Progressive Continuous Range Query Using Tree-Like Index
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Solution Overview
Problem
Existing continuous range query methods are inefficient, requiring extensive computing resources and being slow to return query results, especially when dealing with moving objects in location-based services, as they need to reload and sort data for each segment of a query point's trajectory, leading to CPU and memory overhead and potentially irrelevant results due to user location changes.
Innovation Solution
The progressive continuous range query method uses a tree-like index to efficiently search for objects within a range by generating split points along a query point's trajectory, allowing for instantaneous reporting of initial results and gradual production of subsequent results in order of distance or time, reducing the need for repeated queries and minimizing CPU and memory usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing CRQ methods are used to query moving object data, then query results can be obtained, but extensive computing resources are required and the query speed is slow
Solution Approach 1:
The patent pre-calculates and stores split points (intersection points between query trajectories and domain region boundaries) in advance within the tree-like index structure. When a continuous range query is executed, the system only needs to traverse these pre-computed split points along the query trajectory rather than performing complex geometric calculations for each query segment, significantly reducing computing resources and improving query speed
Solution Approach 2:
The patent divides the query trajectory into segments based on pre-computed split points, where each segment represents a portion of the trajectory where the set of objects within range remains constant. This segmentation allows the system to process queries in discrete steps rather than continuously, reducing the computational burden while maintaining query accuracy
2Measurement precision
If existing CRQ methods reload and sort data for each segment of query trajectory, then complete query results are obtained, but CPU and memory overhead increases significantly
Solution Approach 1:
The patent pre-organizes data into a tree-like index structure with pre-computed domain regions and split points during the indexing phase. This preliminary organization eliminates the need for repeated data loading and sorting during query execution, as the index structure already contains all necessary information about where objects may enter or exit the query range along any given trajectory
Solution Approach 2:
The patent uses a dynamic query processing approach that traverses the pre-built index structure following the query trajectory, activating only the necessary index nodes and split points relevant to the current query. This dynamic traversal avoids processing entire datasets or segments that are not relevant to the query, reducing CPU and memory overhead while maintaining result accuracy
3Reliability
If users wait for complete CRQ results, then all query results are received, but user location may change making results irrelevant
Solution Approach 1:
The patent segments the query results into discrete portions corresponding to different trajectory segments defined by split points. The system can return results incrementally as the query point moves through each segment, allowing users to receive and use relevant results before their location changes, while still maintaining the option to retrieve complete results if needed
Solution Approach 2:
The patent implements periodic result delivery aligned with the query trajectory progression. As the query point reaches each pre-computed split point, the system periodically delivers the current set of objects within range, providing updated results at regular intervals along the trajectory rather than requiring users to wait for complete results, thus maintaining result relevance to current location
Data Source
AI summary
Methods for progressive continuous range query (PCRQ) are provided. A method can include using branch-and-bound to index interest points with a tree-index and generating a nearest enter split-point for a root node in the tree-index and adding to min-heap. Next, whether min-heap has more elements and whether a next split-point in min-heap is closer than a destination can be determined. Whether a query point has reached a split-point can be investigated followed by retrieving an entry that has generated the split-point. The split-point can be then be removed from min-heap.