The analysis of dynamic data is challenging. Indeed, the structure of such data changes over time, potentially in a very fast speed. In addition, the objects in such data-sets are often complex. In this paper, our practical motivation is to perform users profiling, i.e. to follow users' geographic location and navigation logs to detect changes in their habits and their interests. We propose a new framework in which we first create, for each user, a signal of the evolution in the distribution of their interest and another signal based on the distribution of physical locations recorded during their navigation. Then, we detect automatically the changes in interest or locations thanks a new jump-detection algorithm. We compared the proposed approach with a set of existing signal-based algorithms on a set of artificial data-sets and we showed that our approach is faster and produces less errors for this kind of task. We then applied the proposed framework on a real data-set and we detected different categories of behavior among the users, from users with very stable interest and locations to users with clear changes in their behaviors, either in interest, location or both.