| Original language | English |
|---|---|
| Title of host publication | Encyclopedia of Big Data Technologies |
| Editors | Sherif Sakr , Albert Zomaya |
| Number of pages | 11 |
| Publisher | Springer |
| Publication date | 2018 |
| ISBN (Print) | 978-3-319-63962-8 |
| DOIs | |
| Publication status | Published - 2018 |
Abstract
Streaming process discovery, streaming conformance checking, and streaming process mining in general (also known as online process mining) are disciplines which
analyze event streams to extract a process model or to assess their conformance with respect to a given reference model. The main characteristic of this family of techniques is to analyze events immediately as they are generated (instead of storing them in a log for late processing). This allows to drastically reduce the latency among phases of the BPM lifecycle (cf. Dumas et al (2013)), thus allowing faster process adaptations and better executions.
analyze event streams to extract a process model or to assess their conformance with respect to a given reference model. The main characteristic of this family of techniques is to analyze events immediately as they are generated (instead of storing them in a log for late processing). This allows to drastically reduce the latency among phases of the BPM lifecycle (cf. Dumas et al (2013)), thus allowing faster process adaptations and better executions.