K-NN Spatial Query Reuse for Database Efficiency
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Solution Overview
Problem
Current methods for performing K-nearest neighbor (K-NN) spatial queries in spatial databases are inefficient when dealing with a large number of queries, especially when the number of query points exceeds the number of data objects, and when query points are changing in real-time, as they require performing each query individually, leading to significant time consumption.
Innovation Solution
A computer-implemented method that pre-computes K-NN query results for a predefined location and re-uses them to perform K-NN queries on nearby query points, limiting the spatial search range by calculating a maximum distance and area of interest, allowing for parallel processing and increased efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If each K-NN query is performed individually using standard spatial database methods, then query accuracy is maintained, but the time consumption increases significantly when dealing with a large number of query points
Solution Approach 1:
The patent pre-computes K-NN results for a set of representative query points before the actual query execution. These pre-computed results are stored and reused during runtime to answer queries without performing full spatial searches, thereby significantly reducing query time while maintaining accuracy for queries within the pre-computed range
Solution Approach 2:
The patent divides the spatial database into multiple partitions or regions, and pre-computes K-NN results for each partition separately. This segmentation allows parallel processing of different regions and reduces the computational burden on any single query, enabling efficient handling of large numbers of query points
2Reliability
If the spatial search range is expanded to cover the entire database to ensure complete search coverage, then query completeness is improved, but the computational complexity and processing time increase
Solution Approach 1:
The patent applies different search strategies to different spatial regions. For query points within pre-computed ranges, it uses the stored results directly. For query points outside these ranges or in regions without pre-computed data, it performs full spatial searches. This local differentiation optimizes the balance between completeness and complexity
3Productivity
If pre-computed K-NN data is reused for multiple query points to improve processing speed, then query efficiency increases, but the applicability is limited to queries within a specific spatial range
Solution Approach 1:
The patent creates multiple sets of pre-computed K-NN results for different spatial partitions or regions of the database. Each pre-computed set can be applied to queries within its specific region, effectively extending the versatility of the pre-computation approach across the entire database while maintaining high efficiency for queries in any region
Data Source
AI summary
K-NN spatial queries may be performed, and the results of one K-NN query are re-used to perform a plurality of K-NN queries on a set of query points. More specifically, a K-NN query is performed on a pre-defined location, for example, an object point, to find the nearest K data objects for this pre-defined location, the results of this K-NN query being used to perform further K-NN queries on the query points near the pre-defined location. In doing so, the efficiency of large scale K-NN spatial queries is improved by limiting the spatial search range of the K-NN queries to be performed and re-using pre-computed K-NN data.