Knowledge-Enriched Route Computation

  • Georgios Skoumas
  • Klaus Arthur Schmid
  • Gregor Jossé
  • Matthias Schubert
  • Mario A. Nascimento
  • Andreas Züfle
  • Matthias Renz
  • Dieter Pfoser
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9239)

Abstract

Directions and paths, as commonly provided by navigation systems, are usually derived considering absolute metrics, e.g., finding the shortest or the fastest path within an underlying road network. With the aid of Volunteered Geographic Information (VGI), i.e., geo-spatial information contained in user generated content, we aim at obtaining paths that do not only minimize distance but also lead through more popular areas. Based on the importance of landmarks in Geographic Information Science and in human cognition, we extract a certain kind of VGI, namely spatial relations that define closeness (nearby, next to) between pairs of points of interest (POIs), and quantify them following a probabilistic framework. Subsequently, using Bayesian inference we obtain a crowd-based closeness confidence score between pairs of POIs. We apply this measure to the corresponding road network based on an altered cost function which does not exclusively rely on distance but also takes crowdsourced geo-spatial information into account. Finally, we propose two routing algorithms on the enriched road network. To evaluate our approach, we use Flickr photo data as a ground truth for popularity. Our experimental results – based on real world datasets – show that the paths computed w.r.t. our alternative cost function yield competitive solutions in terms of path length while also providing more “popular” paths, making routing easier and more informative for the user.

References

  1. 1.
    Richter, K.F., Winter, S.: Cognitive aspects: how people perceive, memorize, think and talk about landmarks. In: Landmarks, pp. 41–108. Springer International Publishing, Cham (2014)Google Scholar
  2. 2.
    Skoumas, G., Schmid, K.A., Jossé, G., Züfle, A., Nascimento, M.A., Renz, M., Pfoser, D.: Towards knowledge-enriched path computation. In: Proceedings of the 22nd ACM International Conference on Advances in Geographic Information Systems, 485–488 (2014)Google Scholar
  3. 3.
    Skoumas, G., Pfoser, D., Kyrillidis, A.: On quantifying qualitative geospatial data: a probabilistic approach. In: Proceedings of the Second ACM International Workshop on Crowdsourced and Volunteered Geographic Information, pp. 71–78 (2013)Google Scholar
  4. 4.
    Skoumas, G., Pfoser, D., Kyrillidis, A.T.: Location estimation using crowdsourced geospatial narratives. In: CoRR abs/1408.5894 (2014)Google Scholar
  5. 5.
    Loper, E., Bird, S.: NLTK: The natural language toolkit. In: Proceedings of the ACL 2002 Workshop on Effective Tools and Methodologies for Teaching Natural Language Processing and Computational Linguistics , vol. 1, pp. 63–70 (2002)Google Scholar
  6. 6.
    Bishop, C.M.: Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag New York Inc, Secaucus (2006)Google Scholar
  7. 7.
    Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the em algorithm. J. Roy. Stat. Soc. Ser. B 39, 1–38 (1977)MATHMathSciNetGoogle Scholar
  8. 8.
    Borzsony, S., Kossmann, D., Stocker, K.: The skyline operator. In: Proceedings of the 17th International Conference on Data Engineering, pp. 421–430 (2001)Google Scholar
  9. 9.
    Shekelyan, M., Jossé, G., Schubert, M.: Paretoprep: fast computation of path skylines queries. In: CoRR abs/1410.0205 (2014)Google Scholar
  10. 10.
    Graf, F., Kriegel, H.-P., Renz, M., Schubert, M.: MARiO: multi-attribute routing in open street map. In: Pfoser, D., Tao, Y., Mouratidis, K., Nascimento, M.A., Mokbel, M., Shekhar, S., Huang, Y. (eds.) SSTD 2011. LNCS, vol. 6849, pp. 486–490. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  11. 11.
    Mousselly-Sergieh, H., Watzinger, D., Huber, B., Döller, M., Egyed-Zsigmond, E., Kosch, H.: World-wide scale geotagged image dataset for automatic image annotation and reverse geotagging. In: Proceedings of the 5th ACM Multimedia Systems Conference, pp. 47–52 (2014)Google Scholar
  12. 12.
    Sacharidis, D., Bouros, P.: Routing directions: keeping it fast and simple. In: Proceedings of the 21st ACM International Conference on Advances in Geographic Information Systems, pp. 164–173 (2013)Google Scholar
  13. 13.
    Westphal, M., Renz, J.: Evaluating and minimizing ambiguities in qualitative route instructions. In: Proceedings of the 19th ACM International Conference on Advances in Geographic Information Systems, pp. 171–180 (2011)Google Scholar
  14. 14.
    Lv, M., Chen, L., Chen, G.: Discovering personally semantic places from GPS trajectories. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, pp. 1552–1556 (2012)Google Scholar
  15. 15.
    Yan, Z., Chakraborty, D., Parent, C., Spaccapietra, S., Aberer, K.: Semitri: a framework for semantic annotation of heterogeneous trajectories. In: Proceedings of the 14th International Conference on Extending Database Technology, pp. 259–270 (2011)Google Scholar
  16. 16.
    Palma, A.T., Bogorny, V., Kuijpers, B., Alvares, L.O.: A clustering-based approach for discovering interesting places in trajectories. In: Proceedings of the ACM Symposium on Applied Computing, pp. 863–868 (2008)Google Scholar
  17. 17.
    Alvares, L.O., Bogorny, V., Kuijpers, B., de Macedo, J.A.F., Moelans, B., Vaisman, A.: A model for enriching trajectories with semantic geographical information. In: Proceedings of the 15th Annual ACM International Symposium on Advances in Geographic Information Systems, pp. 22:1–22:8 (2007)Google Scholar
  18. 18.
    Parent, C., Spaccapietra, S., Renso, C., Andrienko, G., Andrienko, N., Bogorny, V., Damiani, M.L., Gkoulalas-Divanis, A., Macedo, J., Pelekis, N., Theodoridis, Y., Yan, Z.: Semantic trajectories modeling and analysis. ACM Comput. Surv. 45, 42:1–42:32 (2013)CrossRefGoogle Scholar
  19. 19.
    Spaccapietra, S., Parent, C.: Adding meaning to your steps. In: Proceedings of the 30th International Conference on Conceptual Modeling, pp. 13–31 (2011)Google Scholar
  20. 20.
    Yan, Z., Chakraborty, D., Parent, C., Spaccapietra, S., Aberer, K.: Semantic trajectories: mobility data computation and annotation. ACM Trans. Intell. Syst. Technol. 4, 49:1–49:38 (2013)CrossRefGoogle Scholar
  21. 21.
    Yan, Z., Spremic, L., Chakraborty, D., Parent, C., Spaccapietra, S., Aberer, K.: Automatic construction and multi-level visualization of semantic trajectories. In: Proceedings of the 18th International Conference on Advances in Geographic Information Systems, pp. 524–525 (2010)Google Scholar
  22. 22.
    Feldman, D., Sugaya, A., Sung, C., Rus, D.: iDiary: from GPS signals to a text-searchable diary. In: Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems, pp. 6:1–6:12 (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Georgios Skoumas
    • 1
  • Klaus Arthur Schmid
    • 2
  • Gregor Jossé
    • 2
  • Matthias Schubert
    • 2
  • Mario A. Nascimento
    • 3
  • Andreas Züfle
    • 2
  • Matthias Renz
    • 2
  • Dieter Pfoser
    • 4
  1. 1.National Technical University of AthensAthensGreece
  2. 2.Ludwig-Maximilians-Universität MünchenMunichGermany
  3. 3.University of AlbertaEdmontonCanada
  4. 4.George Mason UniversityFairfaxUSA

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