Professeurs et Recherche

Gaël Bernard

Coordonnées

Assistant diplômé
Département des systèmes d'information

Assistant du professeur Periklis Andritsos

Contact
Gael.Bernard@unil.ch
Internef, bureau 235
Tél 021.692.34.03

Adresse postale
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 dernières publications classées par: type de publication - année

: Revue avec comité de lecture

Parties de livre

	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.

Actes de conférence

	(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.

Actes de conférence (partie)

	Bernard G. , Andritsos P. (2017, Juin). 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.

	Bernard G., Boillat T., Legner C. ; Andritsos P. (2016, Déc). 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).

Actes de conférence (poster)

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

Mots-clés

process mining, data analytics, datawarehousing, business process management