scholarly journals Tree Index Nearest Neighbor Search of Moving Objects along a Road Network

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Wei Jiang ◽  
Fangliang Wei ◽  
Guanyu Li ◽  
Mei Bai ◽  
Yongqiang Ren ◽  
...  
Keyword(s):  
Road Network ◽  
Moving Objects ◽  
Nearest Neighbor ◽  
Search Algorithm ◽  
Urban Transportation ◽  
Road Networks ◽  
Nearest Neighbor Search ◽  
Index Structure ◽  
Widespread Application ◽  
Neighbor Search

With the widespread application of location-based service (LBS) technology in the urban Internet of Things, urban transportation has become a research hotspot. One key issue of urban transportation is the nearest neighbor search of moving objects along a road network. The fast-updating operations of moving objects along a road network suppress the query response time of urban services. Thus, a tree-indexed searching method is proposed to quickly find the answers to user-defined queries on frequently updating road networks. First, a novel index structure, called the double tree-hash index, is designed to reorganize the corresponding relationships of moving objects and road networks. Second, an index-enhanced search algorithm is proposed to quickly find the k -nearest neighbors of moving objects along the road network. Finally, an experiment shows that compared with state-of-the-art algorithms, our algorithm shows a significant improvement in search efficiency on frequently updating road networks.

scholarly journals Mobile Location Indexing Based On Synthetic Moving Objects

2019 ◽  
Vol 9 (4) ◽  
pp. 2556
Author(s):  
Thu Thu Zan ◽  
Sabai Phyu
Keyword(s):  
Performance Evaluation ◽  
Location Privacy ◽  
Moving Objects ◽  
Nearest Neighbor ◽  
Data Access ◽  
Nearest Neighbor Search ◽  
Index Structure ◽  
Mobile Objects ◽  
Neighbor Search ◽  
Privacy And Confidentiality

Today, the number of researches based on the data they move known as mobile objects indexing came out from the traditional static one. There are some indexing approaches to handle the complicated moving positions. One of the suitable ideas is pre-ordering these objects before building index structure. In this paper, a structure, a presorted-nearest index tree algorithm is proposed that allowed maintaining, updating, and range querying mobile objects within the desired period. Besides, it gives the advantage of an index structure to easy data access and fast query along with the retrieving nearest locations from a location point in the index structure. A synthetic mobile position dataset is also proposed for performance evaluation so that it is free from location privacy and confidentiality. The detail experimental results are discussed together with the performance evaluation of KDtree-based index structure. Both approaches are similarly efficient in range searching. However, the proposed approach is especially much more save time for the nearest neighbor search within a range than KD tree-based calculation.

INCREMENTAL K-NEAREST-NEIGHBOR SEARCH ON ROAD NETWORKS

2008 ◽  
Vol 09 (04) ◽  
pp. 455-470 ◽  
Author(s):  
GENG ZHAO ◽  
KEFENG XUAN ◽  
DAVID TANIAR ◽  
BALA SRINIVASAN
Keyword(s):  
Road Network ◽  
Nearest Neighbor ◽  
Road Networks ◽  
Nearest Neighbor Search ◽  
Query Point ◽  
K Nearest Neighbor ◽  
K Nearest Neighbors ◽  
Neighbor Search ◽  
New Type ◽  
K Nearest Neighbor Search

Most query search on road networks is either to find objects within a certain range (range search) or to find K nearest neighbors (KNN) on the actual road network map. In this paper, we propose a novel query, that is, incremental k nearest neighbor (iKNN). iKNN can be defined as given a set of candidate interest objects, a query point and the number of objects k, find a path which starts at the query point, goes through k interest objects and the distance of this path is the shortest among all possible paths. This is a new type of query, which can be used when we want to visit k interest objects one by one from the query point. This approach is based on expanding the network from the query point, keeping the results in a query set and updating the query set when reaching network intersection or interest objects. The basic theory of this approach is Dijkstra's algorithm and the Incremental Network Expansion (INE) algorithm. Our experiment verified the applicability of the proposed approach to solve the queries, which involve finding incremental k nearest neighbor.

scholarly journals Complexity of Error-Correcting Code Based on Nearest Neighbor Search Algorithm

2019 ◽  
pp. 7-20
Author(s):  
Levon Arsalanyan ◽  
Hayk Danoyan
Keyword(s):  
Time Complexity ◽  
Nearest Neighbor ◽  
Search Algorithm ◽  
Error Correcting Code ◽  
Error Correcting Codes ◽  
Code Word ◽  
Nearest Neighbor Search ◽  
Perfect Codes ◽  
Neighbor Search ◽  
Weight Structures

The Nearest Neighbor search algorithm considered in this paper is well known (Elias algorithm). It uses error-correcting codes and constructs appropriate hash-coding schemas. These schemas preprocess the data in the form of lists. Each list is contained in some sphere, centered at a code-word. The algorithm is considered for the cases of perfect codes, so the spheres and, consequently, the lists do not intersect. As such codes exist for the limited set of parameters, the algorithm is considered for some other generalizations of perfect codes, and then the same data point may be contained in different lists. A formula of time complexity of the algorithm is obtained for these cases, using coset weight structures of the mentioned codes

scholarly journals A Hybrid Spatial Indexing Structure of Massive Point Cloud Based on Octree and 3D R*-Tree

2021 ◽  
Vol 11 (20) ◽  
pp. 9581
Author(s):  
Wei Wang ◽  
Yi Zhang ◽  
Genyu Ge ◽  
Qin Jiang ◽  
Yang Wang ◽  
...  
Keyword(s):  
Point Cloud ◽  
Nearest Neighbor ◽  
Nearest Neighbor Search ◽  
Index Structure ◽  
Spatial Indexing ◽  
Cloud Data ◽  
Neighbor Search ◽  
Nearest Neighbor Searching ◽  
Indexing Structure ◽  
Balanced Tree

The spatial index structure is one of the most important research topics for organizing and managing massive 3D Point Cloud. As a point in Point Cloud consists of Cartesian coordinates (x,y,z), the common method to explore geometric information and features is nearest neighbor searching. An efficient spatial indexing structure directly affects the speed of the nearest neighbor search. Octree and kd-tree are the most used for Point Cloud data. However, Octree or KD-tree do not perform best in nearest neighbor searching. A highly balanced tree, 3D R*-tree is considered the most effective method so far. So, a hybrid spatial indexing structure is proposed based on Octree and 3D R*-tree. In this paper, we discussed how thresholds influence the performance of nearest neighbor searching and constructing the tree. Finally, an adaptive way method adopted to set thresholds. Furthermore, we obtained a better performance in tree construction and nearest neighbor searching than Octree and 3D R*-tree.

Fast multidimensional nearest neighbor search algorithm using priority queue

2008 ◽  
Vol 164 (3) ◽  
pp. 69-77 ◽  
Author(s):  
Shiro Ajioka ◽  
Satoru Tsuge ◽  
Masami Shishibori ◽  
Kenji Kita
Keyword(s):  
Nearest Neighbor ◽  
Search Algorithm ◽  
Priority Queue ◽  
Nearest Neighbor Search ◽  
Neighbor Search
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