Today's process-aware information systems tend to either support business processes or provide flexibility. Classical workflow management systems offer good process support as long as the processes are structured and do not require much flexibility. Information systems that allow for flexibility have a tendency to lack process-related support. If systems offer guidance, then they are typically also inclined to "enforce guidelines" and are perceived as inflexible. Moreover, implementing flexible systems is far from trivial. This paper will show that using a more declarative approach can assist in a better balance between flexibility and support. This is demonstrated by presenting the Declare framework that aims to take care of the full spectrum of flexibility while at the same time supports the user using recommendations and other process-mining-based diagnostics.

Abstract. Process mining allows for the automated discovery of process models from event logs. These models provide insights and enable various types of model-based analysis. This paper demonstrates that the discovered process models can be extended with information to predict the completion time of running instances. There are many scenarios where it is useful to have reliable time predictions. For example, when a customer phones her insurance company for information about her insurance claim, she can be given an estimate for the remaining processing time. In order to do this, we provide a configurable approach to construct a process model, augment this model with time information learned from earlier instances, and use this to predict e.g. the completion time. To provide meaningful time predictions we use a configurable set of abstractions that allow for a good balance between "overfitting" and "underfitting". The approach has been implemented in ProM and through several experiments using real-life event logs we demonstrate its applicability.

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Abstract. The degree of flexibility of workflow management systems heavily influences the way business processes are executed. Constraint-based models are considered to be more flexible than traditional models because of their semantics: everything that does not violate constraints is allowed. Although constraint-based models are flexible, changes to process definitions might be needed to comply with evolving business domains and exceptional situations. Flexibility can be increased by run-time support for dynamic changes -transferring instances to a new model -and ad-hoc changes -changing the process definition for one instance. In this paper we propose a general framework for a constraint-based process modeling language and its implementation. Our approach supports both ad-hoc and dynamic change, and the transfer of instances can be done easier than in traditional approaches.

Abstract. Business processes provide a means of coordinating interactions between workers and organisations in a structured way. However the dynamic nature of the modern business environment means these processes are subject to a increasingly wide range of variations and must demonstrate flexible approaches to dealing with these variations if they are to remain viable. The challenge is to provide flexibility and offer process support at the same time. Many approaches have been proposed in literature and some of these approaches have been implemented in flexible workflow management systems. However, a comprehensive overview of the various approaches has been missing. In this paper, we take a deeper look into the various ways in which flexibility can be achieved and we propose an extensive taxonomy of flexibility. This taxonomy is subsequently used to evaluate a selection of systems and to discuss how the various forms of flexibility fit together.

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To gain competitive advantage, hospitals try to streamline their processes. In order to do so, it is essential to have an accurate view of the "careflows" under consideration. In this paper, we apply process mining techniques to obtain meaningful knowledge about these flows, e.g., to discover typical paths followed by particular groups of patients. This is a non-trivial task given the dynamic nature of healthcare processes. The paper demonstrates the applicability of process mining using a real case of a gynecological oncology process in a Dutch hospital. Using a variety of process mining techniques, we analyzed the healthcare process from three different perspectives: (1) the control flow perspective, (2) the organizational perspective and (3) the performance perspective. In order to do so we extracted relevant event logs from the hospitals information system and analyzed these logs using the ProM framework. The results show that process mining can be used to provide new insights that facilitate the improvement of existing careflows.