Name
Abstract | ||
|---|---|---|
This paper discusses the integration of Nirdizati, a tool for predictive process monitoring, into the Web-based process analytics platform Apromore. Through this integration, Apromore's users can use event logs stored in the Apromore repository to train a range of predictive models, and later use the trained models to predict various performance indicators of running process cases from a live event stream. For example, one can predict the remaining time or the next events until case completion, the case outcome, or the violation of compliance rules or internal policies. The predictions can be presented graphically via a dashboard that offers multiple visualization options, including a range of summary statistics about ongoing and past process cases. They can also be exported into a text file for periodic reporting or to be visualized in third-parties business intelligence tools. Based on these predictions, operations managers may identify potential issues early on, and take remedial actions in a timely fashion, e.g. reallocating resources from one case onto another to avoid that the case runs overtime. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-319-92901-9_21 | Lecture Notes in Business Information Processing |
Keywords | Field | DocType |
Process mining,Predictive monitoring,Business process,Machine learning | Data science,Data mining,Performance indicator,Business process,Computer science,Visualization,Summary statistics,Dashboard (business),Business intelligence,Analytics,Process mining | Conference |
Volume | ISSN | Citations |
317 | 1865-1348 | 1 |
PageRank | References | Authors |
0.35 | 3 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ilya Verenich | 1 | 33 | 2.68 |
| Stanislav Mõskovski | 2 | 1 | 0.35 |
| Simon Raboczi | 3 | 7 | 2.11 |
| Marlon Dumas | 4 | 5742 | 371.10 |
| marcello la rosa | 5 | 1402 | 81.70 |
| Fabrizio Maria Maggi | 6 | 832 | 45.07 |