Mining Users' Intents from Logs

Ghazaleh Khodabandelou (1) , Charlotte Hug (1) , Camille Salinesi (1)

1 CRI - Centre de Recherche en Informatique de Paris 1

Intentions play a key role in information systems engineering. Research on process modeling has highlighted that specifying intentions can expressly mitigate many problems encountered in process modeling as lack of flexibility or adaptation. Process mining approaches mine processes in terms of tasks and branching. To identify and formalize intentions from event logs, this work presents a novel approach of process mining, called Map Miner Method (MMM). This method automates the construction of intentional process models from event logs. First, MMM estimates users' strategies (i.e., the different ways to fulfill the intentions) in terms of their activities. These estimated strategies are then used to infer users' intentions at different levels of abstraction using two tailored algorithms. MMM constructs intentional process models with respect to the Map metamodel formalism. MMM is applied on a real-world dataset, i.e. event logs of developers of Eclipse UDC (Usage Data Collector). The resulting Map process model provides a precious understanding of the processes followed by the developers, and also provide feedback on the effectiveness and demonstrate scalability of MMM.

Domains: Computer Science [cs]; Software Engineering [cs.SE]; Computer Science [cs]; Other [cs.OH]

Ghazaleh Khodabandelou, Charlotte Hug, Camille Salinesi. Mining Users' Intents from Logs. International Journal of Information System Modeling and Design, 2015, Special Issue from the 8th IEEE International Conference on Research Challenges in Information Science (RCIS): 2014, Marrakesh, Morocco, 6 (2), pp.43-71. ⟨10.4018/IJISMD.2015040102⟩. ⟨hal-01140171⟩