Assigning a relevance score to a new metric using natural language processing

Abstract

Disclosed herein is a system for determining scores that are usable to filter a larger set of metrics (e.g., thousands of metrics) down to a smaller set of relevant metrics (e.g., hundreds of metrics) that can be more efficiently queried and ingested for root-cause analysis of an incident. During a training stage, the system analyzes known incidents and converts the names of the metrics, as described via customer-defined words, into mathematical representations (e.g., word embedding featurization vectors). When a new metric with a new name is received for a new incident, the system implements an incident inference stage during which the new name is converted into a new mathematical representation. The system compares the new mathematical representation to the mathematical representations to identify a similar mathematical representation. The system retrieves the score for the metric associated with the similar mathematical representation and assigns the retrieved score to the new metric.