International business machines corporation (20240104398). ARTIFICIAL INTELLIGENCE DRIVEN LOG EVENT ASSOCIATION simplified abstract
Contents
- 1 ARTIFICIAL INTELLIGENCE DRIVEN LOG EVENT ASSOCIATION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 ARTIFICIAL INTELLIGENCE DRIVEN LOG EVENT ASSOCIATION - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
ARTIFICIAL INTELLIGENCE DRIVEN LOG EVENT ASSOCIATION
Organization Name
international business machines corporation
Inventor(s)
Zachary A. Silverstein of Georgetown TX (US)
Hernan A. Cunico of Holly Springs NC (US)
Jonathan D. Dunne of Dungarvan (IE)
Nageswara Sastry Renduchintala of Bengaluru (IN)
ARTIFICIAL INTELLIGENCE DRIVEN LOG EVENT ASSOCIATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240104398 titled 'ARTIFICIAL INTELLIGENCE DRIVEN LOG EVENT ASSOCIATION
Simplified Explanation
The computer implemented method described in the abstract processes log events by determining the dependence and independence of training log events, clustering them to form clustered training log events, generating an event association model using the clustered training log events, and then clustering the log events using the event association model.
- The method processes log events by determining their dependence and independence.
- It clusters training log events based on the determined dependence and independence.
- An event association model is generated using the clustered training log events.
- The log events are then clustered using the event association model.
Potential Applications
This technology could be applied in various fields such as cybersecurity, anomaly detection, and predictive maintenance.
Problems Solved
This technology helps in organizing and analyzing log events efficiently, identifying patterns, and improving decision-making processes based on the data.
Benefits
The benefits of this technology include improved data analysis, enhanced system performance, and better understanding of event relationships.
Potential Commercial Applications
One potential commercial application of this technology could be in the development of advanced data analytics software for businesses.
Possible Prior Art
One possible prior art for this technology could be existing log event processing systems used in cybersecurity and data analysis.
Unanswered Questions
How does this technology handle real-time log event processing?
The abstract does not specify whether this method can handle real-time log event processing or if it is limited to batch processing.
What is the scalability of this technology for processing large volumes of log events?
The abstract does not mention the scalability of this technology for handling large volumes of log events or if there are any limitations in processing massive amounts of data.
Original Abstract Submitted
a computer implemented method processes log events. a computer system determines dependence and independence of training log events. the computer system clusters the training log events with determined dependence and independence to form clustered training log events. the computer system generates an event association model using the clustered training log events. the computer system clusters the log events using the event association model.
