Name
Abstract | ||
|---|---|---|
In several organizations, it has become increasingly popular to document and log the steps that makeup a typical business process. In some situations, a normative workflow model of such processes is developed, and it becomes important to know if such a model is actually being followed by analyzing the available activity logs. In other scenarios, no model is available and, with the purpose of evaluating cases or creating new production policies, one is interested in learning a workflow representation of such activities. In either case, machine learning tools that can mine workflow models are of great interest and still relatively unexplored. We present here a probabilistic workflow model and a corresponding learning algorithm that runs in polynomial time. We illustrate the algorithm on example data derived from a real world workflow. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1145/1081870.1081903 | KDD |
Keywords | Field | DocType |
workflow representation,available activity log,great interest,new production policy,probabilistic workflow model,graphical models,normative workflow model,example data,real world workflow,polynomial time,probabilistic workflow mining,causal models,workflow mining,corresponding learning algorithm,business process,graphical model,machine learning | Data science,Data mining,Computer science,Artificial intelligence,Probabilistic logic,Time complexity,Workflow engine,Workflow,Workflow technology,Business process,Graphical model,Workflow management system,Machine learning | Conference |
ISBN | Citations | PageRank |
1-59593-135-X | 30 | 2.16 |
References | Authors | |
6 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ricardo Silva | 1 | 41 | 4.77 |
| Jiji Zhang | 2 | 149 | 17.52 |
| James G. Shanahan | 3 | 452 | 57.60 |