This is a computer system designed to figure out why a problem (an incident) occurred. It works by looking at records of changes made to a system, using text analysis with specific dictionaries and rules to understand what each change was. It then creates a searchable summary of these changes. When an incident happens, the system compares information about the incident and its timing to the summarized changes to identify which change likely caused the problem.
Why it matters: Filed before widespread adoption of advanced AI for text understanding. Modern large language models can now perform the text analytics and rule inference more dynamically and effectively than the dictionary and rule-based systems common in 2013, simplifying implementation and improving accuracy.
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