Unsupervised discovery of intentional process models from event logs - Université Paris 1 Panthéon-Sorbonne Access content directly
Conference Papers Year : 2014

Unsupervised discovery of intentional process models from event logs


Research on guidance and method engineering has highlighted that many method engineering issues, such as lack of flexibility or adaptation, are solved more effectively when intentions are explicitly specified. However, software engineering process models are most often described in terms of sequences of activities. This paper presents a novel approach, so-called Map Miner Method (MMM), designed to automate the construction of intentional process models from process logs. To do so, MMM uses Hidden Markov Models to model users' activities logs in terms of users' strategies. MMM also infers users' intentions and constructs fine-grained and coarse-grained intentional process models with respect to the Map metamodel syntax (i.e., metamodel that specifies intentions and strategies of process actors). These models are obtained by optimizing a new precision-fitness metric. The result is a software engineering method process specification aligned with state of the art of method engineering approaches. As a case study, the MMM is used to mine the intentional process associated to the Eclipse platform usage. Observations show that the obtained intentional process model offers a new understanding of software processes, and could readily be used for recommender systems.
Fichier principal
Vignette du fichier
msr2014_submission_25.pdf (410.07 Ko) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-00994197 , version 1 (21-05-2014)



Ghazaleh Khodabandelou, Charlotte Hug, Rebecca Deneckere, Camille Salinesi. Unsupervised discovery of intentional process models from event logs. 11th Working Conference on Mining Software Repositories, May 2014, Hyderabad, India. pp.282-291, ⟨10.1145/2597073.2597101⟩. ⟨hal-00994197⟩


156 View
1478 Download



Gmail Mastodon Facebook X LinkedIn More