Hierarchical Grid Spatial Query Indexing
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Solution Overview
Problem
Existing spatial query algorithms face inefficiencies when dealing with large datasets, particularly in scenarios where data is not randomly distributed, leading to high memory overhead and slow query times due to the need for extra pointers and suboptimal data organization.
Innovation Solution
The approach involves dividing the domain space into grids with uneven cell sizes, using a cell-to-item mapping to quickly identify items within a query window by categorizing cells as fully or partially covered, and extending query windows to account for items with size, thereby reducing the number of comparisons needed and minimizing memory usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional spatial query algorithms are used to handle large datasets, then complete spatial coverage is achieved, but memory overhead increases due to extra pointers and query time increases due to comparing all items
Solution Approach 1:
The patent divides the domain space into a hierarchical grid structure with multiple levels, where each level partitions space into cells. This segmentation allows the system to quickly eliminate entire regions that cannot contain query results, avoiding the need to compare every item in the dataset. The hierarchical structure enables progressive refinement from coarse to fine granularity, significantly reducing the number of items that require detailed comparison.
Solution Approach 2:
The patent introduces a spatial dimension by organizing data in a grid-based hierarchical structure rather than flat storage. This adds spatial locality information to the data organization, allowing queries to leverage spatial relationships to prune search spaces. The grid hierarchy transforms the problem from comparing all items to navigating a spatial structure, reducing time complexity from O(n) to O(log n) for index traversal.
2Productivity
If hierarchical grid structures like KD-Tree or Quad-Tree are used, then spatial query efficiency is improved, but device complexity increases due to multiple pointers and complex data structures
Solution Approach 1:
The patent applies local quality by using uneven cell sizes in the grid hierarchy, where different regions of the domain space can have different grid resolutions. This allows densely populated regions to use finer grids for better query accuracy while sparsely populated regions use coarser grids to reduce memory overhead. Each cell stores only the items within its boundaries, eliminating the need for complex pointer structures required by traditional hierarchical trees.
Solution Approach 2:
The patent uses a simplified grid-based copying approach where items are stored in grid cells based on their spatial coordinates. Instead of using complex tree structures with multiple pointers per node, the system copies items into appropriate grid cells at different hierarchical levels, creating a straightforward array-based representation that is easier to implement and maintain while achieving similar spatial partitioning effects.
3Productivity
If data is organized to minimize query time, then faster spatial queries are achieved, but memory overhead increases due to extra pointers for each data item
Solution Approach 1:
The patent implements dynamic grid resolution where the grid cell size varies at different hierarchical levels and different spatial regions. This dynamic structure allows the system to adapt the level of detail to the local data density and query requirements. Coarse grids use larger cells with fewer items, reducing memory overhead, while fine grids use smaller cells for regions requiring higher query precision, optimizing the balance between memory usage and query performance.
Solution Approach 2:
The patent changes the parameter of grid cell size dynamically across the hierarchical structure. By varying the cell size parameter at different levels and regions, the system optimizes memory usage while maintaining query efficiency. Larger cells in upper hierarchical levels reduce the number of cells to manage, while smaller cells in lower levels provide detailed spatial information where needed, eliminating the need for uniform fine-grained storage throughout the entire domain.
Data Source
AI summary
Techniques are provided for improving performance of spatial queries by defining a grid that divides the domain space into cells, and then using a cell-to-item mapping to determine which items do not have to be individually evaluated against the location criteria of the spatial queries. Based on the cell to which an item belongs, the item may automatically qualify as a match, be automatically disqualified, or require item-specific evaluation. To account for items with size, the query window of a spatial query may be expanded. To limit the degree to which the query window is expanded, a plurality of grids may be established for the domain space, where each grid has differently sized cells, and items are assigned to grids based on the size of the items.