Authors
Irene Teinemaa, Marlon Dumas, Marcello La Rosa, Fabrizio Maria Maggi
Publication date
2019
Journal
ACM Transactions on Knowledge Discovery in Data
Volume
13
Issue
2
Pages
17:1-17:57
Publisher
ACM
Description
Predictive business process monitoring refers to the act of making predictions about the future state of ongoing cases of a business process, based on their incomplete execution traces and logs of historical (completed) traces. Motivated by the increasingly pervasive availability of fine-grained event data about business process executions, the problem of predictive process monitoring has received substantial attention in the past years. In particular, a considerable number of methods have been put forward to address the problem of outcome-oriented predictive process monitoring, which refers to classifying each ongoing case of a process according to a given set of possible categorical outcomes—e.g., Will the customer complain or not? Will an order be delivered, canceled, or withdrawn? Unfortunately, different authors have used different datasets, experimental settings, evaluation measures, and baselines to …
Total citations
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Scholar articles
I Teinemaa, M Dumas, ML Rosa, FM Maggi - ACM Transactions on Knowledge Discovery from Data …, 2019