Prioritizing log messages
AssigneeHewlett-Packard Development Company, L.P.
InventorsEran Samuni, Fernando Vizer, Liya Brodin
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Abstract
Prioritizing log messages can include generating a cluster of a plurality of log messages relating to an event and receiving feedback from a number of users relating to an event relevance of the cluster. Prioritizing can also include isolating a number of log messages based on the feedback and predicting a number of future events utilizing the isolated number of log messages.
Claims15
A method for prioritizing log messages, comprising:
- generating a cluster of a plurality of log messages relating to an event;
- receiving feedback, from a number of users via a communication link, relating to an event relevance of the cluster;
- isolating a number of log messages based on the feedback;
- and
- predicting a number of future events utilizing the isolated number of log messages.
A non-transitory machine-readable medium storing a set of instructions executable by a processor to cause a computer to:
- receive a plurality of log messages;
- generate a cluster of the plurality of log messages relating to an event;
- provide a user interface to receive feedback, from a number of users, relating to an event relevance of the cluster;
- calculate a score for each of the plurality of log messages within the cluster based on the feedback;
- isolate a number of log messages based on the score;
- and
- predict a number of future events utilizing the isolated number of log messages.
A system for prioritizing log messages, comprising a processing resource in communication with a non-transitory machine readable medium having instructions executed by the processing resource to implement a cluster engine, a feedback engine, an isolation engine and a prediction engine, wherein:
- the cluster engine accesses a plurality of log messages;
- the cluster engine generates a plurality of clusters comprising the plurality of log messages relating to an event, wherein the plurality of clusters are based on a pattern of log message features;
- the feedback engine receives feedback from a number of users, via a user interface, relating to an event relevance of the cluster;
- the feedback engine calculates a score for each of the plurality of log messages within the cluster based on the feedback;
- the isolation engine isolates a number of log messages based on the score;
- and
- the prediction engine predicts a number of future events utilizing the isolated number of log messages.
Dependencies are inferred from the claim text.