Interval-Based Nearest Neighbor Queries over Sliding Windows from Trajectory Data

2009 ◽  
Author(s):  
Yan Huang ◽  
Chengyang Zhang
Keyword(s):  
Nearest Neighbor ◽  
Trajectory Data ◽  
Sliding Windows ◽  
Nearest Neighbor Queries

scholarly journals A Practical Index Structure Supporting Fréchet Proximity Queries among Trajectories

2021 ◽  
Vol 7 (3) ◽  
pp. 1-33
Author(s):  
Joachim Gudmundsson ◽  
Michael Horton ◽  
John Pfeifer ◽  
Martin P. Seybold
Keyword(s):  
Nearest Neighbor ◽  
Search Space ◽  
Spatial Dimension ◽  
Index Structure ◽  
Trajectory Data ◽  
Efficiency And Effectiveness ◽  
Real World Datasets ◽  
Dynamic Tree ◽  
Search Space Pruning ◽  
Nearest Neighbor Queries

We present a scalable approach for range and k nearest neighbor queries under computationally expensive metrics, like the continuous Fréchet distance on trajectory data. Based on clustering for metric indexes, we obtain a dynamic tree structure whose size is linear in the number of trajectories, regardless of the trajectory’s individual sizes or the spatial dimension, which allows one to exploit low “intrinsic dimensionality” of datasets for effective search space pruning. Since the distance computation is expensive, generic metric indexing methods are rendered impractical. We present strategies that (i) improve on known upper and lower bound computations, (ii) build cluster trees without any or very few distance calls, and (iii) search using bounds for metric pruning, interval orderings for reduction, and randomized pivoting for reporting the final results. We analyze the efficiency and effectiveness of our methods with extensive experiments on diverse synthetic and real-world datasets. The results show improvement over state-of-the-art methods for exact queries, and even further speedups are achieved for queries that may return approximate results. Surprisingly, the majority of exact nearest-neighbor queries on real datasets are answered without any distance computations.

Continuous K Nearest Neighbor Queries of Moving Objects in Road Networks

2010 ◽  
Vol 33 (8) ◽  
pp. 1396-1404 ◽  
Author(s):  
Liang ZHAO ◽  
Luo CHEN ◽  
Ning JING ◽  
Wei LIAO
Keyword(s):  
Moving Objects ◽  
Nearest Neighbor ◽  
Road Networks ◽  
Nearest Neighbor Queries

Aggregate keyword nearest neighbor queries on road networks

2017 ◽  
Vol 22 (2) ◽  
pp. 237-268 ◽  
Author(s):  
Pengfei Zhang ◽  
Huaizhong Lin ◽  
Yunjun Gao ◽  
Dongming Lu
Keyword(s):  
Nearest Neighbor ◽  
Road Networks ◽  
Nearest Neighbor Queries

scholarly journals A MEDIAN-BASED DEPTHMAP FUSION STRATEGY FOR THE GENERATION OF ORIENTED POINTS

2016 ◽  
Vol III-3 ◽  
pp. 115-122
Author(s):  
M. Rothermel ◽  
N. Haala ◽  
D. Fritsch
Keyword(s):  
Surface Reconstruction ◽  
Nearest Neighbor ◽  
State Of The Art ◽  
Depth Map ◽  
Median Filtering ◽  
Tree Structures ◽  
Fusion Strategy ◽  
Depth Maps ◽  
Disparity Maps ◽  
Nearest Neighbor Queries

Due to good scalability, systems for image-based dense surface reconstruction often employ stereo or multi-baseline stereo methods. These types of algorithms represent the scene by a set of depth or disparity maps which eventually have to be fused to extract a consistent, non-redundant surface representation. Generally the single depth observations across the maps possess variances in quality. Within the fusion process not only preservation of precision and detail but also density and robustness with respect to outliers are desirable. Being prune to outliers, in this article we propose a local median-based algorithm for the fusion of depth maps eventually representing the scene as a set of oriented points. Paying respect to scalability, points induced by each of the available depth maps are streamed to cubic tiles which then can be filtered in parallel. Arguing that the triangulation uncertainty is larger in the direction of image rays we define these rays as the main filter direction. Within an additional strategy we define the surface normals as the principle direction for median filtering/integration. The presented approach is straight-forward to implement since employing standard oc- and kd-tree structures enhanced by nearest neighbor queries optimized for cylindrical neighborhoods. We show that the presented method in combination with the MVS (Rothermel et al., 2012) produces surfaces comparable to the results of the Middlebury MVS benchmark and favorably compares to an state-of-the-art algorithm employing the Fountain dataset (Strecha et al., 2008). Moreover, we demonstrate its capability of depth map fusion for city scale reconstructions derived from large frame airborne imagery.

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