Approximating the Fréchet Distance When Only One Curve Is c-Packed

Authors Joachim Gudmundsson , Tiancheng Mai , Sampson Wong



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Author Details

Joachim Gudmundsson
  • School of Computer Science, University of Sydney, Australia
Tiancheng Mai
  • School of Computer Science, University of Sydney, Australia
Sampson Wong
  • Department of Computer Science, University of Copenhagen, Denmark

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Joachim Gudmundsson, Tiancheng Mai, and Sampson Wong. Approximating the Fréchet Distance When Only One Curve Is c-Packed. In 35th International Symposium on Algorithms and Computation (ISAAC 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 322, pp. 37:1-37:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024) https://doi.org/10.4230/LIPIcs.ISAAC.2024.37

Abstract

One approach to studying the Fréchet distance is to consider curves that satisfy realistic assumptions. By now, the most popular realistic assumption for curves is c-packedness. Existing algorithms for computing the Fréchet distance between c-packed curves require both curves to be c-packed. In this paper, we only require one of the two curves to be c-packed. Our result is a nearly-linear time algorithm that (1+ε)-approximates the Fréchet distance between a c-packed curve and a general curve in ℝ^d, for constant values of ε, d and c.

Subject Classification

ACM Subject Classification
  • Theory of computation → Computational geometry
Keywords
  • Fréchet distance
  • c-packed curve
  • approximation algorithm

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References

  1. Helmut Alt and Michael Godau. Computing the Fréchet distance between two polygonal curves. International Journal of Computational Geometry & Applications, 05:75-91, 1995. URL: https://doi.org/10.1142/s0218195995000064.
  2. Karl Bringmann. Why walking the dog takes time: Fréchet distance has no strongly subquadratic algorithms unless SETH fails. In Proceedings of the IEEE 55th Annual Symposium on Foundations of Computer Science. IEEE, 2014. URL: https://doi.org/10.1109/focs.2014.76.
  3. Karl Bringmann and Marvin Künnemann. Improved approximation for Fréchet distance on c-packed curves matching conditional lower bounds. International Journal of Computational Geometry & Applications, 27:85-119, 2017. URL: https://doi.org/10.1142/s0218195917600056.
  4. Frederik Brüning, Jacobus Conradi, and Anne Driemel. Faster approximate covering of subcurves under the Fréchet distance. In Proceedings of the 30th Annual European Symposium on Algorithms, ESA 2022, volume 244 of LIPIcs, pages 28:1-28:16. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2022. URL: https://doi.org/10.4230/LIPICS.ESA.2022.28.
  5. Kevin Buchin, Maike Buchin, Joachim Gudmundsson, Aleksandr Popov, and Sampson Wong. Map-matching queries under Fréchet distance on low-density spanners. In Proceedings of the 40th International Symposium on Computational Geometry, SoCG 2024, volume 293 of LIPIcs, pages 27:1-27:15. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2024. URL: https://doi.org/10.4230/LIPICS.SOCG.2024.27.
  6. Kevin Buchin, Maike Buchin, Wouter Meulemans, and Wolfgang Mulzer. Four Soviets walk the dog: Improved bounds for computing the Fréchet distance. Discrete & Computational Geometry, 58(1):180-216, 2017. URL: https://doi.org/10.1007/s00454-017-9878-7.
  7. Kevin Buchin, Tim Ophelders, and Bettina Speckmann. SETH says: Weak Fréchet distance is faster, but only if it is continuous and in one dimension. In Proceedings of the 30th Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2019, pages 2887-2901. SIAM, 2019. URL: https://doi.org/10.1137/1.9781611975482.179.
  8. Daniel Chen, Anne Driemel, Leonidas J. Guibas, Andy Nguyen, and Carola Wenk. Approximate map matching with respect to the Fréchet distance. In Proceedings of the 13th Workshop on Algorithm Engineering and Experiments, ALENEX 2011, pages 75-83. SIAM, 2011. URL: https://doi.org/10.1137/1.9781611972917.8.
  9. Jacobus Conradi, Anne Driemel, and Benedikt Kolbe. (1+ε)-ANN data structure for curves via subspaces of bounded doubling dimension. Comput. Geom. Topol., 3(2):6:1-6:22, 2024. URL: https://www.cgt-journal.org/index.php/cgt/article/view/45.
  10. Jacobus Conradi, Anne Driemel, and Benedikt Kolbe. Revisiting the Fréchet distance between piecewise smooth curves. CoRR, abs/2401.03339, 2024. URL: https://doi.org/10.48550/arXiv.2401.03339.
  11. Anne Driemel and Sariel Har-Peled. Jaywalking your dog: Computing the Fréchet distance with shortcuts. SIAM Journal on Computing, 42(5):1830-1866, 2013. URL: https://doi.org/10.1137/120865112.
  12. Anne Driemel, Sariel Har-Peled, and Carola Wenk. Approximating the Fréchet distance for realistic curves in near linear time. Discrete & Computational Geometry, 48(1):94-127, 2012. URL: https://doi.org/10.1007/s00454-012-9402-z.
  13. M. Maurice Fréchet. Sur quelques points du calcul fonctionnel. Rendiconti del Circolo Matematico di Palermo, 22(1):1-72, 1906. URL: https://doi.org/10.1007/bf03018603.
  14. Joachim Gudmundsson, Zijin Huang, André van Renssen, and Sampson Wong. Computing a subtrajectory cluster from c-packed trajectories. In Proceedings of the 34th International Symposium on Algorithms and Computation, ISAAC 2023, volume 283 of LIPIcs, pages 34:1-34:15. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2023. URL: https://doi.org/10.4230/LIPICS.ISAAC.2023.34.
  15. Joachim Gudmundsson, Michael Mai, and Sampson Wong. Approximating the Fréchet distance when only one curve is c-packed. CoRR, abs/2407.05114, 2024. URL: https://doi.org/10.48550/arXiv.2407.05114.
  16. Joachim Gudmundsson, Martin P. Seybold, and Sampson Wong. Map matching queries on realistic input graphs under the Fréchet distance. ACM Trans. Algorithms, 20(2):14, 2024. URL: https://doi.org/10.1145/3643683.
  17. Joachim Gudmundsson, Yuan Sha, and Sampson Wong. Approximating the packedness of polygonal curves. Comput. Geom., 108:101920, 2023. URL: https://doi.org/10.1016/J.COMGEO.2022.101920.
  18. Joachim Gudmundsson and Michiel H. M. Smid. Fast algorithms for approximate Fréchet matching queries in geometric trees. Comput. Geom., 48(6):479-494, 2015. URL: https://doi.org/10.1016/J.COMGEO.2015.02.003.
  19. Sariel Har-Peled and Benjamin Raichel. The Fréchet distance revisited and extended. ACM Trans. Algorithms, 10(1):3:1-3:22, 2014. URL: https://doi.org/10.1145/2532646.
  20. Sariel Har-Peled and Timothy Zhou. How packed is it, really? CoRR, abs/2105.10776, 2021. URL: https://arxiv.org/abs/2105.10776.
  21. Minghui Jiang, Ying Xu, and Binhai Zhu. Protein structure-structure alignment with discrete Fréchet distance. Journal of Bioinformatics and Computational Biology, 06(01):51-64, 2008. URL: https://doi.org/10.1142/s0219720008003278.
  22. Richard J. Kenefic. Track clustering using Fréchet distance and minimum description length. Journal of Aerospace Information Systems, 11(8):512-524, 2014. URL: https://doi.org/10.2514/1.i010170.
  23. Patrick Laube. Computational Movement Analysis. Springer International Publishing, 2014. URL: https://doi.org/10.1007/978-3-319-10268-9.
  24. Peter Ranacher and Katerina Tzavella. How to compare movement? A review of physical movement similarity measures in geographic information science and beyond. Cartography and Geographic Information Science, 41(3):286-307, 2014. URL: https://doi.org/10.1080/15230406.2014.890071.
  25. Otfried Schwarzkopf and Jules Vleugels. Range searching in low-density environments. Information Processing Letters, 60(3):121-127, 1996. URL: https://doi.org/10.1016/s0020-0190(96)00154-8.
  26. Kevin Toohey and Matt Duckham. Trajectory similarity measures. SIGSPATIAL Special, 7(1):43-50, 2015. URL: https://doi.org/10.1145/2782759.2782767.
  27. Ivor van der Hoog, Eva Rotenberg, and Sampson Wong. Data structures for approximate discrete Fréchet distance. CoRR, abs/2212.07124, 2022. URL: https://doi.org/10.48550/arXiv.2212.07124.
  28. Haozhou Wang, Han Su, Kai Zheng, Shazia Sadiq, and Xiaofang Zhou. An effectiveness study on trajectory similarity measures. In Proceedings of the 24th Australasian Database Conference - Volume 137, ADC '13, pages 13-22. Australian Computer Society, Inc., 2013. Google Scholar
  29. Tim Wylie and Binhai Zhu. Protein chain pair simplification under the discrete Fréchet distance. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 10(6):1372-1383, 2013. URL: https://doi.org/10.1109/tcbb.2013.17.
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