ALSR: An Adaptive Label Screening and Relearning Approach for Anomaly Detection Anomaly detection using KPIs (Key Performance Indicators) is key to AIOps (Artificial Intelligence for IT Operations). Recent anomaly detection approaches have adopted machine learning to detect anomalies on the perspective of individual time points more than events. These approaches do not make effective use of the labels of continuous anomaly intervals, nor do they pay attention to the differences among anomaly points. The detection performance is therefore not high enough. In this paper, we propose an anomaly detection approach named ALSR, which uses a label screening model and a relearning model to analyze and utilize the continuous anomaly intervals of KPIs in finer granularity. The label screening algorithm takes advantage of the continuity of anomaly intervals to remove unnecessary data from the training set, so as to better suit to intervaloriented anomaly detection. The relearning algorithm reclassifies the truefalse positive points within range of detected anomalies, thus effectively reduces the number of false positive points. ALSR uses statistical characteristics and time series models for feature extraction, and the feature set is proved to better describe the characteristics of KPIs. We conduct comprehensive experiments on 25 KPIs, and the total Fscore of ALSR is 0.965, which outperforms stateoftheart anomaly detection approaches.

Using anomaly detection to support classification of fast running packaging processes In this paper we propose a new method to assist in labeling data arriving from fast running processes using anomaly detection. A result is the possibility to manually classify data arriving at a high rates to train machine learning models. To circumvent the problem of not having a real ground truth we propose specific metrics for model selection and validation of the results.The use case is taken from the food packaging industry, where processes are affected by regular but short breakdowns causing interruptions in the production process. Fast production rates make it hard for machine operators to identify the source and thus the cause of the breakdown. Self learning assistance systems can help them finding the root cause of the problem and assist the machine operator in applying lasting solutions. These learning systems need to be trained to identify reoccurring problems using data analytics. Training is not easy as the process is too fast to be manually monitored to add specific classifications on the single data points.