User Agent String Parser with Dynamic Pattern Learning
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Solution Overview
Problem
Existing methods for parsing user agent strings are inefficient in extracting and analyzing user agent information, as they struggle to recognize and handle non-standard formats, leading to high rates of unrecognized and incorrectly recognized strings.
Innovation Solution
A system that utilizes standard and customized extractors to process user agent strings, employing regular expressions to match patterns, and a mapper to supplement extracted information with predefined data, with an alerting mechanism to update patterns based on unrecognized strings, thereby improving recognition accuracy and coverage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing parsing methods are used, then the system can process user agent strings, but the recognition accuracy is low and many strings remain unrecognized
Solution Approach 1:
The parsing system dynamically adapts to new user agent string formats by continuously learning from unrecognized strings. The system updates its patterns and extractors based on encountered variations, transforming from a static recognition tool into a dynamic system that evolves with incoming data, thereby improving recognition accuracy while maintaining low unrecognized rates
Solution Approach 2:
The system implements a feedback mechanism where unrecognized user agent strings are fed back into the learning process. This feedback loop allows the system to identify patterns in previously unrecognized formats, update its extraction rules, and improve future recognition, directly addressing the low accuracy and high unrecognized rate problems
2Adaptability or versatility
If standard extractors alone are used, then the system maintains simplicity, but it cannot handle non-standard user agent formats
Solution Approach 1:
The extraction system is segmented into multiple specialized extractors (standard extractor, customized extractor, regex-based extractor) that handle different types of user agent formats. Each extractor focuses on specific patterns, allowing the system to maintain simplicity within each component while achieving high versatility through their coordinated operation
Solution Approach 2:
The system creates universal extractors that can handle multiple formats through configurable patterns and mappings. The customized extractor, in particular, is designed to be multi-functional, capable of adapting to various non-standard formats through learning, thus providing both versatility and controlled complexity
3Productivity
If the system processes all user agent strings in real-time, then it provides immediate analysis, but it loses efficiency when dealing with diverse formats
Solution Approach 1:
The system performs preliminary actions by pre-compiling patterns, mappings, and extraction rules before processing user agent strings. Common formats are pre-configured with optimized extraction paths, allowing the system to quickly process standard strings while reserving more resources for complex, non-standard formats, thereby maintaining high overall efficiency
Solution Approach 2:
The system applies different processing qualities to different user agent strings based on their format. Standard formats receive fast, simplified processing paths, while non-standard or complex formats receive more intensive, adaptive processing. This local differentiation of processing quality maintains high productivity for the majority of cases while ensuring accurate handling of diverse formats
Data Source
AI summary
The present disclosure provides a method for parsing a user agent string. Information is extracted from a user agent string based on user agent string patterns. The extracted information is mapped to predefined information based on patterns of the extracted information.