Name
Abstract | ||
|---|---|---|
The manual construction of business process models is a timeconsuming and error-prone task. To ease the construction of such models, several modeling support techniques have been suggested. However, while recommendation systems are widely used e.g. in e-commerce, such techniques are rarely implemented in process modeling tools. The creation of such systems is a complex task since a large number of requirements and parameters have to be addressed. In order to improve the situation, we develop a data model that can serve as a backbone for the development of process modeling recommender systems (PMRS). We systematically develop the model in a stepwise approach using established requirements and validate it against a data model that has been reverse-engineered from a real-world system. We expect that our contribution will provide a useful starting point for designing the data perspective of process modeling recommendation features. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-48393-1_7 | Lecture Notes in Business Information Processing |
Keywords | Field | DocType |
Enterprise process modeling,Recommender systems,Requirements,Data model | Recommender system,Systems engineering,Computer science,Work in process,Process modeling,Business process modeling,Data model | Conference |
Volume | ISSN | Citations |
267.0 | 1865-1348 | 1 |
PageRank | References | Authors |
0.37 | 11 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Fellmann | 1 | 51 | 18.08 |
| Dirk Metzger | 2 | 16 | 5.34 |
| Oliver Thomas | 3 | 79 | 27.56 |