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Abstract. In the engineering domain, the development of complex products (e.g., cars) necessitates the coordination of thousands of (sub-)processes. One of the biggest challenges for process management systems is to support the modeling, monitoring and maintenance of the many interdependencies between these sub-processes. The resulting process structures are large and can be characterized by a strong relationship with the assembly of the product; i.e., the sub-processes to be coordinated can be related to the dierent product components. So far, sub-process coordination has been mainly accomplished manually, resulting in high eorts and inconsistencies. IT support is required to utilize the information about the product and its structure for deriving, coordinating and maintaining such data-driven process structures. In this paper, we introduce the COREPRO framework for the data-driven modeling of large process structures. The approach reduces modeling eorts signicantly and provides mechanisms for maintaining data-driven process structures.

There has recently been some interest in applying machine learning techniques to support the acquisition and adaptation of workflow models. The different learning algorithms, that have been proposed, share some restrictions, which may prevent them from being used in practice. Approaches applying techniques from grammatical inference are restricted to sequential workflows. Other algorithms allowing concurrency require unique activity nodes. This contribution shows how the basic principle of our previous approach to sequential workflow induction can be generalized, so that it is able to deal with concurrency. It does not require unique activity nodes. The presented approach uses a log-likelihood guided search in the space of workflow models, that starts with a most general workflow model containing unique activity nodes. Two split operators are available for specialization.

Abstract. Car development is based on long running, concurrently executed and highly dependent processes. The coordination and synchronization of these processes has become a complex and error-prone task due to the increasing number of functions and embedded systems in modern cars. These systems realize advanced features by embedded software and enable the distribution of functionality as required, for example, by safety equipment. Different life cycle times of mechanical, software and hardware components as well as different duration of their development processes require efficient coordination. Furthermore, productdriven process structures, dynamic adaptation of these structures, and handling real-world exceptions result in challenging demands for any IT system. In this paper we elaborate fundamental requirements for the IT support of car development processes, taking release management as characteristic example. We show to which extent current product data and process management technology meets these requirements, and discuss which essential limitations still exist. This results in a number of fundamental challenges requiring new paradigms for the product-driven design, enactment and adaptation of processes.

Current workflow management systems (WFMS) offer little aid for the acquisition of workflow models and their adaptation to changing requirements. To support these activities we propose to integrate machine learning and workflow management. This enables an inductive approach to workflow acquisition and adaptation by processing traces of manually enacted workflows. We present a machine learning component that combines two different machine learning algorithms. In this paper we focus mainly on the first one, which induces the structure of the workflow, based on the induction of hidden markov models. The second algorithm, a standard decision rule induction algorithm, induces transition conditions. The main concepts have been implemented in a prototype, which we have validated using artificial process traces. The induced workflow models can be imported by the business process management system ADONIS 1 .