Regular Expression Generation Using Longest Common Subsequence Analysis
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Solution Overview
Problem
Current data formatting and preprocessing methods for big data analytics are inefficient, especially for large-scale datasets, as they often rely on manual and ad hoc processes, leading to inaccurate results due to poor signal-to-noise ratios and the inclusion of duplicate or erroneous data.
Innovation Solution
A system for generating regular expressions using a data processing system that identifies a longest common subsequence (LCS) among input character sequences, allowing for the automatic creation of patterns to match and extract data, with features like user interfaces for selecting character subsets and handling both positive and negative examples to minimize false positives and negatives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual and ad hoc processes are used for data formatting and preprocessing, then data can be cleaned and curated, but the process becomes inefficient and impractical for large-scale datasets
Solution Approach 1:
The system automatically generates regular expressions by analyzing input data patterns without requiring manual intervention. The regex generator autonomously identifies data formats, creates appropriate regex patterns, and applies them for data extraction and validation, enabling the system to service itself rather than relying on manual preprocessing operations
Solution Approach 2:
The system transforms raw data into structured formats by applying dynamically generated regular expressions. The regex patterns are generated based on analyzed data characteristics, and the system adjusts matching parameters to efficiently process large-scale datasets, converting unstructured or semi-structured data into clean, curated formats suitable for analytics
2Reliability
If manual processes are used for data cleaning, then data quality can be improved, but the signal-to-noise ratio remains poor due to duplicate and erroneous data
Solution Approach 1:
The system employs a feedback mechanism where the regex generator continuously analyzes matched data, evaluates pattern effectiveness, and refines regular expressions based on results. This iterative process improves data cleaning accuracy over time, identifying and removing duplicates and errors while preserving valid data signals through successive refinement cycles
Solution Approach 2:
The system replaces manual mechanical data cleaning processes with automated computational methods. Regular expressions provide a systematic, rule-based approach to identifying and removing duplicate and erroneous data, substituting human-operated mechanical processes with algorithmic automation that consistently applies cleaning rules across large datasets
3Productivity
If automated regular expression generation is implemented, then data preprocessing efficiency improves, but the system complexity increases
Solution Approach 1:
The system segments the data preprocessing task into distinct modular components: data analysis module, regex generation module, pattern matching module, and data extraction module. Each component performs a specific function independently, allowing the complex automated process to be broken down into manageable segments that can be developed, maintained, and executed efficiently
Data Source
AI summary
Techniques for generated regular expressions are disclosed. In some embodiments, a regular expression generator may receive input data comprising one or more character sequences. The regular expression generator may convert character sequences into a sets of regular expression codes and/or span data structures. The regular expression generator may identify a longest common subsequence shared by the sets of regular expression codes and/or spans, and may generate a regular expression based upon the longest common subsequence. A negative example may be used to generate the regular expression. Context from the negative example may be determined in order to generate the regular expression.