US 12,224,896 B2
Scoring events as likely cause events in a resource network
Kristian Jon Stewart, Surrey (GB); Jonathan Ian Settle, Dursley (GB); and Yasser Abduallah, Bayonne, NJ (US)
Assigned to International Business Machines Corporation, Armonk, NY (US)
Filed by INTERNATIONAL BUSINESS MACHINES CORPORATION, Armonk, NY (US)
Filed on May 14, 2021, as Appl. No. 17/320,277.
Prior Publication US 2022/0368586 A1, Nov. 17, 2022
Int. Cl. H04L 41/5041 (2022.01); H04L 9/40 (2022.01); H04L 41/0631 (2022.01); H04L 43/04 (2022.01); H04L 45/00 (2022.01); H04L 47/2408 (2022.01); H04L 47/2483 (2022.01); H04L 67/10 (2022.01); H04L 67/1014 (2022.01)
CPC H04L 41/0631 (2013.01) [H04L 43/04 (2013.01)] 21 Claims
OG exemplary drawing
 
1. A computer-implemented method for scoring events as likely cause events in a resource network, comprising:
processing incoming events relating to resources in a resource network to correlate related events as a related group;
classifying each of the incoming events into a category, wherein the categories represent event status types that cause events, and wherein each category has a different event classification weight;
mapping the events of the related group onto nodes of a sub-topology representing resources of the resource network and having edges representing relationships between the resources;
scoring each event by discovering one or more paths between a node event and other node events of the related group, wherein the scoring is based on a combination of an event classification weight of the node event and a discovered path score comprising aggregated directional relationship scores of edges of a discovered path from the node event to another node event of the related group, wherein a directional relationship score of an edge of the discovered path is configured according to a direction of relationship from the node event, and wherein the scoring indicates a likelihood of an event being a proximate cause event in the related group; and
dynamically mapping and scoring events such that the scores are updated as new incoming correlated related events occur, wherein the dynamic mapping comprises extending the sub-topology as required to include new nodes representing resources of the new incoming correlated related events, and wherein the scoring is resource agnostic by being based on event classification weight rather than a node weight.