Name
Title | ||
|---|---|---|
Declarative Process Mining: Reducing Discovered Models Complexity by Pre-Processing Event Logs. |
Abstract | ||
|---|---|---|
The discovery of declarative process models by mining event logs aims to represent flexible or unstructured processes, making them visible to business and improving their manageability. Although promising, the declarative perspective may still produce models that are hard to understand, both due to their size and to the high number of restrictions of the process activities. This work presents an approach to reduce declarative model complexity by aggregating activities according to inclusion and hierarchy semantic relations. The approach was evaluated through a case study with an artificial event log and its results showed complexity reduction on the resulting hierarchical model. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-10172-9_28 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
process mining,declarative modeling | Data mining,Computer science,Process modeling,Reduction (complexity),Artificial intelligence,Hierarchy,Hierarchical database model,Machine learning,Model complexity,Process mining | Conference |
Volume | ISSN | Citations |
8659 | 0302-9743 | 4 |
PageRank | References | Authors |
0.42 | 12 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pedro H. Piccoli Richetti | 1 | 10 | 2.26 |
| Fernanda Araujo Baiao | 2 | 40 | 3.72 |
| Flavia Santoro | 3 | 71 | 11.51 |