Tabular Data Recognition System for Cross-Format Standardization
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Solution Overview
Problem
Tabular data from various sources in industries like financial services and medical diagnostics is often stored in different formats and standards, making it difficult and time-consuming to process effectively.
Innovation Solution
A system and method for recognizing tabular data that determines boundary sets for header and body row data, calculates header and initial data scores to match data with standard columns, and transforms data into a consistent format for processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If tabular data from multiple sources is processed without standardization, then data processing speed is maintained, but data quality and consistency deteriorate
Solution Approach 1:
The system performs preliminary actions by automatically detecting data formats, determining boundary sets, and calculating header scores before actual data processing begins. This preprocessing stage identifies and standardizes data structures in advance, eliminating the need for manual format conversion during processing and reducing overall processing time while maintaining data consistency.
Solution Approach 2:
The system changes parameters by transforming data from various formats into a standardized structure. It calculates header scores and data scores to determine the degree of resemblance to standard columns, then applies transformations to align data with expected formats. This parameter transformation approach maintains data integrity while achieving consistency across multiple sources.
2Manufacturing precision
If manual formatting of tabular data is performed, then data accuracy is improved, but processing efficiency deteriorates
Solution Approach 1:
The system implements self-service by automatically detecting data formats, calculating header scores, and transforming data without manual intervention. The algorithm independently determines boundary sets and matches data to standard columns based on calculated scores, eliminating the need for manual formatting while maintaining high data accuracy through systematic scoring and validation.
Solution Approach 2:
The system replaces manual mechanical formatting operations with an automated computational mechanism. Instead of human operators manually adjusting data formats, the system uses algorithms to calculate header scores, determine data resemblance to standard columns, and perform automatic transformations, thereby maintaining accuracy while dramatically improving processing efficiency.
3Adaptability or versatility
If data standardization is implemented, then data usability is improved, but system complexity increases
Solution Approach 1:
The system segments the data standardization process into distinct components: boundary set determination, header score calculation, data score calculation, and transformation application. Each component handles a specific aspect of standardization independently, making the overall complex process manageable and maintainable while achieving high data usability through systematic processing.
Solution Approach 2:
The system introduces intermediary elements (header scores and data scores) that mediate between raw data and standardized output. These score calculations serve as intermediate steps that evaluate data resemblance to standard columns, enabling automatic decision-making about how to transform data while maintaining versatility and usability without requiring complex manual intervention.
Data Source
AI summary
A computer implemented method is disclosed for providing a platform for recognizing tabular data created by a plurality of applications and stored in different formats and/or organized by different standards. The tabular data comprises data within one or more rows of one or more input columns. The method comprises executing on one or more processors the steps of (a) determining one or more boundary sets for data from a data source, each set defining boundaries for header row data and data within a body row of an input column, (b) selecting a boundary set of the one or more boundary sets, (c) calculating a header score for a header row data of the input column of a boundary set, the score representing the extent to which the header row data resembles a header of a standard column, and (d) calculating an initial data score for data of an input column that represents the extent to which the data within body rows of the input column resembles the data of a standard column.