Name
Abstract | ||
|---|---|---|
Business process mining is becoming an increasingly important field for understanding the behavioral perspective of any given organization. In a process mining project, process experts are tasked with discovering or improving the operational business processes. They do so by analyzing event logs, the starting point of any process mining endeavor. Despite event logs capturing behavioral information, we argue that they are also a rich source of domain specific information. This information is not represented explicitly in a process model but, nevertheless, it provides valuable contextual information. To this end, we propose a semi-automatic approach to discover a data model that complements traditional process mining techniques with domain specific information. The approach is evaluated in terms of feasibility by being applied to two real-life event logs. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/978-3-030-62522-1_5 | CONCEPTUAL MODELING, ER 2020 |
Keywords | DocType | Volume |
Process mining, Event log, Data model | Conference | 12400 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dorina Bano | 1 | 1 | 1.72 |
| Mathias Weske | 2 | 3641 | 279.13 |