Synthetic Event Logs for Concept Drift Detection
- Citation Author(s):
-
Victor Gallego-Fontenla
(Universidade de Santiago de Compostela)
- Submitted by:
- Victor Gallego-Fontenla
- Last updated:
- DOI:
- 10.21227/fyrn-4553
- Data Format:
- Research Article Link:
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Abstract
Real life business processes change over time, in both planned and unexpected ways. These changes over time are called concept drifts and its detection is a big challenge in process mining since the inherent complexity of the data makes difficult distinguishing between a change and an anomalous execution. The following logs were generated synthetically in order to prove the quality of different concept drift detection algorithms.
Instructions:
The log files are available in 4 different sizes: 2500, 5000, 7500 and 10000 traces.
Each log has a sudden drift at every 10% of the log.
The change patterns applied to the model are the ones from the paper "Change patterns and change support features - Enhancing flexibility in process-aware information systems".
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