Extended dynamic intelligent log analysis tool

A non-transitory, computer-readable medium storing one or more instructions executable by a computer system to perform operations comprising:

• obtaining a log file comprising a plurality of log entries, each log entry comprising an error message and a timestamp;
• identifying communities of errors, where identifying communities of errors comprises:
• creating an error type for each unique error message in the plurality of log entries;
• dividing the log file into a plurality of sessions, each session representing a predetermined period of time;
• generating a graph, wherein each error type is plotted as a node in the graph;
• determining, for a plurality of node pairs, a number of sessions in which both nodes of each node pair occur;
• plotting edges between each node pair of the plurality of node pairs;
• assigning a weight to each edge based on the determined number of sessions;
• and
• performing a community detection algorithm on the graph to identify communities of errors;
• and
• identifying anomalous sessions by identifying sessions comprising errors from more than one community.

A computer-implemented system, comprising:

• one or more computers;
• and
• one or more computer memory devices interoperably coupled with the one or more computers and having tangible, non-transitory, machine-readable media storing one or more instructions that, when executed by the one or more computers, perform one or more operations comprising:
• obtaining a log file comprising a plurality of log entries, each log entry comprising an error message and a timestamp;
• identifying communities of errors, where identifying communities of errors comprises:
• creating an error type for each unique error message in the plurality of log entries;
• dividing the log file into a plurality of sessions, each session representing a predetermined period of time;
• generating a graph, wherein each error type is plotted as a node in the graph;
• determining, for a plurality of node pairs, a number of sessions in which both nodes of each node pair occur;
• plotting edges between each node pair of the plurality of node pairs;
• assigning a weight to each edge based on the determined number of sessions;
• and
• performing a community detection algorithm on the graph to identify communities of errors;
• and
• identifying anomalous sessions by identifying sessions comprising errors from more than one community.