can they handle infinite behaviour. In this article, we address this research problem by introducing a new framework for the definition of behavioural quotients. We prove that corresponding quotients guarantee desired properties that existing measures have failed to support. We demonstrate the application of the quotients for capturing precision and recall measures between a collection of recorded executions and a system specification. We use a prototypical implementation of these measures to contrast their monotonic assessment with measures that have been defined in prior research.

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Abstract. Companies realize their services by business processes to stay competitive in a dynamic market environment. In particular, they track the current state of the process to detect undesired deviations, to provide customers with predicted remaining durations, and to improve the ability to schedule resources accordingly. In this setting, we propose an approach to predict remaining process execution time, taking into account passed time since the last observed event.While existing approaches update predictions only upon event arrival and subtract elapsed time from the latest predictions, our method also considers expected events that have not yet occurred, resulting in better prediction quality. Moreover, the prediction approach is based on the Petri net formalism and is able to model concurrency appropriately. We present the algorithm and its implementation in ProM and compare its predictive performance to state-of-the-art approaches in simulated experiments and in an industry case study.

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