Spatial Query Processing via Query Window Index
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Solution Overview
Problem
Spatial queries in geographic databases often require extensive memory and computing resources due to the need for point-by-point comparisons of geometric data, leading to inefficient processing times, especially when determining overlapping locations or spatial relationships.
Innovation Solution
The construction of a query window index structure that represents the query window geometry, allowing for direct comparison with spatial data indexes to determine topological relationships without approximations, thereby reducing the need for secondary searches and optimizing processing speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If point-by-point comparison of geometries is performed to determine overlapping locations, then measurement precision is improved, but processing time increases significantly
Solution Approach 1:
The patent segments the spatial comparison process into two stages: first using MBR intersection testing to quickly identify potential overlaps, then performing detailed geometry comparison only on segments that pass the initial filter. This segmentation eliminates unnecessary point-by-point comparisons while maintaining measurement precision for actual overlapping regions.
Solution Approach 2:
The patent performs preliminary filtering using minimum bounding rectangle (MBR) intersection tests before conducting detailed geometry comparisons. By pre-identifying candidate regions that may contain overlaps, the system avoids time-consuming point-by-point comparisons in non-overlapping areas, significantly reducing overall processing time while preserving accuracy for actual overlaps.
2Measurement precision
If detailed geometry comparison is performed to determine overlapping locations, then measurement precision is improved, but computing power requirements increase
Solution Approach 1:
The patent divides the computing workload into two phases: a low-power MBR intersection test phase that filters out non-overlapping regions, and a high-power detailed comparison phase that processes only the filtered segments. This segmentation dramatically reduces total computing power requirements by eliminating comparisons in regions that definitely do not overlap.
Solution Approach 2:
The patent performs only the necessary level of comparison for each region: MBR-level filtering for most areas and detailed geometry comparison only where needed. This partial action approach avoids excessive computing power consumption by limiting detailed analysis to segments with potential overlaps.
3Speed
If MBR hierarchy is used for spatial indexing, then query processing speed is improved, but device complexity increases
Solution Approach 1:
The patent implements an R-tree structure where MBRs are nested in a hierarchical fashion, with child MBRs contained within parent MBRs. This nesting organization enables efficient pruning of search spaces during queries, improving processing speed while maintaining a structured, manageable index format.
Solution Approach 2:
The patent transforms the flat spatial data into a hierarchical multi-dimensional index structure. By organizing MBRs across multiple levels with spatial relationships, the system adds dimensional organization that enables faster query processing through hierarchical traversal and pruning, balancing the increased structural complexity with significant performance gains.
Data Source
AI summary
One example method of spatial query processing using query window indexes includes receiving a spatial query on spatial data that has an associated spatial data index. The spatial query specifies a query window. A query window index structure is constructed that describes a geometry of the query window. The query window index structure is compared to the spatial data index and, when necessary, to the spatial data to determine a spatial relationship between the query window and the spatial data.