Reasoning about precedence constraints for process mining applications

A key task in process mining consists of building a graph of causal dependencies over process activities, which can then help derive more expressive models in some high-level modeling language. An approach to accomplishing this task is presented, where the learning process can exploit background knowledge available to the analyst. The method is based on encoding the information gathered from the log and the (possibly) given background knowledge in terms of precedence constraints, i.e., constraints over the topology of the graphs. Learning algorithms are formulated in terms of reasoning problems over precedence constraints. The whole approach has been implemented in a prototype system and results of experimental activity are reported.