A Survey on Concept Drift in Process Mining
Concept drift in process mining (PM) is a challenge as classical methods assume processes are in a steady-state, i.e., events share the same process version. We conducted a systematic literature review on the intersection of these areas, and thus, we review concept drift in process mining and bring forward a taxonomy of existing techniques for drift detection and online process mining for evolving environments. Existing works depict that (i) PM still primarily focuses on offline analysis, and (ii) the assessment of concept drift techniques in processes is cumbersome due to the lack of common evaluation protocol, datasets, and metrics.
Code (0)
등록된 구현이 없습니다.
Tasks
Drift DetectionSurveySystematic Literature ReviewSimilar Papers 제목 키워드 기반
CONDA-PM -- A Systematic Review and Framework for Concept Drift Analysis in Process Mining
Business processes evolve over time to adapt to changing business environments. This requires continuous monitoring of business processes to gain insights into whether they conform to the intended design or deviate from …
ManagementSystematic Literature ReviewHandling Concept Drift for Predictions in Business Process Mining
Predictive services nowadays play an important role across all business sectors. However, deployed machine learning models are challenged by changing data streams over time which is described as concept drift. Prediction…
BIG-bench Machine LearningA Framework for Explainable Concept Drift Detection in Process Mining
Rapidly changing business environments expose companies to high levels of uncertainty. This uncertainty manifests itself in significant changes that tend to occur over the lifetime of a process and possibly affect its pe…
Drift DetectionOne or Two Things We know about Concept Drift -- A Survey on Monitoring Evolving Environments
The world surrounding us is subject to constant change. These changes, frequently described as concept drift, influence many industrial and technical processes. As they can lead to malfunctions and other anomalous behavi…
Anomaly DetectionDrift DetectionSystematic Literature ReviewAdvances on Concept Drift Detection in Regression Tasks using Social Networks Theory
Mining data streams is one of the main studies in machine learning area due to its application in many knowledge areas. One of the major challenges on mining data streams is concept drift, which requires the learner to d…
Drift Detectionregression