Name
Abstract | ||
|---|---|---|
Nowadays, all kinds of information systems store detailed information in logs. Examples of such systems include classical workow management systems (Staware), ERP systems (SAP), case handling systems (FLOWer), PDM systems (Windchill), CRM systems (Microsoft Dynamics CRM), middleware (IBM WebSphere), hospital information systems (Chipsoft), but also embedded systems like medical systems (X-ray machines), mobile phones, car entertainment systems, produc- tion systems (e.g., wafer steppers), copiers, and sensor networks. Process mining has emerged as a way to analyze these systems based on these detailed logs. Unlike classical data mining, the focus of process mining is on processes. First, process mining allows us to extract a process model from an event log. Second, it allows us to detect discrepancies between a modeled process (as it was envisioned to be) and an event log (as it actually is). Third, it can enrich an existing model with knowledge de- rived from an event log. This demo shows our tool ProM, which is the world-leading tool in the area of process mining. |
Year | Venue | Keywords |
|---|---|---|
2009 | BPM (Demos) | sensor network,process model,embedded system,middleware,process mining,data mining,management system,information system |
DocType | Citations | PageRank |
Conference | 45 | 1.84 |
References | Authors | |
7 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wil Van Der Aalst | 1 | 20894 | 1418.27 |
| Boudewijn F. van Dongen | 2 | 1875 | 97.84 |
| Christian W. Günther | 3 | 800 | 34.39 |
| Anne Rozinat | 4 | 325 | 17.75 |
| Eric Verbeek | 5 | 559 | 29.66 |
| Ton Weijters | 6 | 1165 | 71.80 |