Abstract: The present disclosure provides systems and methods for generation of parsing scripts or rules for unstructured or semi-structured system log messages, including systems and methods for identifying and clustering of same or substantially similar system log messages using machine learning. Patterns indicative of the same or substantially similar types system log messages can be generated based on the clustering of the system log messages and calculated similarities of attributes or distances between common features/fields of the system log messages, with the results of the clustering presented for analysis and development or adjustment of parsing scripts.

Abstract: Systems and methods for generating a parser from a log file including: receiving a log file, wherein the log file is a structured text file of a plurality of data elements; invoking a machine learning model to: process the log file to identify name-value-pairs from the data elements; classify the log file as being associated with a schema based in part on the name-value pairs; map a first name-value pair to a first input field of the schema based on characteristics of the first name-value pair; determine a confidence level associated with mapping the first name-value pair to the first input field; and when the confidence level for mapping the first name-value pair exceeds a threshold, provide the first name-value pair to the first input field; and generating a parser from the plurality of input fields of the schema.

Abstract: The present disclosure provides systems and methods for generation of parsing scripts or rules for unstructured or semi-structured system log messages, including systems and methods for identifying and clustering of same or substantially similar system log messages using machine learning. Patterns indicative of the same or substantially similar types system log messages can be generated based on the clustering of the system log messages and calculated similarities of attributes or distances between common features/fields of the system log messages, with the results of the clustering presented for analysis and development or adjustment of parsing scripts.

Abstract: The present disclosure provides systems and methods for generation of parsing scripts or rules for unstructured or semi-structured system log messages, including systems and methods for identifying and clustering of same or substantially similar system log messages using machine learning. Patterns indicative of the same or substantially similar types system log messages can be generated based on the clustering of the system log messages and calculated similarities of attributes or distances between common features/fields of the system log messages, with the results of the clustering presented for analysis and development or adjustment of parsing scripts.