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Intentional Process Mining: Discovering and Modeling the Goals Behind Processes using Supervised Learning

Intentional Process Mining: Discovering and Modeling the Goals Behind Processes using Supervised Learning

Rebecca Deneckère, Charlotte Hug, Ghazaleh Khodabandelou, Camille Salinesi
Copyright: © 2014 |Volume: 5 |Issue: 4 |Pages: 26
ISSN: 1947-8186|EISSN: 1947-8194|EISBN13: 9781466654983|DOI: 10.4018/ijismd.2014100102
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MLA

Deneckère, Rebecca, et al. "Intentional Process Mining: Discovering and Modeling the Goals Behind Processes using Supervised Learning." IJISMD vol.5, no.4 2014: pp.22-47. http://doi.org/10.4018/ijismd.2014100102

APA

Deneckère, R., Hug, C., Khodabandelou, G., & Salinesi, C. (2014). Intentional Process Mining: Discovering and Modeling the Goals Behind Processes using Supervised Learning. International Journal of Information System Modeling and Design (IJISMD), 5(4), 22-47. http://doi.org/10.4018/ijismd.2014100102

Chicago

Deneckère, Rebecca, et al. "Intentional Process Mining: Discovering and Modeling the Goals Behind Processes using Supervised Learning," International Journal of Information System Modeling and Design (IJISMD) 5, no.4: 22-47. http://doi.org/10.4018/ijismd.2014100102

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Abstract

Understanding people's goals is a challenging issue that is met in many different areas such as security, sales, information retrieval, etc. Intention Mining aims at uncovering intentions from observations of actual activities. While most Intention Mining techniques proposed so far focus on mining individual intentions to analyze web engine queries, this paper proposes a generic technique to mine intentions from activity traces. The proposed technique relies on supervised learning and generates intentional models specified with the Map formalism. The originality of the contribution lies in the demonstration that it is actually possible to reverse engineer the underlying intentional plans built by people when in action, and specify them in models e.g. with intentions at different levels, dependencies, links with other concepts, etc. After an introduction on intention mining, the paper presents the Supervised Map Miner Method and reports two controlled experiments that were undertaken to evaluate precision, recall and F-Score. The results are promising since the authors were able to find the intentions underlying the activities as well as the corresponding map process model with satisfying accuracy, efficiency and performance.

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