Extraction Rule Enhancement via User Feedback
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Solution Overview
Problem
Analyzing and searching massive quantities of machine-generated data from diverse sources is challenging due to the complexity and variability of data formats, requiring efficient extraction and validation of extraction rules to facilitate effective data retrieval and analysis.
Innovation Solution
A data processing environment that enhances and validates extraction rules using user feedback and monitoring, generating new rules as needed to handle varying data formats and improve data extraction efficiency, incorporating a field extraction system that manages and processes data from multiple sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If extraction rules are manually created and maintained for diverse data formats, then data extraction accuracy can be maintained, but the complexity and time required to manage extraction rules increases significantly
Solution Approach 1:
The system implements feedback loops where extraction results are monitored and used to automatically refine and update extraction rules. User feedback on extraction accuracy triggers rule enhancements, creating a continuous improvement cycle that maintains accuracy while reducing manual management overhead.
Solution Approach 2:
The extraction rule system performs self-enhancement by automatically learning from processed data and user interactions. The system autonomously generates new extraction rules and modifies existing ones based on observed patterns, reducing the need for manual rule creation and maintenance while preserving extraction accuracy.
2Productivity
If traditional extraction methods are used for massive quantities of machine-generated data, then existing extraction rules can be applied, but the efficiency and speed of data retrieval decreases
Solution Approach 1:
The system performs preliminary enhancement of extraction rules by pre-processing and pre-compiling optimized extraction patterns before actual data retrieval operations. This advance preparation of extraction rules enables faster processing of massive data volumes without sacrificing accuracy during the actual retrieval phase.
Solution Approach 2:
The extraction rule system dynamically adapts to varying data formats and volumes by continuously learning from processed data. The rules evolve and optimize themselves in real-time based on the characteristics of the data being processed, enabling efficient handling of massive quantities of machine-generated data across diverse formats.
3Adaptability or versatility
If extraction rules are frequently updated to handle varying data formats, then adaptability to new data sources improves, but system stability and reliability decreases
Solution Approach 1:
The system uses feedback mechanisms to monitor the impact of rule updates on extraction stability. By tracking extraction success rates and error patterns, the system can identify when updates are necessary and when stability should be prioritized, enabling informed decisions that balance adaptability with reliability.
Solution Approach 2:
The system implements cushioning measures by maintaining backup extraction rules and using validation mechanisms before deploying updates. This protective approach ensures that even when rules are updated to handle new data formats, the system maintains stability through fallback options and thorough testing protocols.
Data Source
AI summary
Embodiments of the present invention are directed to enhancing extraction rules utilizing user feedback. In embodiments, a set of extraction rules relevant to an event set are provided for display. Thereafter, a selection of an extraction rule is received and, in response, a set of events matching the selected extraction rule is provided for display. A modification, for example provided by a user, in association with the extraction rule or the set of events is received. Such a modification is then used (e.g., via machine learning) to enhance extraction rules available for performing subsequent data extraction.