From knowledge-driven to data-driven inter-case feature encoding in predictive process monitoring-Reference-Cited by-同舟云学术

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From knowledge-driven to data-driven inter-case feature encoding in predictive process monitoring

Published:2019-09 Issue: Volume:84 Page:255-264
ISSN:0306-4379
Container-title:Information Systems
language:en
Short-container-title:Information Systems

Author:

Senderovich Arik,Francescomarino Chiara Di,Maggi Fabrizio Maria

Funder

Lyon Sachs Postdoctoral Fellowship Foundation, Canada

Estonian Research Council

Publisher

Elsevier BV

Subject

Hardware and Architecture,Information Systems,Software

Reference30 articles.

1. Process mining: Data science in action;van der Aalst,2016

2. Time prediction based on process mining;van der Aalst;Inf. Syst.,2011

3. F.M. Maggi, C. Di Francescomarino, M. Dumas, C. Ghidini, Predictive monitoring of business processes, in: Advanced Information Systems Engineering - 26th International Conference, CAiSE 2014, Thessaloniki, Greece, June 16–20, 2014, Proceedings, 2014, pp. 457–472.

4. A. Leontjeva, R. Conforti, C. Di Francescomarino, M. Dumas, F.M. Maggi, Complex symbolic sequence encodings for predictive monitoring of business processes, in: Business Process Management - 13th International Conference, BPM 2015, Innsbruck, Austria, August 31–September 3, 2015, Proceedings, 2015, pp. 297–313.

5. I. Teinemaa, M. Dumas, F.M. Maggi, C. Di Francescomarino, Predictive business process monitoring with structured and unstructured data, in: Business Process Management - 14th International Conference, BPM 2016, Rio de Janeiro, Brazil, September 18–22, 2016, Proceedings 2016, pp. 401–417.

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