Spatial Two-Tuple Determination via Subspace Partitioning
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Solution Overview
Problem
In spatial data analysis, large global areas without prior filtering of invalid data lead to wastage of network bandwidth and computing resources, reducing overall performance.
Innovation Solution
A method is proposed to determine a spatial two-tuple by obtaining a given spatial distance and spatial objects, determining a global area using minimum bounding rectangles (MBR) and extended MBRs, subdividing the area into subspaces, and partitioning spatial objects based on their position relationships to identify matching spatial objects within each subspace.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a large global area is selected without filtering invalid data in advance, then more complete spatial data can be processed, but network bandwidth and computing storage resources are wasted
Solution Approach 1:
The global area is divided into multiple subspaces through subspace division, and spatial objects are partitioned into different subsets based on their position relationships with subspaces. This segmentation allows processing to be focused on relevant regions only, filtering out invalid data that would otherwise consume network bandwidth and computing resources.
Solution Approach 2:
The method performs preliminary filtering of spatial objects by determining their position relationships with subspaces before actual spatial distance connection processing. This preliminary action identifies and excludes invalid data in advance, preventing waste of network bandwidth and computing storage resources during subsequent processing stages.
2Reliability
If a large global area is processed without filtering, then all potential spatial relationships are considered, but overall processing performance is reduced
Solution Approach 1:
By dividing the global area into subspaces and partitioning spatial objects accordingly, the method processes only relevant spatial relationships within each subspace. This segmentation maintains completeness of spatial analysis while significantly improving processing efficiency by avoiding unnecessary computations on invalid data.
Solution Approach 2:
The method performs preliminary partitioning of spatial objects based on their position relationships with subspaces before conducting spatial distance connection processing. This preliminary action filters invalid data in advance, ensuring that subsequent processing focuses only on potentially valid spatial relationships, thus improving overall productivity.
3Reliability
If spatial objects are processed in the entire global area, then all spatial two-tuples are found, but computing storage resources are wasted on invalid data
Solution Approach 1:
The method segments spatial objects into different subsets based on their position relationships with subspaces. This segmentation ensures that only spatial objects within relevant subspaces are processed for spatial two-tuple detection, maintaining completeness while reducing computing storage resource consumption by excluding invalid data.
Solution Approach 2:
The method performs preliminary partitioning of spatial objects by determining their position relationships with subspaces before spatial two-tuple detection. This preliminary action filters invalid data in advance, preventing waste of computing storage resources on data that cannot contribute to the final result.
Data Source
AI summary
A method for determining a spatial two-tuple is performed by a computer device. The method comprises: obtaining a given spatial distance and multiple spatial objects, wherein each spatial object belongs to a first spatial object set or belongs to a second spatial object set (101); determining a global area according to a minimum bounding rectangle (MBR) and an extended minimum bounding rectangle (EMBR) corresponding to the first spatial object set and an MBR and an EMBR corresponding to the second spatial object set (102); performing subspace division on the global area to obtain multiple subspaces (103); partitioning the multiple spatial objects according to a positional relationship between each spatial object and each subspace to determine spatial objects comprised in each subspace (104); and according to a spatial distance between the spatial objects of each subspace, determining a spatial two-tuple comprised in each subspace that matches the given spatial distance (105).