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.

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 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.

Bregman Hyperplane Trees for Fast Approximate Nearest Neighbor Search

2012 ◽  
Vol 3 (4) ◽  
pp. 75-87
Author(s):  
Bilegsaikhan Naidan ◽  
Magnus Lie Hetland
Keyword(s):  
Query Processing ◽  
Nearest Neighbor ◽  
Nearest Neighbor Search ◽  
Index Structure ◽  
Data Sets ◽  
Search Performance ◽  
Space Partitioning ◽  
Neighbor Search ◽  
Order Of Magnitude ◽  
Exact Nearest Neighbor

This article presents a new approximate index structure, the Bregman hyperplane tree, for indexing the Bregman divergence, aiming to decrease the number of distance computations required at query processing time, by sacrificing some accuracy in the result. The experimental results on various high-dimensional data sets demonstrate that the proposed index structure performs comparably to the state-of-the-art Bregman ball tree in terms of search performance and result quality. Moreover, this method results in a speedup of well over an order of magnitude for index construction. The authors also apply their space partitioning principle to the Bregman ball tree and obtain a new index structure for exact nearest neighbor search that is faster to build and a slightly slower at query processing than the original.

Simple and Incremental Nearest-Neighbor Search in Spatio-Temporal Databases

2011 ◽  
pp. 204-224
Author(s):  
Katerina Raptopoulou ◽  
Apostolos N. Papadopoulos ◽  
Yannis Manolopoulos
Keyword(s):  
Traffic Control ◽  
Moving Objects ◽  
Nearest Neighbor ◽  
Nearest Neighbor Search ◽  
Temporal Databases ◽  
Wireless Technologies ◽  
Neighbor Search ◽  
Efficient Processing ◽  
Spatio Temporal ◽  
Nearest Neighbor Queries

The efficient processing of nearest-neighbor queries in databases of moving objects is considered very important for applications such as fleet management, traffic control, digital battlefields and more. Such applications have been rapidly spread due to the fact that mobile computing and wireless technologies nowadays are ubiquitous. This chapter presents important aspects towards simple and incremental nearest-neighbor search for spatio-temporal databases. More specifically, we describe the algorithms that have already been proposed for simple and incremental nearest neighbor queries and present a new algorithm regarding that issue. Finally, we study the problem of keeping a query consistent in the presence of insertions, deletions and updates of moving objects.

Approximate aggregate nearest neighbor search on moving objects trajectories

2015 ◽  
Vol 22 (11) ◽  
pp. 4246-4253 ◽  
Author(s):  
Mohammad Reza Abbasifard ◽  
Hassan Naderi ◽  
Zohreh Fallahnejad ◽  
Omid Isfahani Alamdari
Keyword(s):  
Moving Objects ◽  
Nearest Neighbor ◽  
Nearest Neighbor Search ◽  
Neighbor Search ◽  
Aggregate Nearest Neighbor

scholarly journals How Good Are Modern Spatial Libraries?

2020 ◽  
Author(s):  
Varun Pandey ◽  
Alexander van Renen ◽  
Andreas Kipf ◽  
Alfons Kemper
Keyword(s):  
Spatial Data ◽  
High Performance ◽  
Nearest Neighbor ◽  
Vantage Point ◽  
Nearest Neighbor Search ◽  
Index Structure ◽  
Spatial Query ◽  
Neighbor Search ◽  
Spatial Data Management ◽  
Real World Datasets

Abstract Many applications today like Uber, Yelp, Tinder, etc. rely on spatial data or locations from its users. These applications and services either build their own spatial data management systems or rely on existing solutions. JTS Topology Suite (JTS), its C++ port GEOS, Google S2, ESRI Geometry API, and Java Spatial Index (JSI) are some of the spatial processing libraries that these systems build upon. These applications and services depend on indexing capabilities available in these libraries for high-performance spatial query processing. In this work, we compare these libraries qualitatively and quantitatively based on four different spatial queries using two real world datasets. We also compare these libraries with an open-source implementation of the Vantage Point Tree—an index structure that has been well studied in image retrieval and nearest-neighbor search algorithms for high-dimensional data. We found that Vantage Point Trees are very competitive and even outperform the aforementioned libraries in two queries.

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