Real-Time Extraction Rule Generation for Unstructured Machine Data
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Solution Overview
Problem
The rapid increase in machine-generated data creates large, unstructured datasets that are difficult to analyze efficiently, as existing systems struggle to extract relevant field values from these datasets, leading to improper or ineffective extraction rules that result in incorrect or omitted values.
Innovation Solution
A system and method for real-time display of event records and extracted values using user interfaces that enable automatic or manual generation of extraction rules, including regular expressions, allowing users to edit rules and view updated results dynamically, with statistics on unique extracted values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data analysis methods are used on large unstructured datasets, then data processing can be performed, but the analysis efficiency is low and extraction rules are difficult to determine
Solution Approach 1:
The system performs preliminary actions by automatically generating extraction rules from sample data before full-scale analysis. The rule generation engine creates initial extraction rules by analyzing patterns in sample records, which then serve as the basis for processing the entire large dataset, significantly reducing the time required to determine extraction rules.
Solution Approach 2:
The system enables self-service by allowing extraction rules to be automatically generated and refined without extensive manual intervention. The rule generation engine autonomously analyzes data patterns and creates extraction rules, while the feedback mechanism allows the system to self-improve by learning from incorrect extractions and adjusting rules accordingly.
2Measurement precision
If manual extraction rule creation is used, then extraction accuracy can be improved, but the time and effort required increases significantly
Solution Approach 1:
The rule generation engine performs self-service by automatically analyzing data patterns and generating extraction rules without manual intervention. This maintains high extraction accuracy through intelligent pattern recognition while eliminating the significant time investment required for manual rule creation by human analysts.
Solution Approach 2:
The system implements feedback mechanisms where extraction results are evaluated and used to refine extraction rules. When incorrect extractions are detected, the system learns from these errors and adjusts the rules accordingly, maintaining high accuracy while reducing the time needed for manual rule refinement.
3Productivity
If extraction rules are applied to large datasets, then field values can be extracted, but incorrect or omitted values may result from improper rules
Solution Approach 1:
The system employs feedback loops where extraction results are continuously evaluated for correctness. When incorrect or omitted values are detected, the feedback mechanism triggers rule refinement and re-extraction, ensuring high reliability while maintaining efficient processing speeds through automated correction rather than manual review of entire datasets.
Solution Approach 2:
The system applies partial action by focusing extraction and validation efforts on critical fields and high-risk extractions rather than uniformly processing all data. This allows the system to maintain high extraction accuracy for important fields while preserving overall data extraction speed through selective intensive processing.
Data Source
AI summary
Embodiments are directed towards real time display of event records and extracted values based on at least one extraction rule, such as a regular expression. A user interface may be employed to enable a user to have an extraction rule automatically generate and/or to manually enter an extraction rule. The user may be enabled to manually edit a previously provided extraction rule, which may result in real time display of updated extracted values. The extraction rule may be utilized to extract values from each of a plurality of records, including event records of unstructured machine data. Statistics may be determined for each unique extracted value, and may be displayed to the user in real time. The user interface may also enable the user to select at least one unique extracted value to display those event records that include an extracted value that matches the selected value.