Hybrid Table Matching for Variable-Format Data Extraction
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Solution Overview
Problem
Existing data extraction from tabular data files with varying structures is time-consuming and requires manual data structuring due to inconsistent formatting across files, especially in environments like laboratories and businesses.
Innovation Solution
A data extraction system utilizing a hybrid matcher that combines schema-level and instance-level matching to identify and extract semantically matched tables based on user-defined templates, along with a validator to ensure consistent formatting, and optionally using machine learning for automated data analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual data structuring and clean-up is performed to handle inconsistent formatting, then data extraction reliability is improved, but processing time and labor requirements increase
Solution Approach 1:
The system performs preliminary actions by automatically detecting and establishing a unified data structure before extraction. The data extraction module creates a standardized schema that anticipates the needs of subsequent processing, eliminating the need for manual structuring during extraction and reducing overall processing time while maintaining reliability.
Solution Approach 2:
The system enables self-service by automatically adapting to different file formats and establishing its own data structure without human intervention. The data extraction module autonomously detects patterns, validates data, and creates unified structures, freeing users from manual data cleaning tasks while ensuring consistent extraction quality.
2Productivity
If a unified data structure is imposed on all files, then data extraction efficiency is improved, but adaptability to varying file formats decreases
Solution Approach 1:
The system applies dynamics by making the data structure adaptive rather than rigid. The data extraction module dynamically adjusts the unified structure based on detected patterns in different file formats, allowing the system to maintain efficiency while accommodating variations in data organization across different sources.
Solution Approach 2:
The system changes parameters by allowing the data structure to evolve based on input characteristics. The extraction module modifies structural parameters such as field names, data types, and relationships according to the detected patterns in each file format, enabling both efficient extraction and high adaptability simultaneously.
3Measurement precision
If schema-level matching is performed to identify candidate tables, then matching accuracy is improved, but processing complexity increases
Solution Approach 1:
The system segments the matching process into distinct schema-level and instance-level stages. By dividing complexity into manageable segments, the data extraction module can perform schema matching to identify candidate tables with high accuracy, then proceed to instance-level matching only for promising candidates, reducing overall processing complexity while maintaining precision.
Solution Approach 2:
The system applies partial action by performing schema-level matching comprehensively to establish candidate tables, then applying instance-level matching selectively only to the top candidates. This partial application of the more complex instance-level matching reduces overall processing complexity while maintaining high matching accuracy through the preliminary schema filtering.
Data Source
AI summary
Described herein is a data extraction system for extracting data from one or more tabular data files. The system includes a user interlace adapted for facilitating one or more users to submit one or more tabular data files, each tabular data file including at least one table.