A well-known information reuse approach is to exploit event logs for process discovery and optimization. However, process discovery is rarely used for product development projects. This is because information systems in product development, e. g., Product-Lifecycle-Management (PLM) systems, do not provide the event logs required by process discovery algorithms. Additionally, existing algorithms struggle with development projects, as these are unstructured and rich in variety. In this paper, we propose a novel approach to process discovery in order to make it applicable and tailored to product development projects. Instead of using flat event logs, we provide a graph-based data structure that is able to represent both activities and data of product development projects with the dataflow between activities. Based on this structure, we can leverage provenance available in PLM systems. Furthermore, we may use frequent subgraph mining to discover process patterns. Such patterns are well suited to describe different variants and common sub-processes of unstructured processes. Using a prototype, we evaluate this approach and successfully discover prevailing patterns. These patterns may be used by engineers to support their decision-making or help improve the execution of development projects.