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Gaël Bernard

Contact

Graduate Assistant
Department of Information Systems

Assistant of professor Periklis Andritsos

Contact
Gael.Bernard@unil.ch
Internef, room 235
Tel 021.692.34.03

Postal address
Université de Lausanne
Quartier UNIL-Chamberonne
Bâtiment Internef
1015 Lausanne


Quartier UNIL-Dorigny
Bâtiment Internef
Bureau : 139
CH-1015 Lausanne
Téléphone : 021 692 33 14

Publications

8 last publications ordered by: publication type  -  year

: Peer Reviewed

Book Sections

Bernard Gaël , Andritsos Periklis (2019). Accurate and Transparent Path Prediction Using Process Mining. Advances in Databases and Information Systems (pp. 235-250). Springer International Publishing.


Bernard Gaël , Andritsos Periklis (2019). Contextual and Behavioral Customer Journey Discovery Using a Genetic Approach. Advances in Databases and Information Systems (pp. 251-266). Springer International Publishing.


Bernard Gaël , Andritsos Periklis (2018). CJM-ab: Abstracting Customer Journey Maps Using Process Mining. Lecture Notes in Business Information Processing (pp. 49-56). Springer International Publishing.


Proceedings

(2017). Cjm-ex: Goal-oriented exploration of customer journey maps using event logs and data analytics.


(2017). Discovering Customer Journey Maps using a Mixture of Markov Models. SIMPDA2017.


In Proceedings

Bernard G. , Andritsos P. (2017, Jun). A Process Mining Based Model for Customer Journey Mapping. Forum and Doctoral Consortium Papers Presented at the 29th International Conference on Advanced Information Systems Engineering (CAiSE 2017), 1848 (pp. 49-56). CEUR Workshop Proceedings. Peer Reviewed


Bernard G., Boillat T., Legner C. ; Andritsos P. (2016, Dec). When Sales Meet Process Mining: A Scientific Approach to Sales Process and Performance Management. Proceedings of the 37th International Conference on Information Systems (ICIS 2016). Peer Reviewed


In Proceedings (poster)

Bernard Gaël , Andritsos Periklis (2019, May). Discovering Customer Journeys from Evidence: a Genetic Approach Inspired by Process Mining. 31th International Conference on Advanced Information Systems Engineering (CAiSE’19). Peer Reviewed


Keywords

  • process mining, data analytics, datawarehousing, business process management

 
 
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