Abstract
Process mining uses event logs to learn and reason about business process models. Existing algorithms for mining the control-flow of processes in general do not take into account the probabilistic nature of the underlying process, which affects the behaviour of algorithms and the amount of data needed for confidence in mining. We contribute a first step towards a novel probabilistic framework within which to talk about approaches to process mining, and apply it to the well-known Alpha Algorithm. We show that knowledge of model structures and algorithm behaviour can be used to predict the number of traces needed for mining.
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Weber, P., Bordbar, B., Tiňo, P. (2011). A Principled Approach to the Analysis of Process Mining Algorithms. In: Yin, H., Wang, W., Rayward-Smith, V. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2011. IDEAL 2011. Lecture Notes in Computer Science, vol 6936. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23878-9_56
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DOI: https://doi.org/10.1007/978-3-642-23878-9_56
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