Processes are omnipresent in humans’ everyday activities: withdrawals from an ATM, loan requests from a bank, renewals of driver’s licenses, purchases of goods from online retail systems. In particular, the business domain has strongly embraced processes as an instrument to help in the organization of business operations, leading to so-called business processes. A business process is a set of logically-related tasks performed to achieve a defined business outcome. Business processes have a big impact on the achievement of business goals and they are widely acknowledged as one of the more important assets of any organization next to the organization’s customer basis and, more recently, data. Thus, there is a high interest in keeping business processes performing at their best and improving those that do not perform well. Nowadays, business processes are supported by a wide range of enabling technologies, including Web services and business process engines, which enable the (partial)automation of processes. Information systems supporting the execution of processes typically store a wealth of process knowledge that includes process models, process progression information and business data. The availability of such process knowledge gives unprecedented opportunities to get insight into business processes, which leads to the question of how to exploit this knowledge for facilitating the improvement of processes. In order to answer this question, we propose to exploit process knowledge from two different but complementary perspectives. In the first one, we take the process execution perspective and leverage on process execution data generated by information systems to analyze and understand the actual behavior of executed processes. In the second one, we take the process design perspective and propose to extract process model patterns from existing models for reuse in the design of processes. The final goal of this thesis is to facilitate process improvement by exploiting existing process knowledge not only for gaining insight into and understanding of processes but also for reusing the resulting knowledge in the improvement thereof. We have successfully applied our approaches in the context of service-based business processes and assisted dataflow-based mashup development. In the former, we validated our approach through a end-user study of the usability and understandability of our approach and tools, while in the latter the evaluations were performed through experiments run on a dataset of models from the mashup tool Yahoo! Pipes.