Geohash Depth Level Selection for Spatial Join Filtering
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Solution Overview
Problem
Current database technologies face inefficiencies in filtering spatial data due to the high computational cost of determining spatial relationships, especially when dealing with large datasets, as traditional indexing methods are inadequate for multi-dimensional geometries and often result in excessive cell entries for varying geometry sizes.
Innovation Solution
The method involves selecting common geohash depth levels, generating a filter table, and creating filter entries to efficiently execute database queries by identifying the smallest common geohash depth level for geometries, reducing unnecessary calculations and improving query performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traditional indexing methods are used for spatial data, then data organization is simplified, but computational cost increases and query performance deteriorates
Solution Approach 1:
The patent segments spatial data into hierarchical geohash levels (e.g., level 0 to level 8), where each level represents a different granularity of spatial resolution. This segmentation allows the system to choose appropriate depth levels for filtering based on query requirements, avoiding the need to process all spatial data at maximum granularity and thus reducing computational cost while maintaining query accuracy.
Solution Approach 2:
The patent introduces a new dimensional approach by creating filter tables that store pre-computed geohash values at multiple depth levels. This adds a temporal/dimensional dimension to spatial indexing, allowing queries to leverage pre-processed spatial relationships rather than computing them in real-time, thereby improving query execution speed without significantly increasing structural complexity.
2Productivity
If geohash structures are used to represent geographic locations, then spatial filtering efficiency improves, but system complexity and storage requirements increase
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing geohash values at multiple depth levels in filter tables before queries are executed. This pre-processing step creates ready-to-use spatial filters that can be quickly applied during query execution, improving spatial filtering efficiency while managing system complexity through structured pre-computation rather than runtime calculation.
Solution Approach 2:
The patent changes parameters by allowing dynamic selection of geohash depth levels (from level 0 to level 8 or higher) based on query specificities. This parameter flexibility enables the system to balance between filtering accuracy and computational efficiency, using coarser levels for broad filters and finer levels for precise queries, thereby improving overall efficiency without requiring the system to maintain all possible granularities simultaneously.
3Measurement precision
If all geohash depth levels are processed for spatial joins, then filtering accuracy improves, but computational load and processing time increase
Solution Approach 1:
The patent introduces dynamics by making geohash depth level selection adaptive rather than static. The system dynamically chooses which depth levels to process based on query characteristics, data distribution, and performance requirements. This dynamic approach allows the system to maintain high filtering accuracy when needed while reducing processing time for less critical queries, resolving the contradiction between precision and speed.
Solution Approach 2:
The patent applies partial action by processing only the necessary subset of geohash depth levels for each query rather than all levels. The system determines the minimum required depth levels based on query bounds and data characteristics, performing just enough filtering to achieve acceptable accuracy without the overhead of processing all possible depth levels, thus reducing processing time while maintaining sufficient precision.
Data Source
AI summary
A method, system, and program product for implementing an automated query filtering process for spatial data is provided. The method includes selecting a set of common depth levels for geohash structures. Data indicating results of the selection is stored and a specified depth level of the set of common geohash depth levels is selected. The selected geohash depth level is associated with a spatial column for spatial data to determine a set of geohash depth levels required to generate geohash values. A filter table or index associated with the spatial column is generated based on the selected subset of common geohash depth levels and a relationship between the spatial column, the specified geohash depth level and the filter table is stored within a database. Geohash values for the filter table are generated and a query of the database is executed with respect to the specified geohash depth level, the filter entries, and the filter table.