Abstract  Web service compositions are becoming more and more complex, involving numerous interacting ad-hoc services. These services are often implemented as business processes themselves. By analysing such complex web service compositions one is able to better understand, control and eventually re-design them. Our contribution to this problem is a mining algorithm, based on a statistical technique to discover composite web service patterns from execution logs. Our approach is characterised by a “local” pattern’s discovery that covers partial results through a dynamic programming algorithm. Those locally discovered patterns are then composed iteratively until the composite Web service is discovered. The analysis of the disparities between the discovered model and the initial ad-hoc composite model (delta-analysis) enables initial design gaps to be detected and thus to re-engineer the initial Web service composition.