Automatic Process Model Discovery from Textual Methodologies: An Archaeology Case Study

Abstract : — Process mining has been successfully used in automatic knowledge discovery and in providing guidance or support. The known process mining approaches rely on processes being executed with the help of information systems thus enabling the automatic capture of process traces as event logs. However, there are many other fields such as Humanities, Social Sciences and Medicine where workers follow processes and log their execution manually in textual forms instead. The problem we tackle in this paper is mining process instance models from unstructured, text-based process traces. Using natural language processing with a focus on the verb semantics, we created a novel unsupervised technique TextProcessMiner that discovers process instance models in two steps: 1.ActivityMiner mines the process activities; 2.ActivityRelationshipMiner mines the sequence, parallelism and mutual exclusion relationships between activities. We employed technical action research through which we validated and preliminarily evaluated our proposed technique in an Archaeology case. The results are very satisfactory with 88% correctly discovered activities in the log and a process instance model that adequately reflected the original process. Moreover, the technique we created emerged as domain independent.
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Elena Viorica Epure, Patricia Martín-Rodilla, Charlotte Hug, Rebecca Deneckère, Camille Salinesi. Automatic Process Model Discovery from Textual Methodologies: An Archaeology Case Study. IEEE Ninth International Conference on Research Challenges in Information Science, May 2015, Athens, Greece. ⟨hal-01149742⟩

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