ChAPMaN: a Context Aware Process MiNer

In this paper we introduce ChAPMaN (Context Aware Process MiNer), a novel PM tool. This tool allows for the generation of process models which can be subsequently used for the retrieval of similar processes or for data mining tasks such as distance-based clustering or classication. ChAPMaN is based on an algorithm studied to guarantee an output model that only includes paths actually recorded as traces in the event log. In order to realize this objective, our algorithm: (1) makes an intensive use of all the available frequency information about the activities recorded in the event log; (2) properly forks the model into various branches, on the basis of the dierent execution contexts, implicitly represented by subsets of the traces in the event log. This feature makes ChAPMaN particularly well suited for all those applications in which adherence to reality of the mined model is vital, like, e.g., patient management and health care quality assessment. Interestingly, when a mined model is already available, ChAPMaN can also be adopted as a \path checker", to rule out spurious paths that may have been introduced in the model during the mining phase. The work is still in a rather preliminary phase, and some limitations need to be addressed. In the future, we plan to produce an enhanced version of the tool, and to extensively test it in real world domains.