Log File Substring Boundary Identification for Table Construction
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Solution Overview
Problem
Existing methods for extracting data from log files and placing it in tabular form are cumbersome, requiring manual effort or programming expertise, and are not well-suited for programming by example (PBE) technologies due to the need for explicit identification of header, comment, and record boundaries.
Innovation Solution
The solution involves processing log files to automatically identify and filter header and comment lines, learning regular expressions to distinguish these from data lines, and inferring record boundaries without user input, allowing PBE technologies to synthesize programs that construct tables from extracted data, while providing suggestions for user-selected substrings to simplify the extraction process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual extraction of data from log files is performed, then data can be placed in tabular form, but the process is cumbersome and time-consuming
Solution Approach 1:
The system performs self-service by automatically identifying record boundaries and filtering header/comment lines without requiring manual user input. The machine learning model autonomously processes log files to extract data into tabular form, eliminating the need for manual intervention in the extraction process.
Solution Approach 2:
The patent replaces manual mechanical processes with automated computational methods. Instead of manual data extraction and tabulation, the system uses machine learning models and automated scripts to process log files, substituting human effort with computational automation.
2Extent of automation
If a programmer writes a script to extract data from log files, then data extraction can be automated, but programming expertise is required and each log file structure requires a custom script
Solution Approach 1:
The machine learning model provides a universal solution that can handle multiple log file structures without requiring separate custom scripts for each format. The model learns from training data and generalizes to different log file types, making the extraction process universally applicable across various logging systems.
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between the raw log files and the final tabular output. This intermediary layer learns the structure and patterns of log files automatically, mediating the extraction process without requiring users to write custom scripts for each log file format.
3Ease of operation
If conventional PBE technologies are used to synthesize programs for extracting data from log files, then programming can be simplified, but users must explicitly identify header lines, comment lines, and record boundaries
Solution Approach 1:
The system performs preliminary actions by pre-processing log files to identify and filter header lines and comment lines before the PBE synthesis process. Record boundaries are pre-identified using machine learning, so users don't need to manually specify these elements when creating extraction programs.
Solution Approach 2:
The system performs self-service by automatically identifying record boundaries and filtering non-data lines without requiring explicit user input. The machine learning model autonomously analyzes the log file structure and prepares the data for PBE synthesis, eliminating the burden of manual boundary identification from users.
Data Source
AI summary
Described herein are various technologies pertaining to identifying boundaries of a substring in a log file, wherein the substring is to be extracted from the log file and used to construct a table. An indication is received that a user has selected a beginning boundary of the substring, and the ending boundary of the substring is automatically identified.