论文标题
用于服务监控数据的自适应学习
Adaptive Learning for Service Monitoring Data
论文作者
论文摘要
服务监视应用程序不断生成数据以监视其可用性。因此，实时，准确地对传入数据进行分类至关重要。为此，我们的研究使用学习++开发了一种自适应分类方法，该方法可以处理不断发展的数据分布。这种方法依次预测并更新了使用新数据的监视模型，逐渐忘记了过去的知识并确定了突然的概念漂移。我们采用从工业应用获得的连续数据块来逐步评估预测变量的性能。
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.
