Abstract
Automated process discovery represents the defining capability of process mining. By exploiting transactional data from information systems, it aims to extract valuable process knowledge. Through process mining, an important link between two disciplines – data mining and business process management – has been established. However, while methods of both data mining and process management are well- established in practice, the potential of process mining for evaluation of business operations has only been recently recognised outside academia. Our quantitative analysis of real-life event log data investigates both the performance and social dimensions of a selected core business process of an Austrian IT service company. It shows that organisations can substantially benefit from adopting automated process discovery methods to visualise, understand and evaluate their processes. This is of particular relevance in today’s world of data-driven decision making.
Original language | English |
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Title of host publication | Proceedings of the 6th International Symposium on Data-driven Process Discovery and Analysis (SIMPDA 2016) |
Editors | Paolo Ceravolo, Christian Guetl, Stefanie Rinderle-Ma |
Place of Publication | Graz, Austria |
Publisher | CEUR Workshop Proceedings |
Pages | 35 - 49 |
Publication status | Published - 2016 |
Austrian Classification of Fields of Science and Technology (ÖFOS)
- 102022 Software development
- 102
- 102001 Artificial intelligence
- 502
- 502050 Business informatics