Gradual Drift Detection in Process Models Using Conformance Metrics
Changes, planned or unexpected, are common during the execution of real-life processes. Detecting these changes is a must for optimizing the performance of organizations running such processes. Most of the algorithms present in the state-of-the-art focus on the detection of sudden changes, leaving aside other types of changes. In this paper, we will focus on the automatic detection of gradual drifts, a special type of change, in which the cases of two models overlap during a period of time. The proposed algorithm relies on conformance checking metrics to carry out the automatic detection of the changes, performing also a fully automatic classification of these changes into sudden or gradual. The approach has been validated with a synthetic dataset consisting of 120 logs with different distributions of changes, getting better results in terms of detection and classification accuracy, delay and change region overlapping than the main state-of-the-art algorithms.
Code (0)
등록된 구현이 없습니다.
Tasks
Drift DetectionSimilar Papers 제목 키워드 기반
A Conformance Checking-based Approach for Drift Detection in Business Processes
Real life business processes change over time, in both planned and unexpected ways. The detection of these changes is crucial for organizations to ensure that the expected and the real behavior are as similar as possible…
BenchmarkingDrift DetectionDetecting sudden and gradual drifts in business processes from execution traces
Business processes are prone to unexpected changes, as process workers may suddenly or gradually start executing a process differently in order to adjust to changes in workload, season, or other external factors. Early d…
Drift DetectionImproving Real-Time Concept Drift Detection using a Hybrid Transformer-Autoencoder Framework
In applied machine learning, concept drift, which is either gradual or abrupt changes in data distribution, can significantly reduce model performance. Typical detection methods,such as statistical tests or reconstructio…
A Framework for Evaluating and Benchmarking Concept Drift Detection Methods
Data stream mining is fundamentally challenged by concept drift, where distributional changes can degrade model performance. Despite the proliferation of drift detection methods, progress in the field is hindered by inco…
Hyperparameter OptimizationDetecting Concept Drift in the Presence of Sparsity -- A Case Study of Automated Change Risk Assessment System
Missing values, widely called as \textit{sparsity} in literature, is a common characteristic of many real-world datasets. Many imputation methods have been proposed to address this problem of data incompleteness or spars…
Drift DetectionImputationMissing Values