Data Complementing System Using Cell-Region Mesh Models
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Solution Overview
Problem
Existing data complementing systems face challenges in efficiently completing missing data in region-specific datasets, especially when there are no similar regions available for comparison.
Innovation Solution
A data complementing system that generates a complement model using external region and cell-region characteristic data, allowing for accurate prediction and completion of missing data even in the absence of similar regions, by employing multiple regression analysis and significance testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If map data processing apparatus searches for similar regions to complement missing data, then data completeness is improved, but the method fails when no similar regions exist
Solution Approach 1:
The patent segments the region into multiple cell regions (meshes) and creates complement models for each cell region independently. This allows the system to handle data complementation at a granular level, enabling it to work even when no similar entire regions exist, by finding similarities at the cell region level and combining results.
Solution Approach 2:
The patent transitions from region-level data complementation to cell region-level complementation, adding a spatial dimension of granularity. By dividing the region into meshes and creating individual complement models for each cell, the system can leverage local similarities even when global similarities are absent.
2Measurement precision
If complement model is generated using external region data, then data accuracy is improved, but system complexity increases
Solution Approach 1:
The system divides the complex task of region-level data complementation into multiple simpler cell region-level tasks. By generating complement models for individual cell regions and then aggregating results, the system manages complexity through modular processing while maintaining accuracy through localized analysis.
Solution Approach 2:
Each cell region generates its own complement model using external region data specific to that cell's characteristics. This self-service approach allows each cell to be complemented independently based on its local context, improving accuracy while the modular nature keeps system complexity manageable.
Data Source
AI summary
A data complementing system stores cell-region characteristic data that includes values of a plurality of data items regarding a cell region that is a region obtained by dividing the region into a mesh, information indicating a missing data item that is the data item of missing data being data missed in the cell-region characteristic data, external region characteristic data that includes values of a plurality of data items regarding an external region that is different from the region, and an external cell-region characteristic data that includes values of a plurality of data items regarding an external cell region obtained by dividing the external region into a mesh, generates a complement model for generating complement data indicating a value of the missing data item based on the external region characteristic data and the external cell-region characteristic data, and generates the complement data based on the complement model.