Discovering expressive process models from noised log data

Process-oriented systems have been increasingly attracting data mining researchers, mainly due to the advantages that the application of inductive process mining techniques to log data could open to both the analysis of complex processes and the design of new process models. However, the actual impact of process mining in the industry is endangered by some simplifying assumptions these techniques relies on. In fact, current approaches have still problems to mine models over languages that allow for complex constructs, e.g., duplicate tasks, hidden tasks, non-free-choice constructs, and/or when noise is admitted in the log. In this paper, some advances to facing these problems are made, by proposing an algorithm which can deal with duplicate and hidden tasks, as well as with the presence of noise and non-free choice relationships among process activities. Importantly, due to the local nature of the search strategy exploited by the algorithm, the proposed approach seems suited to scale in real-world application scenarios.