IDENTIFYING CORRELATED OPERATION MANAGEMENT EVENTS

• US 20120078903A1
• Filed: 09/23/2010
• Published: 03/29/2012
• Est. Priority Date: 09/23/2010
• Status: Abandoned Application

• Current Assignee: Micro Focus LLC (Open Text Corporation)
• Original Assignee: Micro Focus LLC (Open Text Corporation)
• Inventors: Gupta, Chetan Kumar, Mehta, Abhay, Wang, Song, Bergstein, Stefan
• Application Number:
  US12/888,800
• Publication Number:
  US 20120078903A1
• Time in Patent Office: Days
• US Class Current: 707/737
• CPC Class Codes: G06F 11/0724 in a multiprocessor or a multi-core unit multiprocessors per se G06F15/80
• CPC Class Codes: G06F 11/079 Root cause analysis, i.e. error or fault diagnosis in a hardware test environment G06F11/22; in a software test environment G06F11/36

Abstract

A technique includes receiving data indicative of operation management events, where each event occurs at an associated time. The technique includes processing the data to selectively group the events in episodes based on the associated times and identifying which events are correlated based at least in part on the episodes.

Claims

1. A method comprising:

receiving data indicative of operation management events, each event occurring at an associated time;

processing the data in a machine to selectively group the events in episodes based on the associated times; and

identifying which events are correlated based at least in part on the episodes.

2. The method of claim 1, further comprising:

classifying the events according to event types, comprising for each event, subdividing a description of the event into tokens and classifying the event based on a comparison of the tokens with tokens derived from the other event descriptions.

3. The method of claim 1, wherein the identifying comprises determining whether given events are correlated based on an examination of all of the episodes to determine whether the given events occur together across a significant number of the episodes.

4. The method of claim 1, wherein the processing the data to selectively group the events comprises selectively grouping the events based on events that occur within a predetermined duration of time of each other.

5. The method of claim 1, wherein the processing the data to selectively group the events further comprises selectively removing events from the grouping based on a frequency at which the event occurs.

6. The method of claim 1, further comprising:

determining correlation rules for correlated events.

10. The method of claim 1, the storage medium storing instructions that when executed by the computer cause the computer to selectively remove events from being considered for inclusion in one of the episodes based on a frequency at which the event occurs.

11. The method of claim 1 the storage medium storing instructions that when executed by the computer cause the computer to classify the events according to event types and process the event types to organize the events into the episodes.

12. The method of claim 11, the storage medium storing instructions that when executed by the computer cause the computer to, for each event, subdivide a description of the event into tokens and classify the event based on a comparison of the tokens with tokens derived from at least one of the other events.

13. The method of claim 11, the storage medium storing instructions that when executed by the computer cause the computer to determine the event type based at least in part on an affiliated application.

7. An article comprising a computer readable storage medium to store instructions that when executed by a computer cause the computer to:

receive data indicative of events occurring in a system, each event occurring at an associated time;

process the data to selectively organize the events in episodes based on the associated times; and

submit each of the episodes to a data miner to identify whether any correlation rules are associated with the episode.

8. The article of claim 7, the storage medium storing instructions that when executed by the computer cause the computer to selectively organize the events in the episodes such that events that occur within a predetermined time of each other are grouped in the same episode.

9. The article of claim 8, wherein the associated times span a time range, the storage medium storing instructions that when executed by the computer cause the computer to slide a window of time over the range and select events having associated times that fall within time boundaries indicated by the window for inclusion in the same episode.

14. An apparatus comprising:

a log to store data indicative of operation management events, each event occurring at an associated time; and

a processor-based episode creator to selectively group the events in episodes based on the associated times and for each episode, communicate data indicative of the episode to a data miner to determine whether events of the episode are correlated.

15. The apparatus of claim 14, wherein the episode creator selectively groups the events based on events that occur within a predetermined duration of time of each other.

16. The apparatus of claim 14, wherein the episode creator selectively removing events from the grouping based on a frequency at which the event occurs.

17. The apparatus of claim 14, wherein the episode creator classifies the events according to event types.

18. The apparatus of claim 14, wherein the episode creator, for each event, subdivides the events into tokens, and classifies the event based on a comparison of the tokens with tokens derived from the other events.

19. The apparatus of claim 14, wherein the episode creator communicates data to the data miner indicative of a support threshold specifying how often events are to occur before the events are otherwise considered to be correlated.

20. The apparatus of claim 14, wherein the episode creator communicates data to the data miner indicative of a confidence threshold specifying a conditional probability for two events before the events are otherwise considered to be correlated.