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Most relevant point query on road networks

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Abstract

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.

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References

  1. Liu B, Yuan L, Lin, X, Qin L, Zhang W, Zhou J (2019) Efficient (\(\alpha \), \(\beta \))-core computation: An index-based approach. In: The World Wide Web Conference. WWW ’19, pp 1130–1141. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3308558.3313522

  2. Greco S, Molinaro C, Pulice C (2018) Efficient maintenance of shortest distances in dynamic graphs. IEEE Trans Knowl Data Eng 30(3):474–487. https://doi.org/10.1109/TKDE.2017.2772233

    Article  MATH  Google Scholar 

  3. Li L, Zhang M, Hua W, Zhou X (2020) Fast query decomposition for batch shortest path processing in road networks. In: 2020 IEEE 36th international conference on data engineering (ICDE)

  4. Chang L, Lin X, Qin L, Yu JX, Pei J (2015) Efficiently computing top-k shortest path join. In: EDBT

  5. Chondrogiannis T, Bouros P, Gamper J, Leser U, Blumenthal DB (2020) Finding k-shortest paths with limited overlap. VLDB J (2)

  6. Zhong R, Li G, Tan KL, Zhou L (2013) G-tree: An efficient index for knn search on road networks. In: Proceedings of the 22nd ACM international conference on conference on information and knowledge management

  7. Zhang S, Li X, Zong M, Zhu X, Wang R (2018) Efficient knn classification with different numbers of nearest neighbors. IEEE Trans Neural Netw Learn Syst 29(5):1774–1785. https://doi.org/10.1109/TNNLS.2017.2673241

    Article  MathSciNet  MATH  Google Scholar 

  8. Gao Y, Chen L, Li X, Yao B, Chen G (2015) Efficient k-closest pair queries in general metric spaces. VLDB J 24(3):415–439. https://doi.org/10.1007/s00778-015-0383-4

    Article  MATH  Google Scholar 

  9. Ahmadi E, Nascimento MA (2016) K-closest pairs queries in road networks. In: 2016 17th IEEE international conference on mobile data management (MDM), vol 1, pp 232–241. https://doi.org/10.1109/MDM.2016.44

  10. Ferreira R, Grossi R, Marino A, Pisanti N, Rizzi R, Sacomoto G (2012) Optimal listing of cycles and st-paths in undirected graphs. Proc Ann ACM SIAM Symp Discrete Algorithms. https://doi.org/10.1137/1.9781611973105.134

    Article  MATH  Google Scholar 

  11. Leser U (2005) A query language for biological networks. Bioinformatics (Oxford, England) 21(Suppl 2):33–9. https://doi.org/10.1093/bioinformatics/bti1105

    Article  MATH  Google Scholar 

  12. Qiu X, Cen W, Qian Z, Peng Y, Zhang Y, Lin X, Zhou J (2018) Real-time constrained cycle detection in large dynamic graphs. Proc VLDB Endowment 11(12):1876–1888

    Article  MATH  Google Scholar 

  13. Sun S, Chen Y, He B, Hooi B (2021) Pathenum: towards real-time hop-constrained s-t path enumeration

  14. Peng Y, Zhang Y, Lin X, Zhang W, Qin L, Zhou J (2019) Hop-constrained s-t simple path enumeration: Towards bridging theory and practice. Proc VLDB Endow 13(4):463–476

    Article  MATH  Google Scholar 

  15. Lu S, He B, Li Y, Fu H (2020) Accelerating exact constrained shortest paths on gpus. Proc VLDB Endow 14(4):547–559. https://doi.org/10.14778/3436905.3436914

  16. Selvakkumaran N, Karypis G (1995) Analysis of multilevel graph partitioning. In: supercomputing, IEEE/ACM Sc95 conference

  17. Barrientos RJ, Riquelme JA, Hernández-García R, Navarro CA, Soto-Silva W (2021) Fast knn query processing over a multi-node gpu environment

  18. Naim A, Bowkett J, Karumanchi S, Tavallali P, Kennedy B (2021) Deterministic iteratively built kd-tree with knn search for exact applications

  19. Vadiraja P, Balada CP (2021) Leveraging reinforcement learning for evaluating robustness of knn search algorithms

  20. Corral A, Manolopoulos Y, Theodoridis Y, Vassilakopoulos M (2004) Algorithms for processing k-closest-pair queries in spatial databases. Data Knowl Eng 49(1):67–104

    Article  MATH  Google Scholar 

  21. Roumelis G, Vassilakopoulos M, Corral A, Manolopoulos Y (2014) A new plane-sweep algorithm for the k-closest-pairs query. In: International conference on current trends in theory and practice of informatics

  22. Karypis G, Kumar V (1998) A fast and high quality multilevel scheme for partitioning irregular graphs. Siam J Sci Comput 20(1):359

    Article  MathSciNet  MATH  Google Scholar 

  23. Wu F, Xie X, Shi J (2021) Top-k Closest Pair Queries over Spatial Knowledge Graph. Database systems for advanced applications, 26th international conference, DASFAA 2021, Taipei, Taiwan, 11–14 Apr 2021. Proceedings, Part I

  24. Hu H, Lee DL, Xu J (2006) Fast nearest neighbor search on road networks. In: Advances in database technology—EDBT 2006, 10th international conference on extending database technology, Munich, Germany, 26–31 Mar 2006, Proceedings

  25. Gotthilf Z, Lewenstein M (2009) Improved algorithms for the k simple shortest paths and the replacement paths problems. Inf Proc Lett 109(7):352–355. https://doi.org/10.1016/j.ipl.2008.12.015

    Article  MathSciNet  MATH  Google Scholar 

  26. Hershberger J, Maxel M, Suri S (2007) Finding the k shortest simple paths: a new algorithm and its implementation. ACM Trans Algorithms 3(4)

  27. Jin YY (1971) Finding the k shortest loopless paths in a network. Manage Sci 17(11):712–716

    MathSciNet  MATH  Google Scholar 

  28. Knuth DonaldErvin (2011) The art of computer programming. Volume 4A: Combinatorial Algorithms,Addison-Wesley Professional

  29. Roberts B, Kroese DP (2007) Estimating the number of s-t paths in a graph. J Graph Algo Appl 11(1):195–214

    Article  MathSciNet  MATH  Google Scholar 

  30. Rizzi R, Sacomoto G, Sagot MF (2014) Efficiently listing bounded length st-paths. Eprint Arxiv 8986:318–329

    MathSciNet  MATH  Google Scholar 

  31. Grossi R, Marino A, Versari L (2018) Efficient algorithms for listing k disjoint st-paths in graphs. Latin Am Symp Theor Inf

  32. Lai Z, Peng Y, Yang S, Lin X, Zhang W (2020) Pefp: Efficient k-hop constrained s-t simple path enumeration on fpga

  33. Peng Y, Lin X, Zhang Y, Zhang W, Zhou J (2021) Efficient hop-constrained s-t simple path enumeration. VLDB J (2)

  34. Ahmadi E, Nascimento MA (2017) Datasets of roads, public transportation and points-of-interest in Amsterdam, Berlin and Oslo. https://sites.google.com/ualberta.ca/nascimentodatasets/

Download references

Acknowledgement

This article is funded by the Program of Docking Research between Supercomputing and Big Data Management Services in Geospatial of the Department of Natural Resources of Hunan Province.

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Authors and Affiliations

  1. College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, China

    Zining Zhang, Shenghong Yang, Yunchuan Qin, Zhibang Yang, Yang Huang & Xu Zhou

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  1. Zining Zhang
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  2. Shenghong Yang
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  3. Yunchuan Qin
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  4. Zhibang Yang
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  5. Yang Huang
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  6. Xu Zhou
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Contributions

All authors contributed to the research concept and design. ZZ is responsible for the drafting of the experiment and paper, SY is responsible for the overall architecture and design of the paper, ZY is responsible for the experimental scheme, YH puts forward the optimization of the algorithm, and XZ puts forward ideas and data collection.

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Correspondence to Shenghong Yang.

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Zhang, Z., Yang, S., Qin, Y. et al. Most relevant point query on road networks. Neural Comput & Applic 37, 7473–7483 (2025). https://doi.org/10.1007/s00521-022-07485-x

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  • DOI: https://doi.org/10.1007/s00521-022-07485-x

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