Product service plays an essential role in day-to-day operations of nowadays manufacturing industries. However, the changing demands of the market/customers, the increasing complexity of product functionalities and the extended product lifecycles present challenges to related In-Service projects. In order to handle the increasing number of projects and to control the costs and resource consumptions, it is critical to improve the efficiency and automation of process management. Within this context, this paper introduces some data-driven approaches to interpret and represent changes of project process over time in an automatic manner. These approaches aim to help project actors improve their understanding of process structure and the efficiency of process management, and also enable them to investigate process changes from more dynamic perspectives. To evaluate the approaches, a dataset from an aerospace organisation is considered in this paper.