Abstract-In this paper we address the challenge of applying process mining to discover models of human behaviour from sensor data. This challenge is caused by a gap between sensor data and the event logs that are used as input for process mining techniques, so we provide a transformation approach to bridge this gap. As a result, besides the automatic discovery of process models, the transformed sensor data can also be used by various other process mining techniques, e.g. to identify differences between observed behaviour and expected behaviour. We discuss the transformation approach in the context of the design process of smart products and related services, using a case study performed at Philips where a smart baby bottle has been developed. This case study also demonstrates that the use of process mining can add value to the smart product design process.

Artifact-centric process models aim to describe complex processes as a collection of interacting artifacts. Recent development in process mining allow for the discovery of such models. However, the focus is often on the representation of the individual artifacts rather than their interactions. Based on event data, composite state machines representing artifact-centric processes can be discovered automatically. Moreover, the study provides ways of visualising and quantifying interactions among different artifacts. For example, strongly correlated behaviours in different artifacts can be highlighted. Interesting correlations can be subsequently analysed to identify potential causes of process performance issues. The study provides a strategy to explore the interactions and performance differences in this context. The approach has been fully implemented as a ProM plug-in; the CSM Miner provides an interactive artifact-centric process discovery tool focussing on interactions. The approach has been evaluated using real life data, to show that the guided exploration of artifact interactions can successfully identify process performance issues.

Abstract-Artifact-centric process models aim to describe complex processes as a collection of interacting artifacts. Recent development in process mining allow for the discovery of such models. However, the focus is often on the representation of the individual artifacts rather than their interactions. Based on event data we can automatically discover composite state machines representing artifact-centric processes. Moreover, we provide ways of visualizing and quantifying interactions among different artifacts. For example, we are able to highlight strongly correlated behaviours in different artifacts. The approach has been fully implemented as a ProM plug-in; the CSM Miner provides an interactive artifact-centric process discovery tool focussing on interactions. The approach has been evaluated using real life data sets, including the personal loan and overdraft process of a Dutch financial institution.

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In this paper, we present a data-driven approach to enable the creation of evidence-based usability test scenarios. By utilising product usage data to create usability test scenarios, we aim to improve the reliability of the test results and to provide better insights into product usability. The approach consists of four elements: the collection of product usage data, the transformation of these data into logs of user activities, the creation of models of user behaviour, and the guided creation of usability test scenarios based on the models. We discuss the challenges that can be encountered when applying this approach based on our experiences with two case studies in product development. We have created a prototype scenario planning tool and performed a preliminary evaluation of the tool with usability engineers working at Philips Healthcare. The evaluation shows that tool-supported evidence-based usability test creation would be valuable in their daily work.