Synthetic Event Logs for Concept Drift Detection

Synthetic Event Logs for Concept Drift Detection

Citation Author(s):
Victor
Gallego-Fontenla
Universidade de Santiago de Compostela
Submitted by:
Victor Gallego-...
Last updated:
Tue, 07/09/2019 - 14:07
DOI:
10.21227/fyrn-4553
Data Format:
License:
Creative Commons Attribution
Dataset Views:
22
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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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[1] , "Synthetic Event Logs for Concept Drift Detection", IEEE Dataport, 2019. [Online]. Available: http://dx.doi.org/10.21227/fyrn-4553. Accessed: Jul. 16, 2019.
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. (2019). Synthetic Event Logs for Concept Drift Detection. IEEE Dataport. http://dx.doi.org/10.21227/fyrn-4553
, 2019. Synthetic Event Logs for Concept Drift Detection. Available at: http://dx.doi.org/10.21227/fyrn-4553.
. (2019). "Synthetic Event Logs for Concept Drift Detection." Web.
1. . Synthetic Event Logs for Concept Drift Detection [Internet]. IEEE Dataport; 2019. Available from : http://dx.doi.org/10.21227/fyrn-4553
. "Synthetic Event Logs for Concept Drift Detection." doi: 10.21227/fyrn-4553