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Temporal Similarity of Trajectories in Graphs

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Book cover Similarity Search and Applications (SISAP 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12440))

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Abstract

The analysis of similar trajectories in a network provides useful information for different applications. In this study, we are interested in algorithms to efficiently retrieve similar trajectories. Many studies have focused on retrieving similar trajectories by extracting the geometrical and geographical information of trajectories. We provide a similarity function by making use of both the temporal aspect of trajectories and the structure of the underlying network. We propose an approximation technique offering the top-k similar trajectories with respect to a query in a specified time interval in an efficient way. We also investigate how our idea can be applied to similar behavior of the tourists, so as to offer a high-quality prediction of their next movements.

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Notes

  1. 1.

    https://snap.stanford.edu/data/ego-Facebook.html.

  2. 2.

    https://sobigdata.d4science.org/catalogue-sobigdata?path=/dataset/gps_track_milan_italy.

References

  1. Boukhechba, M., Bouzouane, A., Gaboury, S., Gouin-Vallerand, C., Giroux, S., Bouchard, B.: Prediction of next destinations from irregular patterns. J. Ambient Intell. Hum. Comput. 9, 1345–1357 (2017)

    Article  Google Scholar 

  2. De Almeida, V.T., Güting, R.H.: Indexing the trajectories of moving objects in networks. GeoInformatica 9(1), 33–60 (2005)

    Article  Google Scholar 

  3. Grossi, R., Marino, A., Moghtasedi, S.: Finding structurally and temporally similar trajectories in graphs. In: 18th International Symposium on Experimental Algorithms (SEA 2020). Schloss Dagstuhl-Leibniz-Zentrum für Informatik (2020)

    Google Scholar 

  4. Lucchese, C., Perego, R., Silvestri, F., Vahabi, H., Venturini, R.: How random walks can help tourism. In: Baeza-Yates, R., et al. (eds.) ECIR 2012. LNCS, vol. 7224, pp. 195–206. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-28997-2_17

    Chapter  Google Scholar 

  5. Luo, W., Tan, H., Chen, L., Ni, L.M.: Finding time period-based most frequent path in big trajectory data. In: Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data, pp. 713–724. ACM (2013)

    Google Scholar 

  6. Moghtasedi, S.: Time-based similar trajectories on graphs. In: ICTCS, pp. 82–86 (2018)

    Google Scholar 

  7. Monreale, A., Pinelli, F., Trasarti, R., Giannotti, F.: WhereNext: a location predictor on trajectory pattern mining. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 637–646. ACM (2009)

    Google Scholar 

  8. Muntean, C.I., Nardini, F.M., Silvestri, F., Baraglia, R.: On learning prediction models for tourists paths. ACM Trans. Intell. Syst. Technol. (TIST) 7(1), 8 (2015)

    Google Scholar 

  9. Popa, I.S., Zeitouni, K., Oria, V., Barth, D., Vial, S.: PariNet: a tunable access method for in-network trajectories. In: 2010 IEEE 26th International Conference on Data Engineering (ICDE 2010), pp. 177–188. IEEE (2010)

    Google Scholar 

  10. Shang, S., Chen, L., Wei, Z., Jensen, C.S., Zheng, K., Kalnis, P.: Trajectory similarity join in spatial networks. Proc. VLDB Endow. 10(11), 1178–1189 (2017)

    Article  Google Scholar 

  11. Shang, S., Ding, R., Yuan, B., Xie, K., Zheng, K., Kalnis, P.: User oriented trajectory search for trip recommendation. In: Proceedings of the 15th International Conference on Extending Database Technology, pp. 156–167 (2012)

    Google Scholar 

  12. Shang, S., Ding, R., Zheng, K., Jensen, C.S., Kalnis, P., Zhou, X.: Personalized trajectory matching in spatial networks. VLDB J. 23(3), 449–468 (2013). https://doi.org/10.1007/s00778-013-0331-0

    Article  Google Scholar 

  13. Shima, M., Cristina Ioana, M., Franco Maria, N., Roberto, G., Andrea, M.: High-quality prediction of tourist movements using temporal trajectories in graphs (under submission)

    Google Scholar 

  14. Tiakas, E., Papadopoulos, A.N., Nanopoulos, A., Manolopoulos, Y., Stojanovic, D., Djordjevic-Kajan, S.: Trajectory similarity search in spatial networks, pp. 185–192. IEEE (2006)

    Google Scholar 

  15. Tiakas, E., Rafailidis, D.: Scalable trajectory similarity search based on locations in spatial networks. In: Bellatreche, L., Manolopoulos, Y. (eds.) MEDI 2015. LNCS, vol. 9344, pp. 213–224. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-23781-7_17

    Chapter  Google Scholar 

  16. Ying, H., et al.: Time-aware metric embedding with asymmetric projection for successive poi recommendation. World Wide Web 22(5), 2209–2224 (2019)

    Article  Google Scholar 

  17. Zhu, J., Jiang, W., Liu, A., Liu, G., Zhao, L.: Effective and efficient trajectory outlier detection based on time-dependent popular route. World Wide Web 20(1), 111–134 (2016). https://doi.org/10.1007/s11280-016-0400-6

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Dipartimento di Informatica, Università di Pisa, Pisa, Italy

    Shima Moghtasedi

Authors
  1. Shima Moghtasedi
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Corresponding author

Correspondence to Shima Moghtasedi .

Editor information

Editors and Affiliations

  1. National Institute of Informatics, Tokyo, Japan

    Shin'ichi Satoh

  2. ISTI-CNR, Pisa, Italy

    Lucia Vadicamo

  3. University of Southern Denmark, Odense M, Denmark

    Arthur Zimek

  4. ISTI-CNR, Pisa, Italy

    Fabio Carrara

  5. University of Bologna, Bologna, Italy

    Ilaria Bartolini

  6. IT University of Copenhagen, Copenhagen, Denmark

    Martin Aumüller

  7. IT University of Copenhagen, Copenhagen, Denmark

    Björn Þór Jónsson

  8. IT University of Copenhagen, Copenhagen, Denmark

    Rasmus Pagh

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Moghtasedi, S. (2020). Temporal Similarity of Trajectories in Graphs. In: Satoh, S., et al. Similarity Search and Applications. SISAP 2020. Lecture Notes in Computer Science(), vol 12440. Springer, Cham. https://doi.org/10.1007/978-3-030-60936-8_32

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  • DOI: https://doi.org/10.1007/978-3-030-60936-8_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60935-1

  • Online ISBN: 978-3-030-60936-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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