Voronoi Diagram Progressive Query for Moving Objects
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Solution Overview
Problem
Existing continuous range query techniques for location-based services are resource-intensive and result in slow response times, making them inefficient for real-time and predictive location-based applications.
Innovation Solution
The implementation of Voronoi diagram-based progressive continuous range queries, which utilize a min-heap of split points to progressively output query results as a moving query point changes its distance to objects of interest, reducing computational resources and enabling continuous, real-time updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If existing continuous range query techniques are used, then query results can be obtained, but computing time and resource consumption are excessive
Solution Approach 1:
The patent divides the continuous query space into discrete Voronoi regions based on object locations. By segmenting the query processing into region-based units rather than continuous computation, the system reduces computing time while maintaining query accuracy, directly addressing the contradiction between fast response and processing efficiency.
Solution Approach 2:
The system pre-computes Voronoi diagrams and maintains them in advance, so when a continuous range query arrives, the results can be quickly derived from the pre-prepared spatial structure. This preliminary action eliminates the need for complex real-time computations, significantly reducing computing time without sacrificing productivity.
2Speed
If existing continuous range query techniques are used, then query results can be returned, but response time is slow
Solution Approach 1:
The Voronoi diagram serves as an intermediary data structure that mediates between the query point and the object database. Instead of directly computing distances to all objects, the system uses Voronoi regions to quickly determine which objects are within range, dramatically improving response time while reducing computational complexity through this intermediate spatial representation.
Solution Approach 2:
The patent replaces the mechanical approach of continuously calculating distances to all objects with a geometric substitution using Voronoi diagram properties. The computational complexity is reduced by substituting complex distance calculations with simpler Voronoi region membership tests, achieving fast response times without high computational overhead.
3Reliability
If comprehensive query results are maintained for moving objects, then location-based services are complete, but CPU, IO, and memory resources are excessively consumed
Solution Approach 1:
The system dynamically updates Voronoi diagrams only when object locations change, rather than continuously re-computing all query results. This dynamic approach maintains complete and reliable query results while significantly reducing CPU, IO, and memory resource consumption by updating only the necessary portions of the spatial structure.
Solution Approach 2:
The patent discards redundant computation and data maintenance when objects have not moved, and only recovers (updates) the specific Voronoi regions affected by object location changes. This selective updating maintains query result completeness while minimizing resource consumption by avoiding unnecessary computations and memory operations.
Data Source
AI summary
Devices and methods for providing Voronoi diagram-based progressive continuous range query (CRQ) computation for location-based services are provided. A method can include indexing all interest points using a Voronoi R-tree data structure in a server-side computing device. When a client mobile device sends a CRQ request, the server-side device receives a path of travel and builds a list to store interest points and a min-heap to store split points, iteratively retrieving split points from the min-heap, and reporting the CRQ result progressively until a termination condition is reached.