Log Analysis Pattern Matching Framework
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional techniques for analyzing logs are inefficient and ill-suited for handling the vast amounts of data generated by modern computing devices, particularly in scenarios like search engines, where logs can exceed several terabytes and require flexible pattern matching capabilities that current methods do not support.
Innovation Solution
A pattern matching framework for log analysis is introduced, allowing users to specify two-dimensional patterns using a query language that includes filter and projection patterns, enabling efficient extraction and output of relevant data from logs through a graphical user interface and query engine.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional log analysis techniques are used, then implementation is simple, but analysis efficiency and scalability deteriorate when handling large-scale logs
Solution Approach 1:
The system segments log analysis into distinct components: filter patterns for matching, projection patterns for output formatting, and a query formation mechanism. This segmentation allows each component to be optimized independently, improving overall analysis efficiency without proportionally increasing system complexity.
Solution Approach 2:
The patent introduces a two-dimensional pattern framework where filter patterns operate in one dimension (matching criteria) and projection patterns operate in another dimension (output transformation). This dimensional separation enables complex log analysis tasks to be decomposed into manageable pattern operations, scaling efficiently to large log volumes.
2Adaptability or versatility
If traditional log analysis techniques are used, then resource consumption is low, but capability to handle vast log data deteriorates
Solution Approach 1:
The system performs preliminary actions by pre-compiling filter and projection patterns into optimized query structures before log analysis begins. This preparation work enables the query engine to process vast log data with reduced computational overhead during actual analysis, balancing capability enhancement with resource efficiency.
Solution Approach 2:
The patent transforms log analysis from traditional string-matching operations into parameterized pattern matching using filter and projection patterns. This parameterization allows the system to adapt to different log formats and analysis requirements without proportional increases in computational resource consumption.
3Measurement precision
If complex pattern matching is implemented, then analysis accuracy improves, but ease of use deteriorates
Solution Approach 1:
The patent introduces an intermediary query formation mechanism that translates user-friendly filter and projection pattern specifications into optimized analysis queries. This intermediary layer shields users from the complexity of direct query construction while maintaining high extraction accuracy through structured pattern matching.
Solution Approach 2:
The system uses template-based pattern copying where filter patterns and projection patterns serve as reusable templates. Users can define patterns once and reuse them across multiple analysis tasks, improving ease of operation while maintaining consistent high accuracy through the standardized pattern framework.
Data Source
AI summary
A pattern matching framework for log analysis is described. In one or more implementations, one or more inputs are received via a user interface of a computing device that describe a filter pattern that specifies data that is to be matched and extracted from a log and a projection pattern that specifies how at least a portion of the data extracted using the filter pattern is to be output. A query is formed from the filter pattern and the projection pattern by the computing device that is configured to analyze the log.