Detailed explanation of anomaly detection, time series forecasting, and X-View pattern recognition!
JENNIFER provides error events for a variety of error causes. Anomaly detection is designed to detect performance degradation under sudden load. When metrics deviate from the upper or lower limits of the confidence interval, an alert is issued. The occurrence of anomaly detection events is periodically determined with consideration for performance. This period is the measurement time. *For more details, you can view the related links. For more information, please download the PDF or feel free to contact us.*
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We develop, sell, and support the web application monitoring tool "JENNIFER," the Kubernetes monitoring tool "JENNIFER Kubernetes," and the front-end monitoring tool "JENNIFER Front."