Discovering High-Level Performance Models for Ticket Resolution Processes

Predicting run-time performances is a hot issue in ticket resolution processes. Recent efforts to take account for the sequence of resolution steps, suggest that predictive Process Mining (PM) techniques could be applied in this field, if suitably adapted to the peculiarities of ticket systems. In particular, the performances of a ticket instance usually depend on which kinds of experts worked on it (more than on the mere sequence of resolution tasks), while relevant information about ticket cases is stored in the form of text fields, which are usually disregarded by PM approaches. Instead of relying on a-priori experts groups, we devise an ad-hoc method for clustering experts according to their real working patterns, based on log data. Regarding the discovered groups as abstractions for log events, we also perform a predictive clustering of ticket cases, while using context data as input attributes for splitting the tickets. In this way, different (context-dependent) execution scenarios are recognized for the process, and equipped with more accurate performance predictors. The approach was validated on a real application scenario, where it showed better results than state-of-the-art solutions.