Process mining aims to transform event data recorded in information systems into knowledge of an organisation's business processes. The results of process mining analysis can be used to improve process performance or compliance to rules and regulations. However, applying process mining in practice is not trivial. In this paper we introduce PM 2 , a methodology to guide the execution of process mining projects. We successfully applied PM 2 during a case study within IBM, a multinational technology corporation, where we identified potential process improvements for one of their purchasing processes.

Abstract. Process mining provides fact-based insights into process behaviour captured in event data. In this work we aim to discover models for processes where different facets, or perspectives, of the process can be identified. Instead of focussing on the events or activities that are executed in the context of a particular process, we concentrate on the states of the different perspectives and discover how they are related. We present a formalisation of these relations and an approach to discover state-based models highlighting them. The approach has been implemented using the process mining framework ProM and provides a highly interactive visualisation of the multi-perspective state-based models. This tool has been evaluated on the BPI Challenge 2012 data of a loan application process and on product user behaviour data gathered by Philips during the development of a smart baby bottle equipped with various sensors.

Abstract. Planning human activities within business processes often happens based on the same methods and algorithms as are used in the area of manufacturing systems. However, human behaviour is quite different from machine behaviour. Their performance depends on a number of factors, including workload, stress, personal preferences, etc. In this article we describe an approach for scheduling activities of people that takes into account business rules and dynamic human performance in order to optimise the schedule. We formally describe the scheduling problem we address and discuss how it can be constructed from inputs in the form of business process models and performance measurements. Finally, we discuss and evaluate an implementation for our planning approach to show the impact of considering dynamic human performance in scheduling.