Spreadsheet to Data Model Conversion via Multi-Dimensional Mapping
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Solution Overview
Problem
Spreadsheets often contain semi-structured data with implicit and inconsistent relations, making it difficult for users to effectively present and organize the data.
Innovation Solution
A method to convert a spreadsheet into a data model by identifying local structures, combining them into a generic multi-dimensional model comprising dimensions and data cubes, and mapping these elements to generate a data model for display.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If spreadsheet data is used in its original semi-structured format, then data storage flexibility is maintained, but data organization and presentation effectiveness deteriorate
Solution Approach 1:
The patent segments the semi-structured spreadsheet data into distinct structured components including fact tables, dimension tables, and hierarchy tables. This segmentation transforms the monolithic semi-structured format into modular structured units, improving data presentation effectiveness while managing complexity through organized separation of concerns.
Solution Approach 2:
The patent introduces an intermediary data model layer that sits between the original spreadsheet and the final presentation. This intermediary model automatically identifies and structures relationships, acts as a mediator that transforms semi-structured data into structured formats without requiring direct manual intervention, thereby improving presentation effectiveness while hiding structural complexity.
2Productivity
If manual data organization methods are used, then data structure control is maintained, but time consumption and processing efficiency deteriorate
Solution Approach 1:
The patent implements self-service functionality where the system automatically analyzes spreadsheet data, identifies local structures, detects relationships between data elements, and generates the structured data model without user intervention. This automation eliminates manual data organization time while maintaining structured control, directly improving productivity.
Solution Approach 2:
The patent performs preliminary automatic analysis and structuring of spreadsheet data before the user needs to present or analyze it. By pre-identifying relationships, pre-structuring data into fact and dimension tables, and pre-validating consistency, the system eliminates subsequent manual organization time and accelerates the overall data processing workflow.
3Loss of information
If implicit relationships in spreadsheet data are maintained, then data fidelity to original format is preserved, but data relationship clarity and understanding deteriorate
Solution Approach 1:
The patent extracts implicit relationships from the semi-structured spreadsheet data and makes them explicit in the structured model. By taking out hidden relationships and representing them as defined connections between fact tables, dimension tables, and hierarchy tables, the system improves data relationship clarity while managing transformation complexity through systematic extraction methods.
4Reliability
If spreadsheet data inconsistencies are maintained, then original data integrity is preserved, but data quality and reliability for analysis deteriorate
Solution Approach 1:
The patent implements feedback mechanisms where the system validates data during the transformation process, identifies inconsistencies in the spreadsheet data, and provides feedback for correction or resolution. This feedback loop ensures data quality improvements while managing validation complexity through systematic checking and user-guided resolution of identified issues.
Data Source
AI summary
A method for converting a spreadsheet into a data model includes: obtaining the spreadsheet including a plurality of cells containing data; identifying local structures within the spreadsheet using attributes of the data in the spreadsheet; combining the local structures of the spreadsheet into a generic multi-dimensional model comprising dimensions and data cubes; generating the data model using the generic multi-dimensional model by mapping elements of the generic multi-dimensional model to elements of the data model; and displaying, on a display and to a user, the data model. The dimensions and the data cubes are the elements of the generic multi-dimensional model.