Adaptive Geospatial Query Radius for Entity Threshold Search
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Solution Overview
Problem
Existing geospatial database queries are computationally expensive and inefficient, particularly when searching for a specific number of entities near a given location, leading to long wait times and resource consumption due to iterative adjustments of search parameters.
Innovation Solution
A self-adaptive query system that adjusts search regions in real-time to meet an entity threshold requirement and implements query caching for geospatial cells to optimize future queries based on previous results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional geospatial database queries are used to search for entities near a given location, then the query can return results, but the search performance is reduced and processing time increases due to large numbers of geographic locations and iterative parameter adjustments
Solution Approach 1:
The patent pre-calculates and stores optimal search radius values in a lookup table during a setup phase, organized by entity threshold requirements. When a query is executed, the system directly retrieves the pre-determined radius from the lookup table based on the required entity count, eliminating the need for iterative radius adjustments during actual query processing. This preliminary preparation enables instant query execution with optimal parameters.
Solution Approach 2:
The patent implements a dynamic query optimization system that automatically adjusts search parameters based on the specific query requirements. The system selects different search radii from the lookup table depending on the entity threshold, and can dynamically modify query parameters such as search depth and region boundaries to optimize performance for each specific query scenario while maintaining adaptability to varying data distributions.
2Reliability
If the search region is expanded to ensure finding a threshold number of entities, then the entity threshold requirement is met, but the computational cost and processing time increase
Solution Approach 1:
The system pre-computes optimal search radii for various entity threshold requirements and stores them in a lookup table. Each entry in the lookup table represents the minimum radius needed to satisfy a specific entity threshold, pre-determined through offline analysis. During query execution, the system simply retrieves the appropriate radius from the lookup table, ensuring the entity threshold is met with minimal computational overhead and avoiding unnecessary expansion of the search region.
Solution Approach 2:
The patent changes the search radius parameter dynamically based on the entity threshold requirement by selecting from pre-calculated values in the lookup table. This parameter optimization ensures that the search region is exactly large enough to satisfy the entity threshold without being excessively large, thereby reducing computational cost while maintaining reliability of meeting the threshold requirement.
3Adaptability or versatility
If iterative adjustments of search parameters are performed to meet entity thresholds, then the query can adapt to different scenarios, but the query efficiency decreases and latency increases
Solution Approach 1:
The patent pre-calculates optimal search parameters for various entity threshold scenarios and stores them in a lookup table during a setup phase. This preliminary action captures the adaptability logic in advance, allowing the system to instantly retrieve appropriate parameters for any given entity threshold without performing iterative adjustments at query time, thereby eliminating latency while preserving adaptability.
Solution Approach 2:
The system uses the entity threshold requirement as feedback to select the appropriate pre-calculated search radius from the lookup table. This feedback mechanism ensures that the query parameters are automatically adapted to match the specific requirements of each query scenario, maintaining versatility while avoiding the time-consuming iterative adjustment process.
Data Source
AI summary
Techniques are disclosed relating to determining a minimum search region with a threshold number of entities within the minimum search region. In some embodiments, a system selects, based on a location of a user device, a first region, where the location of the device is at a center of the first region. The system executes, based on the first region, a query on a database storing entity locations. The system increases the first region by a specified amount to generate a second search region, where the increasing is based on determining that entities returned by the first query does not satisfy an entity threshold requirement. The system executes, based on the second region, a second query on the database. In response to the second query returning a number of entities that satisfy the threshold requirement, the system causes display, at the device, of the entities returned by the second query.