One common problem is the normalization of fault data. For example, SNMP traps are a very common fault format and protocol. Its binary format of enterprise and integer indicating a trap number.

This requires human beings create database lookups (called MIBs) to provide descriptive detail of the fault

. This discounts operational configurations like Up/Down correlation or aging configuration settings. Learning these configurations is a possible area for using machine learning technology.

AI can compare common worded, complete configured trap types and guess what they should be

. Human beings can right-click, update where applicable. The result would be a build-as-you-go rules engine curated

on-the-fly

. Many Managed Service Providers (MSP) find this interesting. Any organization with diverse and changing data set would find it valuable.