Voronoi Diagram-Based Progressive kNN Query for Moving Objects
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Solution Overview
Problem
Traditional methods for handling continuous k nearest neighbor (CkNN) queries in location-based services are inefficient, particularly when dealing with moving objects, as they require frequent re-querying along a trajectory, which is resource-intensive and costly, especially in large-scale applications with limited resources.
Innovation Solution
The implementation of progressive algorithms that utilize a Voronoi diagram and split points to continuously maintain and update k nearest neighbor results while a query point moves along a path, allowing for a one-time tree traversal and minimizing CPU, IO, and memory usage, enabling efficient and scalable handling of large data sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional methods are used for handling continuous k nearest neighbor queries with moving objects, then query results can be obtained, but resource consumption (CPU, IO, memory) is excessive and performance is poor
Solution Approach 1:
The patent pre-calculates and stores Voronoi diagrams and split points before queries are executed. By preparing the spatial partitioning structure in advance, the system avoids performing expensive computations during actual query operations, thereby improving query efficiency while reducing real-time resource consumption.
Solution Approach 2:
The patent divides the spatial space into Voronoi regions based on object locations, and further segments trajectories into segments between split points. This segmentation allows the system to process queries region-by-region and segment-by-segment rather than computing kNN for every point along entire trajectories, significantly reducing computational overhead and resource usage.
2Reliability
If frequent re-querying is performed along a trajectory to maintain continuous kNN results, then accurate results are obtained, but the cost and resource intensity increase significantly
Solution Approach 1:
The system pre-computes split points where kNN results may change along trajectories, and pre-builds Voronoi diagrams. This preliminary preparation enables the system to accurately track kNN changes by only evaluating at critical split points rather than continuously re-querying, maintaining accuracy while reducing operational complexity.
Solution Approach 2:
The patent introduces Voronoi diagrams and split points as intermediary structures that mediate between object locations and query results. These intermediaries encode spatial relationships and change points, allowing the system to derive accurate continuous kNN results through structured evaluation rather than frequent brute-force re-querying, thus reducing system complexity.
3Quantity of substance
If continuous kNN queries are executed for large-scale applications, then comprehensive results are provided, but CPU, IO, and memory resources are excessively consumed
Solution Approach 1:
The patent segments the large dataset into spatial partitions using Voronoi diagrams, where each region contains objects that are topologically close. This segmentation allows the system to process and query data in manageable chunks rather than handling the entire large dataset uniformly, reducing memory usage and I/O operations while maintaining comprehensive coverage of all objects.
Solution Approach 2:
The system applies different processing strategies to different spatial regions based on their local characteristics. By using Voronoi regions and split points, the system focuses computational resources only on relevant local areas where kNN results may change, rather than uniformly processing the entire large dataset, thereby reducing overall resource consumption while maintaining comprehensive results.
4Use of energy by moving object
If one-time tree traversal is implemented to minimize resource usage, then resource efficiency improves, but the ability to handle dynamic moving objects may be compromised
Solution Approach 1:
The patent uses dynamic split points that are computed based on current object locations and trajectories. While the Voronoi structure provides a stable framework for resource efficiency, the split points and their associated kNN results are dynamically updated as objects move, allowing the system to maintain both resource efficiency and adaptability to dynamic conditions.
Solution Approach 2:
The system continuously monitors object positions and updates split points and kNN results based on feedback from the dynamic environment. This feedback mechanism allows the one-time tree traversal structure to adapt to moving objects by recalculating only the necessary split points and regions, maintaining resource efficiency while preserving dynamic handling capability.
Data Source
AI summary
Methods and apparatuses for providing a k-nearest neighbor for location based services are provided. A method can include querying a database to detect a plurality of interest points within a predetermined distance of the user device using a kNN algorithm, organizing the interest points within a Voronoi tree, and continuously return a position specific result of relevant interest points.