to automatically retrieve a feature vector representation of an event that can be used with supervised learning techniques, making use of certain aspects of the XES standard definition for event logs [@Gunther2014]. In the same section we describe a supervised learning method to map low-level events into target high-level events. Sections \[sec:case\_1\] and \[sec:case\_2\] respectively show the added value of the described supervised event abstraction method for process mining on a real life event log from a smart home environment and on a synthetic log from a digital photocopier respectively. Section \[sec:conclusion\] concludes the paper.

Related Work {#sec:related}
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Supervised event abstraction is an unexplored problem in process mining. A related field is activity recognition within the field of ubiquitous computing. Activity recognition focuses on the task of detecting human activity from either passive sensors [@Kasteren2008; @Tapia2004], wearable sensors [@Bao2004; @Kwapisz2011], or cameras [@Poppe2010]. Activity recognition methods generally work on discrete time windows over the time series of sensor values and aim to map each time window onto the correct type of human activity, e.g. *eating* or *sleeping*. Activity recognition methods can be classified into probabilistic approaches [@Kasteren2008; @Tapia2004; @Bao2004; @Kwapisz2011] and approaches based on ontology reasoning [@Chen2009; @Riboni2011]. The 