Efficient Processing of Continuous Reverse k Nearest Neighbor on Moving Objects in Road Networks

Attique, Muhammad; Cho, Hyung‐Ju; Jin, Rize; Chung, Tae-Sun

doi:10.3390/ijgi5120247

Cited by 4 publications

(4 citation statements)

References 23 publications

(29 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, research focus has shifted to the continuous processing of spatial queries where query or data objects are arbitrarily moving in road networks, which is the most realistic scenario. Considerable research effort has been undertaken to process moving range, k nearest neighbor (kNN), and reverse k nearest neighbor queries (RkNN) [15][16][17][18]. However, there is a lack of efficient algorithms for moving top-k spatial keyword queries.…”

Section: Moving Top-k Spatial Keyword Queriesmentioning

confidence: 99%

Efficient Processing of Moving Top‐k Spatial Keyword Queries in Directed and Dynamic Road Networks

Attique

Cho

Chung

2018

Wireless Communications and Mobile Computing

Self Cite

View full text Add to dashboard Cite

A top-k spatial keyword (TkSk) query ranks objects based on the distance to the query location and textual relevance to the query keywords. Several solutions have been proposed for top-k spatial keyword queries. However, most of the studies focus on Euclidean space or only investigate the snapshot queries where both the query and data object are static. A few algorithms study TkSk queries in undirected road networks where each edge is undirected and the distance between two points is the length of the shortest path connecting them. However, TkSk queries have not been thoroughly investigated in directed and dynamic spatial networks where each edge has a particular orientation and its weight changes according to the traffic conditions. Therefore, in this study, we address this problem by presenting a new method, called COSK, for processing continuous top-k spatial keyword queries for moving queries in directed and dynamic road networks. We first propose an efficient framework to process snapshot TkSK queries. Furthermore, we propose a safe-exit-based approach to monitor the validity of the results for moving TkSK queries. Our experimental results demonstrate that COSK significantly outperforms existing techniques in terms of query processing time and communication cost.

show abstract

Section: Moving Top-k Spatial Keyword Queriesmentioning

confidence: 99%

Efficient Processing of Moving Top‐k Spatial Keyword Queries in Directed and Dynamic Road Networks

Attique

Cho

Chung

2018

Wireless Communications and Mobile Computing

Self Cite

View full text Add to dashboard Cite

show abstract

“…After the procedure is done, the car count in the scene is simply determined by the final number of found key points. Many recent works are based on deep learning for the application of counting moving vehicles or for traffic scene understanding [20,21]. Zheng et al [22], used five deep unsupervised learning models to learn driving modes.…”

Section: Vehicle Detection In Remote Sensing Images Mohammed A-m Samentioning

confidence: 99%

Vehicle Detection In Remote Sensing Images

2019

IJITEE

View full text Add to dashboard Cite

Traffic monitoring and management is one of the most crucial tasks of governing bodies in modern big cities. With each passing day the traffic problem grows in complexity due to the continuous increase of participating vehicles and the hard expansion of the road network and parking places. In this article we introduce a new method for vehicle detection and localization in parking lots using high resolution UAV images. In order to end up with practical and yet effective approach, which could be implemented on low computing hardware resources and integrated with the camera in the UAV, we considered simple steps in the proposed algorithm for optimization. It follows the machine learning pipeline such as preprocessing, sensing, feature extraction, training and classification. In preprocessing the images are thresholded iteratively in multiple color spaces to extract the candidate regions of interest (ROI). The algorithm relies on point and shape features using fast techniques in the feature extraction. The features are then clustered by the K-means algorithm and represented by the resulted clusters’ centers. Region based linear classification is finally applied using SVM to classify if the object is a vehicle or else. The proposed approach proved high detection and classification accuracy more than 86% and still running under the low complexity constraint..

show abstract

“…The use of these LBS applications results in the collection of enormous amounts of location data known as spatial data, and the corresponding queries are known as spatial queries [1]. The most common instances of spatial queries are linked to LBS incorporate shortest path queries [2], [3], range queries [4], [5], k-nearest neighbor queries [6], [7], reverse k-NN queries [8], [9], keyword queries [10], [11], and preference queries [12], [13]. Location data usually consist of user query requests.…”

Section: Introductionmentioning

confidence: 99%

ParSCL: A Parallel and Distributed Framework to Process All Nearest Neighbor Queries on a Road Network

Bhandari,

Hamandawana,

Attique

et al. 2023

IEEE Access

Self Cite

View full text Add to dashboard Cite

The proliferation of current and next-generation mobile and sensing devices has increased at an alarming rate. With these state-of-the-art devices, the global positioning system (GPS) has made remote sensing and location tracking more viable. One such query is the All Nearest Neighbor (ANN ) query, which extracts and returns all data objects that are in close vicinity to all query objects. An ANN is a combination of k-nearest neighbors (kNN ), and join queries. Hence, ANN has useful for applications in different domains such as transportation optimization, locating safe zones, and ride-sharing. An example of its applications is, ''find the nearest gas station for each car parking lot''. Because these applications are responsible for generating a massive number of query requests, a large amount of computation is required to return these query requests. As a single machine cannot meet this demand in this study, we propose a distributed query processing framework to process ANN queries using the Apache Spark framework. In an empirical study, our proposed framework achieved superior query efficiency and scalability compared to other methods and design alternatives.INDEX TERMS All nearest neighbor queries, distributed and parallel processing, spatial query processing.

show abstract

Efficient Processing of Continuous Reverse k Nearest Neighbor on Moving Objects in Road Networks

Cited by 4 publications

References 23 publications

Efficient Processing of Moving Top‐k Spatial Keyword Queries in Directed and Dynamic Road Networks

Efficient Processing of Moving Top‐k Spatial Keyword Queries in Directed and Dynamic Road Networks

Vehicle Detection In Remote Sensing Images

ParSCL: A Parallel and Distributed Framework to Process All Nearest Neighbor Queries on a Road Network

Contact Info

Product

Resources

About