Phil Weber

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—There are many process mining algorithms and representations, making it difficult to choose which algorithm to use or compare results. Process mining is essentially a machine learning task, but little work as been done on systematically analysing algorithms to understand their fundamental properties, such as how much data is needed for confidence in(More)
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(More)
—Noise is a challenge for process mining algorithms, but there is no standard definition of noise nor accepted way to quantify it. This means it is not possible to mine with confidence from event logs which may not record the underlying process correctly. We discuss one way of thinking about noise in process mining. We consider mining from a 'noisy log' as(More)
Process Mining Process mining [1] is the discovery and analysis of models of business processes, from event logs, often represented by Petri nets (PN). Process mining is used to understand, for example: what activities, resources are involved, and how are they related? what affects performance, what decision rules control process flow? predict process(More)
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