Hurra! Human-Readable Router Anomaly Detection

Automated troubleshooting tools must be based on solid and principled algorithms to be useful. However, these tools need to be easily accessible for non-experts, thus requiring to also be usable. This demo combines both requirements by combining an anomaly detection engine inspired by Auto-ML principles, that combines multiple methods to find robust solutions, with automated ranking of results to provide an intuitive interface that is remindful of a search engine. The net result is that HURRA! simplifies as much as possible human operators interaction while providing them with the most useful results first. In the demo, we contrast manual labeling of individual features gathered from human operators from real troubleshooting tickets with results returned by the engine showing an empirically good match at a fraction of the human labor.