The Doctoral Consortium pursues the objectives to provide valuable feedback and guidance to PhD students from experienced researchers, and to promote the development of a community of scholars including both peers and mentors for future careers. Each of the received submissions has been evaluated by the committee. As a result, nine students’ research proposals were accepted. The topics covered by these proposals tackle open process mining challenges from different perspectives, spanning over prescriptive analytics, deviation detection, pattern exploration, simulation, root-cause analysis, log extraction, trace clustering, and alignments of user-interface logs. The PhD students and senior researchers discussed the presented projects, their directions, methods and plans. The challenges and experiences gathered in the course of a PhD in process mining were also shared by the participants. The ICPM 2020 Tool and Demonstration Track is intended to showcase innovative process mining tools and applications that may come either from research initiatives or from industry. Nine contributions were showcased. Tools for process and performance analysis, conformance checking metrics, data extraction, concept drift analysis, generation of anomalies in event logs, process and rule mining, as well as workbenches for process mining software comparison were presented in this edition. The works demonstrate the support that the emerging applications can offer to researchers, practitioners and industry in the process mining field.

Proceedings of the ICPM Doctoral Consortium and Tool Demonstration Track 2020 co-located with the 2nd International Conference on Process Mining (ICPM 2020), Padua, Italy, October 4-9, 2020 / DI CICCIO, Claudio; Depaire, Benoît; De Weerdt, Jochen; Di Francescomarino, Chiara; Munoz-Gama, Jorge. - (2020).

Proceedings of the ICPM Doctoral Consortium and Tool Demonstration Track 2020 co-located with the 2nd International Conference on Process Mining (ICPM 2020), Padua, Italy, October 4-9, 2020

Claudio Di Ciccio
;
2020

Abstract

The Doctoral Consortium pursues the objectives to provide valuable feedback and guidance to PhD students from experienced researchers, and to promote the development of a community of scholars including both peers and mentors for future careers. Each of the received submissions has been evaluated by the committee. As a result, nine students’ research proposals were accepted. The topics covered by these proposals tackle open process mining challenges from different perspectives, spanning over prescriptive analytics, deviation detection, pattern exploration, simulation, root-cause analysis, log extraction, trace clustering, and alignments of user-interface logs. The PhD students and senior researchers discussed the presented projects, their directions, methods and plans. The challenges and experiences gathered in the course of a PhD in process mining were also shared by the participants. The ICPM 2020 Tool and Demonstration Track is intended to showcase innovative process mining tools and applications that may come either from research initiatives or from industry. Nine contributions were showcased. Tools for process and performance analysis, conformance checking metrics, data extraction, concept drift analysis, generation of anomalies in event logs, process and rule mining, as well as workbenches for process mining software comparison were presented in this edition. The works demonstrate the support that the emerging applications can offer to researchers, practitioners and industry in the process mining field.
2020
Process Mining
DI CICCIO, Claudio; Depaire, Benoît; De Weerdt, Jochen; Di Francescomarino, Chiara; Munoz-Gama, Jorge
06 Curatela::06a Curatela
Proceedings of the ICPM Doctoral Consortium and Tool Demonstration Track 2020 co-located with the 2nd International Conference on Process Mining (ICPM 2020), Padua, Italy, October 4-9, 2020 / DI CICCIO, Claudio; Depaire, Benoît; De Weerdt, Jochen; Di Francescomarino, Chiara; Munoz-Gama, Jorge. - (2020).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1449462
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