Dynamic Pattern Matching Rule Reordering for Data Processing
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Solution Overview
Problem
Existing data processing systems face inefficiencies in processing large amounts of unstructured data due to the sequential application of numerous pattern matching rules, which increases processing time and resource consumption, potentially leading to delays in error detection and resolution.
Innovation Solution
Efficient Pattern Matching (EPM) optimizes the application of pattern matching rules by determining their order, organizing them into hierarchies, and creating optimized versions, allowing for parallel processing and dynamic reordering based on matches found in the data, thereby reducing processing time and resource demand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pattern matching rules are applied sequentially to unstructured data, then processing accuracy is maintained, but processing time increases significantly
Solution Approach 1:
The system performs preliminary analysis of the unstructured data to identify characteristics and patterns before applying pattern matching rules. This preliminary action enables the system to pre-determine an optimized sequence for applying rules, placing more likely matching rules earlier in the sequence, thereby reducing overall processing time while maintaining accuracy
Solution Approach 2:
The rule application sequence is made dynamic rather than static. The system adapts the sequence of pattern matching rules based on the specific characteristics of the input data, reordering rules dynamically to optimize processing efficiency for each particular data set while ensuring all rules are eventually applied for accurate matching
2Reliability
If a large number of pattern matching rules are applied to ensure comprehensive data extraction, then extraction completeness is improved, but resource consumption increases
Solution Approach 1:
The system performs preliminary analysis to identify data characteristics and pre-determines which pattern matching rules are most likely to match the given data. This allows the system to apply a optimized subset of rules in a specific sequence, reducing resource consumption while maintaining comprehensive extraction through strategic rule selection and ordering
3Ease of operation
If pattern matching rules are applied in a fixed sequence, then system simplicity is maintained, but processing efficiency decreases
Solution Approach 1:
The system performs preliminary analysis of the input data to determine optimal rule application sequences. By pre-calculating and storing optimized sequences based on data characteristics, the system maintains operational simplicity during actual processing while achieving significant efficiency improvements through data-driven rule ordering
Solution Approach 2:
The system implements dynamic rule sequencing that adapts to different data types and characteristics. While the underlying mechanism is complex, the system presents a simple interface that automatically selects and applies optimized rule sequences based on the input data, maintaining ease of operation while maximizing processing efficiency
Data Source
AI summary
Exemplary methods, apparatuses, and systems parse data with a plurality of pattern matching rules. Pattern matching rules are applied in an ordered sequence and a first rule is applied to the data before a second rule is applied to the data. In response to determining the second rule matches the data, the sequence is reordered to apply the second rule prior to the first rule.