Name
Abstract | ||
|---|---|---|
In addition to the classical exploitation as a means for checking process enactment conformance, process models may be used to predict which activities will be carried out next. The prediction performance may provide indirect indications on the correctness and reliability of a process model. This paper proposes a strategy for activity prediction using the WoMan framework for workflow management. It extends a previous approach, that has proved to be able to handle complex processes. Experimental results on different domains show an increase in prediction performance compared to the previous approach. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-60438-1_36 | Lecture Notes in Artificial Intelligence |
Keywords | Field | DocType |
Process mining,Activity prediction,Process model | Data mining,Computer science,Correctness,Process modeling,Artificial intelligence,Workflow,Machine learning,Process mining | Conference |
Volume | ISSN | Citations |
10352 | 0302-9743 | 2 |
PageRank | References | Authors |
0.37 | 7 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stefano Ferilli | 1 | 722 | 101.11 |
| Floriana Esposito | 2 | 2434 | 277.96 |
| Domenico Redavid | 3 | 51 | 15.17 |
| Sergio Angelastro | 4 | 11 | 3.61 |