Near real-time system or network incident detection

A method, comprising:

• receiving and analyzing, by a system or network incident detection and analysis computing system, historical system or network incident data including a plurality of historical incidents;
• quantifying, by the system or network incident detection and analysis computing system, each historical incident of the plurality of historical incidents to determine a quantified magnitude of an impact of each historical incident;
• comparing, by the system or network incident detection and analysis computing system, the quantified magnitude of the impact of each historical incident to a predetermined threshold to generate a machine learning data set including historical incidents having a quantified magnitude above the predetermined threshold, the historical incidents having a quantified magnitude above the predetermined threshold being a portion of the plurality of historical incidents;
• extracting, from each incident of the plurality of historical incidents, keywords;
• evaluating the keywords to identify inclusion and exclusion criteria, the inclusion criteria including identified sequences of keywords indicating an impact above the predetermining threshold and the exclusion criteria including identified sequences of keywords indicating an impact below the predetermined threshold;
• receiving, by the system or network incident detection and analysis computing system, an incoming incident;
• and
• pre-processing, by the system or network incident detection and analysis computing system, the incoming incident, including:
• analyzing, by the system or network incident detection and analysis computing system, data associated with the incoming incident;
• comparing, by the system or network incident detection and analysis computing system, the analyzed data with the machine learning data set including the one or more historical incidents having a quantified magnitude above the predetermined threshold;
• comparing, by the system or network incident detection and analysis computing system, the analyzed data from the incoming incident with the identified inclusion and exclusion criteria to determine a likelihood that the incoming incident will have a magnitude of impact above the predetermined threshold;
• based on the comparison to the machine learning data set and the inclusion and exclusion criteria, determining, by the system or network incident detection and analysis computing system a likelihood that the incoming incident will have a magnitude of impact above the predetermined threshold;
• responsive to determining that the likelihood that the incoming incident will have a magnitude of impact above the predetermined threshold, aggregating, by the system or network incident detection and analysis computing system the analyzed data from the incoming incident and the data from the one or more historical incidents to generate a completed analysis data set;
• and
• transmitting, by the system or network incident detection and analysis computing system, the completed analysis data set for further processing.

One or more non-transitory computer-readable media storing instructions that, when executed by at least one computer system comprising at least one processor, memory, and a communication interface, cause the at least one computer system to:

• receive and analyze historical system or network incident data including a plurality of historical incidents;
• quantify each historical incident of the plurality of historical incidents to determine a quantified magnitude of an impact of each historical incident;
• compare the quantified magnitude of the impact of each historical incident to a predetermined threshold to generate a machine learning data set including historical incidents having a quantified magnitude above the predetermined threshold, the historical incidents having a quantified magnitude above the predetermined threshold being a portion of the plurality of historical incidents;
• extract, from each incident of the plurality of historical incidents, keywords;
• evaluate the keywords to identify inclusion and exclusion criteria, the inclusion criteria including identified sequences of keywords indicating an impact above the predetermining threshold and the exclusion criteria including identified sequences of keywords indicating an impact below the predetermined threshold;
• receive an incoming incident;
• and
• pre-processing the incoming incident, including:
• analyzing data associated with the incoming incident;
• comparing the analyzed data with the machine learning data set including the one or more historical incidents having a quantified magnitude above the predetermined threshold;
• compare the analyzed data from the incoming incident with the identified inclusion and exclusion criteria to determine a likelihood that the incoming incident will have a magnitude of impact above the predetermined threshold;
• based on the comparison to the machine learning data set and the inclusion and exclusion criteria, determining a likelihood that the incoming incident will have a magnitude of impact above the predetermined threshold;
• responsive to determining that the likelihood that the incoming incident will have a magnitude of impact above the predetermined threshold, aggregating the analyzed data from the incoming incident and data from the one or more historical incidents to generate a completed analysis data set;
• and
• transmitting the completed analysis data set for further processing.