Name
Abstract | ||
|---|---|---|
Business process performance may be affected by a range of factors, such as the volume and characteristics of ongoing cases or the performance and availability of individual resources. Event logs collected by modern information systems provide a wealth of data about the execution of business processes. However, extracting root causes for performance issues from these event logs is a major challenge. Processes may change continuously due to internal and external factors. Moreover, there may be many resources and case attributes influencing performance. This paper introduces a novel approach based on time series analysis to detect cause-effect relations between a range of business process characteristics and process performance indicators. The scalability and practical relevance of the approach has been validated by a case study involving a real-life insurance claims handling process. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-59536-8_12 | ADVANCED INFORMATION SYSTEMS ENGINEERING (CAISE 2017) |
Keywords | Field | DocType |
Process mining,Performance analysis,Root cause analysis | Data science,Information system,Time series,Performance indicator,Business process,Computer science,Root cause analysis,Process mining,Scalability | Conference |
Volume | ISSN | Citations |
10253 | 0302-9743 | 1 |
PageRank | References | Authors |
0.41 | 14 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bart F. A. Hompes | 1 | 1 | 0.75 |
| Abderrahmane Maaradji | 2 | 89 | 6.87 |
| marcello la rosa | 3 | 1402 | 81.70 |
| Marlon Dumas | 4 | 5742 | 371.10 |
| Joos C. A. M. Buijs | 5 | 298 | 19.02 |
| W. M. Aalst | 6 | 59 | 7.56 |