Adaptive Event Parser Reorders Expressions by Match Frequency
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Businesses face inefficiencies in data parsing due to cumbersome event parsers that require customization and are slow when dealing with large volumes of irrelevant data, often necessitating costly and time-consuming reconfiguration, and existing systems waste time comparing data to less relevant grammars.
Innovation Solution
An adaptive parser that automatically reorders known expressions based on match frequency, allowing more commonly matched expressions to be tested first, reducing unnecessary comparisons and improving parsing efficiency by placing the parser in a learning mode to adjust the order of expressions according to data flow patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional event parsers are used to process large volumes of data, then data processing capability is provided, but parsing speed decreases due to comparing data to numerous expressions including less relevant ones
Solution Approach 1:
The system performs preliminary actions by learning and storing the frequency order of expression matches from historical data before actual parsing operations. This pre-established frequency ordering allows the parser to efficiently prioritize which expressions to test first, reducing unnecessary comparisons and improving parsing speed when processing large volumes of data.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring match frequencies and automatically reordering expressions based on learned patterns. The parser uses feedback from actual data processing to dynamically adjust its expression testing sequence, ensuring that commonly matched expressions are tested first, thereby optimizing parsing speed for high-volume data processing.
2Productivity
If parsers are customized for specific applications to improve efficiency, then parsing efficiency increases, but device complexity and reconfiguration time increase
Solution Approach 1:
The parser system performs self-service by automatically learning and reordering expressions based on match frequencies without requiring manual reconfiguration. The system adapts to specific application requirements autonomously by analyzing historical data and adjusting its own expression ordering, thereby maintaining high parsing efficiency while reducing configuration complexity and reconfiguration time.
Solution Approach 2:
The system transitions from static, manually-configured parsers to dynamic, self-adapting parsers that automatically adjust their expression ordering based on learned patterns. This dynamic capability allows the parser to optimize for specific applications on-the-fly, improving parsing efficiency while eliminating the need for complex manual reconfiguration when data patterns change.
3Measurement precision
If specific grammars are placed at the top of the hierarchy to improve matching accuracy, then match precision increases, but time is wasted comparing data to less relevant grammars lower in the hierarchy
Solution Approach 1:
The system changes the ordering parameter of expressions from a static hierarchical structure to a dynamic frequency-based sequence. By reordering expressions according to their match frequencies learned from historical data, the system ensures that the most relevant expressions are tested first, thereby maintaining high matching precision while minimizing the time spent comparing data to less relevant expressions.
4Ease of operation
If generic grammars are used to simplify parser configuration, then ease of operation increases, but the derived metadata becomes scanty and of limited value
Solution Approach 1:
The parser system performs self-service by automatically learning specific expression patterns from historical data, thereby enriching the metadata quality without requiring manual configuration. The system maintains ease of operation by requiring minimal initial setup while autonomously developing specific grammar knowledge through continuous learning, thus preserving both operational simplicity and metadata richness.
Data Source
AI summary
An adaptive parser capable of automatically reordering expressions such that more commonly matched expressions are tested earlier than less commonly matched expressions; by reordering the expressions in this manner, less frequently matched expressions are less frequently tested, or may not be tested at all, thereby lowering the overall number of tests and increasing the amount of data that can be parsed in a given time period.