lifecycle of our research...international conference on research challenges in information systems...
TRANSCRIPT
Intentional Process Model Discovery from Logs
International Journal R. Deneckère , C. Hug, G. Khodabandelou, C. Salinesi, "Intention Mining: Process Model Discovery Using Supervised Learning", International Journal of Information System Modeling and Design (IJISMD), 2014. International Conferences G. Khodabandelou, C. Hug, C. Salinesi, "Toward an Automatic Tool for the Construction of Intentional Process Models from Event Logs", long paper, Submitted to The 8th IEEE International Conference on Research Challenges in Information Systems (RCIS), May 2014, Marrakesh, Morocco. G. Khodabandelou, C. Hug, R. Deneckère, C. Salinesi, "Supervised vs. Unsupervised Learning for Intentional Process Models Discovery", Submitted to BPMDS in conjunction with 26th International Conference on Advanced Information Systems Engineering (CAiSE), June 2014, Thessaloniki, Creek. G. Khodabandelou, C. Hug, R. Deneckère, C. Salinesi, "Unsupervised Discovery of Intentional Process Model from Event Logs", long paper, Accepted in MSR/ICSE (the 36st International Conference on Software Engineering), June 2014, Hyderabad, India. Jankovic, M., Bajec, M., G. Khodabandelou, C. Hug, R. Deneckère, C. Salinesi, "Intelligent Agile Method Framework", Proc. of the 8st International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE), July 2013, Angers, France. G. Khodabandelou, C. Hug, R. Deneckère, C. Salinesi, M. Bajec, E. Kornyshova, M. Jankovic "COTS Products to Trace Method Enactment : Review and Selection “, long paper, European Conference on Information Systems (ECIS), June 2013, Utrecht, Netherlands. G. Khodabandelou, "Contextual Recommendations using Intention Mining on Process Traces", doctoral consortium, the Seventh IEEE International Conference on Research Challenges in Information Science (RCIS), May 2013, Paris, France. G. Khodabandelou, C. Hug, R. Deneckère, C. Salinesi, "Supervised Intentional Process Models Discovery using Hidden Markov Models", long paper, the Seventh IEEE International
Conference on Research Challenges in Information Science (RCIS), May 2013, Paris, France. (Best Paper Award) G. Khodabandelou, C. Hug, R. Deneckère, C. Salinesi, “Process Mining versus Intention Mining", long paper, Exploring Modelling Methods for Systems Analysis and Design (EMMSAD), June 2013, Valencia, Spain. National Conference G. Khodabandelou, C. Hug, R. Deneckère, C. Salinesi, « Découverte Supervisée des Modèles de Processus Intentionnels Utilisant Modèles de Markov Cachés ", papier long, 31th edition, National INFORSID, Mai 2013, Paris, France.
Ghazaleh Khodabandelou Key Words : Intention Mining, Machine Learning, Process Traces
Thesis Director : Camille Salinesi Co-directors : Rebecca Deneckère, Charlotte Hug Centre de Recherche en Informatique
Context : How to discover users' intentions and strategies?
Lifecycle of our research
Traces Analysis
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Processes
Petri nets
Activities
MAP
Intention
Intention
Hypothesis: It is possible to find discover users’ intentions and strategies during process enactment out of logs.
Research question: Can we discover users’ intentions and strategies during process enactment?
Stakeholders
Clustering
Machine Learning
Techniques
Traces base
Stakeholders Logs
Map
HMMs
CRI- Centre de Recherche en Informatique April 2014
Activities
Map discovery :
Estimated Strategies
Pseudo-Map Discovery :
Deep Miner Algorithm
Estimated Strategies
Process Mining Approaches: Discovery of task-oriented model Rigid, Non-representative of humans’ rationale
Intentional Model Machine Learning
Users’ activities represent the real process.
Map process modal allows for intentions/strategies of Information System’ users
Inte
nti
on
Min
ing
Discovery of the rules among logs: Strategies
BPMN
Intention
Traces base
Supervised Learning
Unsupervised Learning
Map Miner Algorithm
Specify an entity Specify an association
Sub-Intentions
Specify an entity Specify an association
Sub-Intentions
Pseudo-Map
Pseudo-Map Discovered Map Process Model