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Most relevant point query on road networks
Graphs are widespread in many real-life practical applications. One of a graph’s fundamental and popular researches is investigating the relations between two given vertices. The relationship between nodes in the graph can be measured by the shortest distance. Moreover, the number of paths is also a...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer London
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9244333/ https://www.ncbi.nlm.nih.gov/pubmed/35789916 http://dx.doi.org/10.1007/s00521-022-07485-x |
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author | Zhang, Zining Yang, Shenghong Qin, Yunchuan Yang, Zhibang Huang, Yang Zhou, Xu |
author_facet | Zhang, Zining Yang, Shenghong Qin, Yunchuan Yang, Zhibang Huang, Yang Zhou, Xu |
author_sort | Zhang, Zining |
collection | PubMed |
description | Graphs are widespread in many real-life practical applications. One of a graph’s fundamental and popular researches is investigating the relations between two given vertices. The relationship between nodes in the graph can be measured by the shortest distance. Moreover, the number of paths is also a popular metric to assess the relationship of different nodes. In many location-based services, users make decisions on the basis of both the two metrics. To address this problem, we propose a new hybrid-metric based on the number of paths with a distance constraint for road networks, which are special graphs. Based on it, a most relevant node query on road networks is identified. To handle this problem, we first propose a Shortest-Distance Constrained DFS, which uses the shortest distance to prune unqualified nodes. To further improve query efficiency, we present Batch Query DFS algorithm, which only needs only one DFS search. Our experiments on four real-life road networks demonstrate the performance of the proposed algorithms. |
format | Online Article Text |
id | pubmed-9244333 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer London |
record_format | MEDLINE/PubMed |
spelling | pubmed-92443332022-06-30 Most relevant point query on road networks Zhang, Zining Yang, Shenghong Qin, Yunchuan Yang, Zhibang Huang, Yang Zhou, Xu Neural Comput Appl S.I.: Efficient Artificial Intelligent Algorithms for Medical Image Analysis Based on High-Performance Computing Graphs are widespread in many real-life practical applications. One of a graph’s fundamental and popular researches is investigating the relations between two given vertices. The relationship between nodes in the graph can be measured by the shortest distance. Moreover, the number of paths is also a popular metric to assess the relationship of different nodes. In many location-based services, users make decisions on the basis of both the two metrics. To address this problem, we propose a new hybrid-metric based on the number of paths with a distance constraint for road networks, which are special graphs. Based on it, a most relevant node query on road networks is identified. To handle this problem, we first propose a Shortest-Distance Constrained DFS, which uses the shortest distance to prune unqualified nodes. To further improve query efficiency, we present Batch Query DFS algorithm, which only needs only one DFS search. Our experiments on four real-life road networks demonstrate the performance of the proposed algorithms. Springer London 2022-06-28 /pmc/articles/PMC9244333/ /pubmed/35789916 http://dx.doi.org/10.1007/s00521-022-07485-x Text en © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | S.I.: Efficient Artificial Intelligent Algorithms for Medical Image Analysis Based on High-Performance Computing Zhang, Zining Yang, Shenghong Qin, Yunchuan Yang, Zhibang Huang, Yang Zhou, Xu Most relevant point query on road networks |
title | Most relevant point query on road networks |
title_full | Most relevant point query on road networks |
title_fullStr | Most relevant point query on road networks |
title_full_unstemmed | Most relevant point query on road networks |
title_short | Most relevant point query on road networks |
title_sort | most relevant point query on road networks |
topic | S.I.: Efficient Artificial Intelligent Algorithms for Medical Image Analysis Based on High-Performance Computing |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9244333/ https://www.ncbi.nlm.nih.gov/pubmed/35789916 http://dx.doi.org/10.1007/s00521-022-07485-x |
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