Name
Title | ||
|---|---|---|
Survey and Cross-benchmark Comparison of Remaining Time Prediction Methods in Business Process Monitoring |
Abstract | ||
|---|---|---|
Predictive business process monitoring methods exploit historical process execution logs to generate predictions about running instances (called cases) of a business process, such as the prediction of the outcome, next activity, or remaining cycle time of a given process case. These insights could be used to support operational managers in taking remedial actions as business processes unfold, e.g., shifting resources from one case onto another to ensure the latter is completed on time. A number of methods to tackle the remaining cycle time prediction problem have been proposed in the literature. However, due to differences in their experimental setup, choice of datasets, evaluation measures, and baselines, the relative merits of each method remain unclear. This article presents a systematic literature review and taxonomy of methods for remaining time prediction in the context of business processes, as well as a cross-benchmark comparison of 16 such methods based on 17 real-life datasets originating from different industry domains.
|
Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3331449 | ACM Transactions on Intelligent Systems and Technology |
Keywords | DocType | Volume |
Business process,machine learning,predictive monitoring,process mining,process performance indicator | Journal | 10 |
Issue | ISSN | Citations |
4 | 2157-6904 | 6 |
PageRank | References | Authors |
0.47 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ilya Verenich | 1 | 6 | 0.47 |
| Marlon Dumas | 2 | 5742 | 371.10 |
| marcello la rosa | 3 | 1402 | 81.70 |
| Fabrizio Maria Maggi | 4 | 46 | 20.83 |
| Irene Teinemaa | 5 | 29 | 3.99 |