Real-time prediction and explanation of sequences of abnormal events

A non-transitory computer-readable storage medium coupled to one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations for automated provision of an explanation for an event, and one or more actions to resolve the event, the operations comprising:

• providing a semantic sequence based on a sequence associated with the event, and context data provided from a knowledge graph;
• receiving a set of cases associated with at least one entity included in the event, the set of cases comprising previous instances of events, and actions performed to resolve the previous instances of events;
• defining a sub-set of cases from the set of cases based on the context data;
• for each case of the sub-set of cases, determining a similarity score, each similarity score representing a degree of similarity between the event, and a case of the sub-set of cases;
• determining an explanation based on features of cases in the sub-set of cases based on the context data;
• and
• providing one or more actions based on actions of cases in the sub-set of cases.

A system, comprising:

• one or more processors;
• and
• a computer-readable storage device coupled to the one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations for automated provision of an explanation for an event, and one or more actions to resolve the event, the operations comprising:
• providing a semantic sequence based on a sequence associated with the event, and context data provided from a knowledge graph;
• receiving a set of cases associated with at least one entity included in the event, the set of cases comprising previous instances of events, and actions performed to resolve the previous instances of events;
• defining a sub-set of cases from the set of cases based on the context data;
• for each case of the sub-set of cases, determining a similarity score, each similarity score representing a degree of similarity between the event, and a case of the sub-set of cases;
• determining an explanation based on features of cases in the sub-set of cases based on the context data;
• and
• providing one or more actions based on actions of cases in the sub-set of cases.