Adaptive Learning for Service Monitoring Data
This work addresses the need for accurate real-time classification in service monitoring applications, but it appears incremental as it builds on existing methods like Learn++ for handling concept drift.
The study tackled the problem of real-time classification of evolving service monitoring data by developing an adaptive classification approach using Learn++, which achieved incremental performance evaluation on industrial data.
Service monitoring applications continuously produce data to monitor their availability. Hence, it is critical to classify incoming data in real-time and accurately. For this purpose, our study develops an adaptive classification approach using Learn++ that can handle evolving data distributions. This approach sequentially predicts and updates the monitoring model with new data, gradually forgets past knowledge and identifies sudden concept drift. We employ consecutive data chunks obtained from an industrial application to evaluate the performance of the predictors incrementally.