Regional Spatial Index Tree for Semantic Information Ascertainment
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Solution Overview
Problem
Existing methods for coordinate semanticization in spatiotemporal big data scenarios require manual ascertainment of semantic information, which is inefficient and lacks computational efficiency, especially when dealing with high-dimensional space searches.
Innovation Solution
The method employs a pre-built regional spatial index tree, such as an R-tree, to ascertain the target region to which a coordinate belongs, and uses the semantic information of that region as the semantic information for the coordinate, thereby automating the ascertainment process and improving computational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual ascertainment of semantic information is used, then accuracy can be maintained, but computational efficiency deteriorates significantly
Solution Approach 1:
The patent pre-divides the geographic space into multiple regions and pre-assigns semantic information to each region before actual queries occur. This preliminary organization of data allows for rapid retrieval during runtime without manual ascertainment, resolving the contradiction between accuracy and computational efficiency.
Solution Approach 2:
The patent segments the geographic space into multiple discrete regions, each associated with specific semantic information. This segmentation enables efficient querying by narrowing down the search scope to relevant regions only, maintaining accuracy while significantly improving computational efficiency compared to manual analysis of the entire space.
2Adaptability or versatility
If high-dimensional space search is performed manually, then comprehensive coverage is achieved, but time complexity increases
Solution Approach 1:
The patent pre-organizes high-dimensional spatial data into a hierarchical region structure with pre-assigned semantic information. This preliminary organization transforms complex high-dimensional search problems into simpler region-matching problems, achieving comprehensive coverage while reducing time complexity through efficient indexing.
Solution Approach 2:
The patent transforms the high-dimensional space search problem into a multi-level regional hierarchy problem. By organizing space into hierarchical regions (from coarse to fine granularity), the patent enables efficient search through dimensional decomposition, reducing time complexity while maintaining comprehensive coverage.
3Productivity
If automated region-based semanticization is implemented, then computational efficiency improves, but device complexity increases
Solution Approach 1:
The patent segments the complex automated semanticization system into modular components: spatial indexing module, region matching module, and semantic information assignment module. This segmentation manages device complexity by breaking down the automated system into manageable, independent functional units while maintaining high computational efficiency.
Data Source
AI summary
A method and apparatus for semanticization is provided. The method includes: ascertaining a target coordinate of a to-be-semanticized location; ascertaining, through a pre-built regional spatial index tree, a target region to which the target coordinate of the to-be-semanticized location belongs; and ascertaining semantic information of the to-be-semanticized location based on semantic information of the target region.