R-tree Spatial Indexing for Hierarchical WFS Feature Data
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Solution Overview
Problem
Conventional Web Feature Service (WFS) systems face difficulties in indexing and searching hierarchical spatial feature data, limiting the processing of WFS requests due to the lack of spatial indexing on feature data.
Innovation Solution
Implementing an R-tree based spatial index to facilitate the indexing and querying of hierarchical spatial feature data, allowing for the decomposition and storage of spatial feature data in a manner that enables efficient searching and manipulation at a sub-element level, while maintaining relationships between spatial collection content and feature data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional WFS systems store hierarchical spatial feature data without spatial indexing, then the system maintains simplicity in data storage structure, but the ability to search and process spatial feature data efficiently deteriorates
Solution Approach 1:
The patent segments hierarchical spatial feature data into individual spatial elements (points, lines, polygons) and stores them in separate spatial collection tables. Each spatial element is indexed independently using R-tree spatial indexes, allowing efficient spatial queries without requiring full hierarchical traversal. This segmentation enables the system to maintain structural simplicity while achieving fast spatial search performance.
Solution Approach 2:
The patent introduces a new dimensional organization by creating spatial collection tables that separate spatial elements from their hierarchical relationships. Instead of storing only nested hierarchical structures, the system creates an additional dimensional layer where spatial elements are stored with their geometric properties and spatial relationships, enabling efficient spatial indexing and querying operations.
2Productivity
If spatial feature data is decomposed and stored in spatial collection tables with R-tree indexing, then the efficiency of spatial querying and manipulation improves, but the complexity of data storage and management increases
Solution Approach 1:
The patent implements a nested table structure where spatial collection tables contain spatial elements that reference parent feature instances, which in turn reference their own parent features, creating a hierarchical nesting pattern. This nesting allows the system to maintain complex hierarchical relationships while using simplified indexed tables at each level, reducing the overall management complexity despite the detailed decomposition.
Solution Approach 2:
The patent introduces spatial collection tables as intermediary structures between the hierarchical feature data and the spatial indexing mechanism. These intermediary tables store spatial elements with their geometric properties and provide the interface for R-tree indexing, simplifying the management of complex spatial relationships while enabling efficient querying operations.
3Ease of operation
If hierarchical spatial feature data is stored without spatial indexing, then the system maintains ease of data manipulation, but the ability to perform spatial searches and intersections deteriorates
Solution Approach 1:
The patent performs preliminary action by pre-computing and storing spatial indexes (R-trees) on spatial elements during data insertion and updates. This preliminary indexing action enables fast spatial searches and intersection operations without requiring complex runtime computations, making spatial operations as easy as standard database queries while dramatically improving search capability.
Data Source
AI summary
Systems, methods, and other embodiments associated with registering a WFS feature type are described. One example method includes creating a feature instance table and a set of collection item tables to register the WFS feature type. Elements in the feature instance table and a collection item table may be related by a feature instance primary key value. The example method may also include producing an r-tree based spatial index for spatial content collection items stored in the set of collection item tables. The method may also include selectively processing a WFS request to manipulate (e.g., insert, delete, update) a WFS feature instance based, at least in part, on the feature instance table and the set of collection item tables. The method may also include selectively processing a WFS query associated with a WFS feature instance based, at least in part, on the r-tree based spatial index.