Automated Data Characterization System with User-Adjustable Abstraction
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Solution Overview
Problem
Current data characterization systems require significant manual setup, are limited in their ability to adapt to changes in data format, and often miss or misinterpret relevant data, making them time-consuming, costly, and error-prone, especially when dealing with large and varied datasets.
Innovation Solution
A computing device generates a data abstraction from textual data, allowing users to make adjustments, and then extracts characterized data using a regular expression that can adapt to changes in data format, combining automated processing with human input for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If current software systems are used to assist with parsing and extracting data, then some data processing can be automated, but they require large amounts of manual setup and provide limited characterization
Solution Approach 1:
The system performs self-characterization by automatically analyzing data records and generating characterizations without requiring manual configuration. The computing device processes data records, identifies patterns, and creates characterizations autonomously, eliminating the need for extensive manual setup while maintaining high automation levels.
Solution Approach 2:
The system performs preliminary characterization on a sample of data records before full processing. By characterizing a subset of records first and using those characterizations to guide subsequent processing, the system reduces the complexity of manual setup while maintaining comprehensive automation throughout the data extraction process.
2Productivity
If current software systems are used for data characterization, then some processing can be performed, but they do not adjust well to changes in data format
Solution Approach 1:
The system dynamically adapts to data format changes by continuously analyzing incoming data records and updating characterizations in real-time. When format changes are detected, the system automatically adjusts its characterization rules and patterns, maintaining high productivity across varying data formats without requiring manual reconfiguration.
Solution Approach 2:
The system uses feedback from processed data records to continuously improve and update characterizations. By monitoring the success of data extraction and characterization, the system automatically adjusts its approach when format changes are detected, ensuring both high productivity and adaptability to new data formats.
3Productivity
If current software systems are used for data extraction, then some data can be processed, but they may still miss or misinterpret relevant data
Solution Approach 1:
The system performs comprehensive characterization analysis on data records, examining multiple aspects and details of each record. By applying thorough and sometimes redundant analysis methods, the system ensures high accuracy in data characterization while maintaining the ability to process large volumes of data efficiently through automated pattern recognition.
Solution Approach 2:
The system replaces manual data review and interpretation with automated computational analysis. By using algorithmic approaches and pattern recognition instead of human analysts, the system achieves both high productivity in processing large data volumes and high precision in characterizing data, eliminating the trade-off between speed and accuracy.
Data Source
AI summary
A system and method for characterizing textual data by generating a first data abstraction based on a set of textual data. The first data abstraction can be presented to a user, and the user can provide instructions to make changes to the first data abstraction to generate a second data abstraction. The textual data can be extracted and characterized from the set of textual data using the second data abstraction.