Companies often specify the intended behaviour of their business processes in a process model. Conformance checking techniques allow us to assess to what degree such process models and corresponding process execution data correspond to one another. In recent years, alignments have proven extremely useful for calculating conformance checking statistics. Existing techniques to compute alignments have been developed to be used in an offline, a posteriori setting. However, we are often interested in observing deviations at the moment they occur, rather than days, weeks or even months later. Hence, we need techniques that enable us to perform conformance checking in an online setting. In this paper, we present a novel approach to incrementally compute prefix-alignments, paving the way for real-time online conformance checking. Our experiments show that the reuse of previously computed prefix-alignments enhances memory efficiency, whilst preserving prefix-alignment optimality. Moreover, we show that, in case of computing approximate prefix-alignments, there is a clear trade-off between memory efficiency and approximation error.

Over the past decade process mining has emerged as a new analytical discipline able to answer a variety of questions based on event data. Event logs have a very particular structure; events have timestamps, refer to activities and resources, and need to be correlated to form process instances. Process mining results tend to be very different from classical data mining results, e.g., process discovery may yield end-to-end process models capturing different perspectives rather than decision trees or frequent patterns. A process-mining tool like ProM provides hundreds of different process mining techniques ranging from discovery and conformance checking to filtering and prediction. Typically, a combination of techniques is needed and, for every step, there are different techniques that may be very sensitive to parameter settings. Moreover, event logs may be huge and may need to be decomposed and distributed for analysis. These aspects make it very cumbersome to analyze event logs manually. Process mining should be repeatable and automated. Therefore, we propose a framework to support the analysis of process mining workflows. Existing scientific workflow systems and data mining tools are not tailored towards process mining and the artifacts used for analysis (process models and event logs). This paper structures the basic building blocks needed for process mining and describes various analysis scenarios. Based on these requirements we implemented RapidProM, a tool supporting scientific workflows for process mining. Examples illustrating the different scenarios are provided to show the feasibility of the approach.B Wil M. P. van der Aalst

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