Pattern Recognition Interface for Unstructured Data Parsing
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Solution Overview
Problem
Current tabular user interface programs, such as Microsoft Excel, are cumbersome in converting unstructured text into structured substrings and lack solutions for applying predefined pattern libraries to format matching and non-matching text, making it difficult to normalize data from diverse sources with different formats for import into databases.
Innovation Solution
A user interface utilizing pattern recognition with regular expressions to parse unstructured data, where matched data is displayed in a specific format and unmatched data in another, allowing for formatting parameters like color and font, and enabling data to be placed in separate columns, facilitating data normalization for database import.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional tabular user interface programs are used to convert unstructured text into structured substrings, then data can be organized into tables, but the process becomes cumbersome and requires wizard-based solutions that are difficult to operate
Solution Approach 1:
The system automatically parses unstructured text data using predefined patterns and regular expressions without requiring user intervention through wizards. The pattern matching engine autonomously identifies and extracts structured information from unstructured text, placing matched data into appropriate columns and formatting it according to predefined rules.
Solution Approach 2:
The manual wizard-based interaction process is replaced with an automated pattern recognition system that uses regular expressions and predefined patterns to perform data parsing. This substitution eliminates the need for step-by-step user guidance while maintaining data conversion capabilities.
2Adaptability or versatility
If legacy file formats with special delimiters or fixed width fields are used, then data can be read from structured sources, but the solution becomes specific to particular customers and cannot handle multiple different formats effectively
Solution Approach 1:
The system uses a universal pattern-based approach that can handle multiple data formats through a single interface. Predefined patterns and regular expressions serve as a universal language for interpreting various data formats, allowing the same system to process data from different sources without requiring custom format definitions for each customer or data type.
Solution Approach 2:
The system changes parameters such as pattern definitions, regular expressions, and formatting rules based on the input data characteristics. By dynamically adjusting these parameters, the system adapts to handle different data formats while maintaining a consistent processing framework, eliminating the need for fixed-width or delimiter-specific configurations.
3Productivity
If manual data normalization processes are used, then data accuracy can be maintained, but the time required to prepare data for database import increases significantly
Solution Approach 1:
The system performs preliminary data parsing and normalization automatically before database import using predefined patterns. By pre-configuring pattern libraries and regular expressions, the system prepares data in advance according to expected formats, eliminating the need for time-consuming manual normalization steps during the import process.
Solution Approach 2:
The data normalization process serves itself through automated pattern matching and formatting. The system independently identifies data types, applies appropriate formatting rules, and places data in correct columns without requiring manual intervention, thereby maintaining accuracy while dramatically reducing preparation time.
4Productivity
If pattern recognition with regular expressions is implemented, then data parsing efficiency improves and data can be rapidly formatted, but the system complexity increases due to pattern library management
Solution Approach 1:
Pattern libraries and regular expressions are pre-configured and stored before data processing begins. The system prepares a comprehensive library of patterns for common data formats and structures in advance, allowing rapid pattern matching during actual data parsing without requiring real-time pattern creation or management.
Solution Approach 2:
The system introduces an intermediary layer between raw data and the parsing logic through predefined patterns and regular expressions. This intermediary layer abstracts the complexity of pattern matching, providing a standardized interface that simplifies data processing while maintaining the power of regular expressions for complex pattern recognition.
Data Source
AI summary
A user interface for parsing unstructured data using pattern recognition. The patterns used in parsing data are formed from regular expressions. The parsed data may be displayed in a first format and unmatched strings in the unstructured text may be displayed in a second format. A format may comprise a desired color, font or any other user interface parameter. In addition, the data that parses according to a pattern may be placed in a column associated with the pattern in a tabular user interface, for example a spreadsheet like Excel™. Associating a pattern with a position to display successful matches in allows for breaking unstructured text into pieces associated with a particular field or column. Modification of the patterns allows for more and more of the unstructured text to match the patterns and when the data has been parsed to the desired level, the data may be imported into a database.