Abstract: The present disclosure involves systems, software, and computer implemented methods for correlating critical events to identified log data. An example event log analyzer can identify a set of log messages. One or more occurrences of a first critical event and a time of each of the occurrences are identified. One or more candidate subsets of log messages are identified. Each log message in each candidate subset is associated with a timestamp that is within a predefined time window prior to the time of an occurrence of the first critical event. A candidate subset of log messages is selected as a correlator of the first critical event. A rule is defined using the selected candidate subset of log messages. The rule defines a second critical event that correlates to the first critical event. The rule is associated with one or more actions to perform when the second critical event occurs.

Abstract: The present disclosure involves systems, software, and computer implemented methods for correlating critical events to identified log data. An example event log analyzer can identify a set of log messages. One or more occurrences of a first critical event and a time of each of the occurrences are identified. One or more candidate subsets of log messages are identified. Each log message in each candidate subset is associated with a timestamp that is within a predefined time window prior to the time of an occurrence of the first critical event. A candidate subset of log messages is selected as a correlator of the first critical event. A rule is defined using the selected candidate subset of log messages. The rule defines a second critical event that correlates to the first critical event. The rule is associated with one or more actions to perform when the second critical event occurs.