C. Rolland, N. Prakash, and A. Benjamen, A Multi-Model View of Process Modelling, Requirements Engineering, vol.4, issue.4, pp.169-187, 1999.
DOI : 10.1007/s007660050018

URL : https://hal.archives-ouvertes.fr/hal-00707568

W. M. Van-der-aalst-st and E. , Process Mining: Discovery, Conformance and Enhancement of Business Processes, pp.184-352, 2011.

C. Hug, R. Deneckère, and C. Salinesi, Map-TBS: Map process enactment traces and analysis, 2012 Sixth International Conference on Research Challenges in Information Science (RCIS), pp.204-209, 2012.
DOI : 10.1109/RCIS.2012.6240435

URL : https://hal.archives-ouvertes.fr/hal-00701230

B. Van-dongen and W. Van-der-aalst, Multi-phase Process Mining: Building Instance Graphs, International Conference on Conceptual Modeling, pp.362-376, 2004.
DOI : 10.1007/978-3-540-30464-7_29

W. Van-der-aalst, A. Medeiros, and A. Weijters, Genetic Process Mining Applications and Theory of Petri Nets, LNCS, vol.3536, pp.48-69, 2005.

J. De-weerdt, M. De-backer, J. Vanthienen, and B. Baesens, A robust F-measure for evaluating discovered process models, 2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), pp.148-155, 2011.
DOI : 10.1109/CIDM.2011.5949428

G. Greco, A. Guzzo, L. Ponieri, and D. Sacca, Discovering expressive process models by clustering log traces, IEEE Transactions on Knowledge and Data Engineering, vol.18, issue.8, pp.1010-1027, 2006.
DOI : 10.1109/TKDE.2006.123

L. E. Baum-leonard and T. Petrie, Statistical Inference for Probabilistic Functions of Finite State Markov Chains, The Annals of Mathematical Statistics, vol.37, issue.6, pp.1554-1563, 1966.
DOI : 10.1214/aoms/1177699147

G. Greco, A. Guzzo, and L. Pontieri, Mining Hierarchies of Models: From Abstract Views to Concrete Specifications, Proceedings of the 3rd International Conference on Business Process Management, BPM 2005, pp.32-47, 2005.
DOI : 10.1007/11538394_3

W. M. Van-der-aalst, A. J. Weijters, and L. Maruster, Workflow mining: discovering process models from event logs, IEEE Transactions on Knowledge and Data Engineering, vol.16, issue.9, pp.1128-1142, 2004.
DOI : 10.1109/TKDE.2004.47

W. Enders-hoboken and N. J. , Applied econometric time series, 2004.

A. Rozinat, M. Veloso, and W. M. Van-der-aalst, Evaluating the quality of discovered process models, 2nd Intl. Workshop on the Induction of Process Models, pp.45-52, 2008.

A. J. Weijters and W. M. Van-der-aalst, Rediscovering Workflow Models from Event Based Data using Little Thumb, Integrated Computer-Aided Engineering, vol.10, issue.2, pp.151-162, 2003.

J. Herbst and D. Karagiannis, Integrating Machine Learning and Workflow Management to Support Acquisition and Adaptation of Workflow Models, Proceedings of the 9 th International Workshop on Database and Expert Systems Applications, pp.745-752, 1998.

R. Agrawal, D. Gunopulos, and F. Leymann, Mining process models from workflow logs, Proceedings of the 6 th International Conference on Extending Database Technology: Advances in Database Technology, pp.469-483, 1998.
DOI : 10.1007/BFb0101003

J. Cook and A. Wolf, Discovering models of software processes from event-based data, ACM Transactions on Software Engineering and Methodology, vol.7, issue.3, pp.215-249, 1998.
DOI : 10.1145/287000.287001

S. Minseok, C. W. Günther, and W. M. Van-der-aalst, Trace Clustering in Process Mining Business Process Management Workshops, Lecture Notes in Business Inf. Processing, vol.17, pp.109-120, 2009.

F. Eibe, I. H. Witten, and . Ed, Data Mining: Practical Machine Learning Tools and Techniques, 2005.

D. Ferreira, M. Zacarias, M. Malheiros, and P. Ferreira, Approaching Process Mining with Sequence Clustering: Experiments and Findings, BPM 2007, pp.360-374, 2007.
DOI : 10.1007/978-3-540-75183-0_26

R. Durbin, S. Eddy, A. Krogh, and G. Mitchison, Biological sequence analysis Probabilistic models of proteins and nucleicacids, 1998.

L. R. Rabiner, A tutorial on hidden Markov models and selected applications in speech recognition, Proceedings of the IEEE, vol.77, issue.2, pp.257-286, 1989.
DOI : 10.1109/5.18626

S. Assar, C. , B. Achour, and S. Si-said, Un Modèle pour la spécification des processus d'analyse des Systèmes d'Information, proceedings of 18ème Congrès INFORSID, pp.287-301, 2000.

G. D. Forney, The viterbi algorithm, Proceedings of the IEEE, vol.61, issue.3, pp.268-278, 1973.
DOI : 10.1109/PROC.1973.9030

L. E. Baum, T. Petrie, G. Soules, and N. Weiss, A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains, The Annals of Mathematical Statistics, vol.41, issue.1, pp.164-171, 1970.
DOI : 10.1214/aoms/1177697196

B. H. Juang and L. R. Rabiner, Hidden Markov Models for Speech Recognition, Technimetrics by American Statistical Association and American Society for Quality, pp.251-272, 1991.
DOI : 10.1007/978-3-642-66286-7

A. W. Biermann and J. A. Feldman, On the Synthesis of Finite-State Machines from Samples of Their Behavior, IEEE Transactions on Computers, vol.21, issue.6, pp.592-597, 1972.
DOI : 10.1109/TC.1972.5009015

J. Cook and A. Wolf, Automating process discovery through event-data analysis, Proceedings of the 17th international conference on Software engineering , ICSE '95, pp.73-82, 1995.
DOI : 10.1145/225014.225021

T. Joachims, Text categorization with support vector machines, LS VIII Number, vol.23, 1997.

C. Goutte, E. Gaussier, and E. , A Probabilistic Interpretation of Precision, Recall and F-Score, with Implication for Evaluation, Proceedings of the 27 th European Conference on Information Retrieval, pp.345-359
DOI : 10.1007/978-3-540-31865-1_25

A. Enright, S. Van-dongen, and C. Ouzounis, An efficient algorithm for large-scale detection of protein families, Nucleic Acids Research, vol.30, issue.7, pp.1575-1584, 2002.
DOI : 10.1093/nar/30.7.1575

S. Das and M. C. Mozer, A unified Gradient Descent/Clustering Architecture for Finite State Machine Induction, proceeding of the 1993 Conference in Advances in Neural Information Processing system, pp.19-26, 1994.

M. Jarke, C. Rolland, A. Sutcliffe, and R. Domges, The NATURE of Requirements Engineering, 1999.
URL : https://hal.archives-ouvertes.fr/hal-00707563

L. Thevenet and C. Salinesi, Aligning IS to Organization???s Strategy: The InStAl Method, Proceedings of CAiSE, pp.203-217, 2007.
DOI : 10.1007/978-3-540-72988-4_15

URL : https://hal.archives-ouvertes.fr/hal-00706188

G. Grosz, MENTOR: a Step Forward in Guidance for Information System Development, Proceedings of the fifth Workshop on the Next Generation of CASE Tools, 1994.
URL : https://hal.archives-ouvertes.fr/hal-00708042

M. Tawbi, C. Souveyet, C. Rolland, G. Khodabandelou, C. Hug et al., L'ECRITOIRE a tool to support a goal-scenario based approach to requirements engineering Information and Software Technology journal COTS Products to trace method enactment: review and selection, 21 st European Conference on Information Systems, 1998.

G. Khodabandelou, C. Hug, R. Deneckère, and C. Salinesi, Process Mining Versus Intention Mining, International Conference on Exploring Modelling Methods for Systems Analysis and Design (EMMSAD), 2013.
DOI : 10.1007/978-3-642-38484-4_33

URL : https://hal.archives-ouvertes.fr/hal-00803968