Y. Wang, K. Cao, and X. Zhang, Complex event processing over distributed probabilistic event streams, Computers & Mathematics with Applications, vol.66, issue.10, pp.1808-1821, 2013.

M. , Finding "Unexplained" Activities in Video, IJCAI, pp.1628-1634, 2011.

A. Skarlatidis, Probabilistic event calculus for event recognition, ACM Transactions on Computational Logic (TOCL), vol.16, issue.2, p.11, 2015.

F. Liu, D. Deng, and P. Li, Dynamic Context-Aware Event Recognition Based on Markov Logic Networks, Sensors, vol.17, issue.3, p.491, 2017.

A. Skarlatidis, A probabilistic logic programming event calculus, Theory and Practice of Logic Programming, vol.15, pp.213-245, 2015.

Y. A. Ivanov and A. F. Bobick, Recognition of visual activities and interactions by stochastic parsing, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, issue.8, pp.852-872, 2000.

V. Morariu and L. S. Davis, Multi-agent event recognition in structured scenarios, Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, pp.3289-3296, 2011.

Y. C. Song, A Markov Logic Framework for Recognizing Complex Events from Multimodal Data, Proc. of the 15th ACM on International Conference on Multimodal Interaction, ser. ICMI '13, pp.141-148, 2013.

A. Piel, Reconnaissance de comportements complexes par traitement en ligne de flux d'evenements, 2014.
URL : https://hal.archives-ouvertes.fr/tel-01262245

P. Carle, C. Choppy, and R. Kervarc, Behaviour Recognition Using Chronicles, 5th International Symposium on Theoretical Aspects of Software Engineering (TASE), pp.100-107, 2011.

Y. Diao, N. Immerman, and D. Gyllstrom, Sase+: An agile language for Kleene closure over event streams, 2007.

G. Cugola and A. Margara, Complex event processing with T-REX, J. of Systems and Software, vol.85, issue.8, pp.1709-1728, 2012.

C. Dousson and P. L. Maigat, Chronicle Recognition Improvement Using Temporal Focusing and Hierarchization, IJCAI, vol.7, pp.324-329, 2007.

A. Artikis, M. Sergot, and G. Paliouras, Run-time composite event recognition, Proc. of the 6th ACM International Conference on Distributed Event-Based Systems, pp.69-80, 2012.

J. F. Allen, Maintaining knowledge about temporal intervals, Communications of the ACM, vol.26, issue.11, pp.832-843, 1983.

F. Pachet, P. Roy, and G. Barbieri, Finite-length Markov processes with constraints, IJCAI, 2011.

J. Bubenzer, Minimization of Acyclic DFAs, Stringology, pp.132-146, 2011.

A. Dries, ProbLog2: Probabilistic logic programming, Joint European Conf. on Machine Learning and Knowledge Discovery in Databases, pp.312-315, 2015.

M. Richardson and P. Domingos, Markov logic networks, Machine Learning, vol.62, pp.107-136, 2006.

S. D. Tran and L. S. Davis, Event modeling and recognition using markov logic networks, Computer Vision-ECCV, pp.610-623, 2008.

A. Skarlatidis, Probabilistic event calculus based on markov logic networks, Proc. of Rule-Based Modeling and Computing on the Semantic Web, ser. LNCS, vol.7018, pp.155-170, 2011.

R. Rincé, R. Kervarc, and P. Leray, On the Use of WalkSAT Based Algorithms for MLN Inference in Some Realistic Applications, 30th International Conf. on Industrial, Engineering, Other Applications of Applied Intelligent Systems, pp.121-131, 2017.

C. Prud'homme, J. Fages, and X. Lorca, , 2017.

H. Kawashima, H. Kitagawa, and X. Li, Complex Event Processing over Uncertain Data Streams, 2010 International Conf. on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC), pp.521-526, 2010.

B. Fazzinga, Efficiently interpreting traces of low level events in business process logs, Information Systems, vol.73, pp.1-24, 2018.

E. , Probabilistic Complex Event Recognition: A Survey, ACM Computing Surveys, vol.50, issue.5, pp.1-31, 2017.

X. Wang and Q. Ji, Context augmented Dynamic Bayesian Networks for event recognition, Pattern Recognition Letters, vol.43, pp.62-70, 2014.

G. Cugola, Introducing uncertainty in complex event processing: Model, implementation, and validation, Computing, vol.97, issue.2, pp.103-144, 2015.

D. Fierens, Inference in probabilistic logic programs using weighted CNF's, Theory and Practice of Logic Programming, vol.15, pp.258-401, 2012.

A. Stolcke, An efficient probabilistic context-free parsing algorithm that computes prefix probabilities, Computational linguistics, vol.21, issue.2, pp.165-201, 1995.