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Visual Fingerprinting: A New Visual Mining Approach for Large-Scale Spatio-temporal Evolving Data

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Advanced Data Mining and Applications (ADMA 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7713))

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Abstract

Spatio-temporal data analysis has important applications in transportation management, urban planning and other fields. However, spatio-temporal data are often highly dimensional, overly large and contain spatial and temporal attributes, which pose special challenges for analysts. In this paper, we propose a new visual aided mining approach, Visual Fingerprinting (VF) for extremely large-scale spatio-temporal feature extraction and analysis. It adopts a visual analytics approach for spatio-temporal data analysis that can generate fingerprints for temporal exploration while preserving the spatial distribution in a region or on a road. Fingerprinting has been proposed to display temporal changes in spatial distributions; for example fingerprints for a region grid can well display temporal changes in the traffic situations of significant spots of a city. These fingerprints integrate important statistical and historical information related to traffic and can be conveniently embedded into urban maps. The sophisticated design of the visualization can better reveal frequent or periodic patterns for temporal attributes. We have tested our approach with real-life vehicle data collected from thousands of taxis and some interesting findings about traffic patterns have been obtained. The experiments validate our methods and demonstrate that our approach can be used for analyzing vehicle trajectories on road networks.

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References

  1. Alvares, L.O., Fern, V.B.J.A., Macedo, E.D., Moelans, B., Spaccapietra, S.: Dynamic modeling of trajectory patterns using data mining and reverse engineering. In: 26th International Conference on Conceptual Modeling (2007)

    Google Scholar 

  2. Andrienko, G., Andrienko, N.: Spatio-temporal aggregation for visual analysis of movements. In: IEEE Symposium on Visual Analytics Science and Technology, VAST 2008, pp. 51–58 ( October 2008)

    Google Scholar 

  3. Andrienko, G., Andrienko, N.: A general framework for using aggregation in visual exploration of movement data. The Cartographic Journal 47(1), 22–40 (2010)

    Article  Google Scholar 

  4. Andrienko, G., Andrienko, N., Bremm, S., Schreck, T., Von Landesberger, T., Bak, P., Keim, D.A.: Space-in-time and time-in-space self-organizing maps for exploring spatiotemporal patterns. Computer Graphics Forum 29(3), 913–922 (2010)

    Article  Google Scholar 

  5. Andrienko, G., Andrienko, N., Dykes, J., Fabrikant, S.I., Wachowicz, M.: Geovisualization of dynamics, movement and change: key issues and developing approaches in visualization research. Information Visualization 7(3-4), 173–180 (2008)

    Article  Google Scholar 

  6. Auria, M.D., Nanni, M., Pedreschi, D.: Time-focused density-based clustering of trajectories of moving objects. Journal of Intelligent Information Systems 27(3), 267–289 (2006)

    Article  Google Scholar 

  7. Compieta, P., Di Martino, S., Bertolotto, M., Ferrucci, F., Kechadi, T.: Exploratory spatio-temporal data mining and visualization. Journal of Visual Languages Computing 18(3), 255–279 (2007)

    Article  Google Scholar 

  8. Crnovrsanin, T., Muelder, C., Correa, C., Ma, K.-L.: Proximity-based visualization of movement trace data, pp. 11–18 (2009)

    Google Scholar 

  9. Giannotti, F., Nanni, M., Pinelli, F., Pedreschi, D.: Trajectory pattern mining. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2007, pp. 330–339. ACM (2007)

    Google Scholar 

  10. Guo, H., Wang, Z.: Tripvista: Triple perspective visual trajectory analytics and its application on microscopic traffic data at a road intersection. In: IEEE Symposium on Pacific Visualization, PacificVis 2010 (2010)

    Google Scholar 

  11. Hurter, C., Tissoires, B., Conversy, S.: FromDaDy: spreading aircraft trajectories across views to support iterative queries. IEEE Transactions on Visualization and Computer Graphics 15(6), 1017–1024 (2009)

    Article  Google Scholar 

  12. Kapler, T., Wright, W.: GeoTime Information Visualization. Information Visualization, 1–8 (2004)

    Google Scholar 

  13. Liu, S., Liu, C., Luo, Q., Ni, L.M., Qu, H.: A visual analytics system for metropolitan transportation. In: Proceedings of the 19th SIGSPATIAL International Conference on Advances in Geographic Information Systems (GIS 2011), pp. 477–480 (2011)

    Google Scholar 

  14. Liu, S., Liu, C., Luo, Q., Ni, L.M., Ramayya, K.: Calibrating large scale vehicle trajectory data. In: Proceedings of the 13th IEEE International Conference on Mobile Data Management (IEEE MDM 2012) (July 2012)

    Google Scholar 

  15. Liu, S., Liu, Y., Ni, L.M., Fan, J., Li, M.: Towards mobility-based clustering. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2010, pp. 919–928. ACM (2010)

    Google Scholar 

  16. Liu, S., Luo, Y., Ni, L.M.: Calibration of vehicle trajectory. Technical Report (2010)

    Google Scholar 

  17. Palma, A.T., Bogorny, V., Kuijpers, B., Alvares, L.O.: A clustering-based approach for discovering interesting places in trajectories. In: Proceedings of the 2008 ACM Symposium on Applied Computing, SAC 2008, pp. 863–868. ACM (2008)

    Google Scholar 

  18. Pelekis, N., Kopanakis, I., Marketos, G., Ntoutsi, I., Andrienko, G., Theodoridis, Y.: Similarity search in trajectory databases. In: 14th International Symposium on Temporal Representation and Reasoning, TIME 2007, pp. 129–140 (2007)

    Google Scholar 

  19. Spaccapietra, S., Parent, C., Damiani, M., Demacedo, J., Porto, F., Vangenot, C.: A conceptual view on trajectories. Data & Knowledge Engineering 65(1), 126–146 (2008)

    Article  Google Scholar 

  20. Vlachos, M., Kollios, G., Gunopulos, D.: Discovering similar multidimensional trajectories. In: Proceedings of 18th International Conference on Data Engineering, ICDE 2002, pp. 673–684. IEEE (2002)

    Google Scholar 

  21. Willems, N., Van De Wetering, H., Van Wijk, J.J.: Visualization of vessel movements. Computer Graphics Forum 28(3), 959–966 (2009)

    Article  Google Scholar 

  22. Yuan, J., Zheng, Y., Zhang, C., Xie, W., Xie, X., Sun, G., Huang, Y.: T-drive: driving directions based on taxi trajectories. In: Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems (GIS 2010), pp. 99–108. ACM (2010)

    Google Scholar 

  23. Zhao, J., Forer, P., Harvey, A.S.: Activities, ringmaps and geovisualization of large human movement fields. Information Visualization 7(3-4), 198–209 (2008)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science and Technology, The Hong Kong University of Science and Technology, Hong Kong

    Jiansu Pu, Huamin Qu & Lionel Ni

  2. iLab, Heinz College, Carnegie Mellon University, USA

    Siyuan Liu

Authors
  1. Jiansu Pu
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  2. Siyuan Liu
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  3. Huamin Qu
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  4. Lionel Ni
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Editor information

Editors and Affiliations

  1. School of Computer Science, Fudan University, Handan Road 220, 200433, Shanghai, China

    Shuigeng Zhou

  2. Chinese Academy of Sciences, Academy of Mathematics and Systems Science, Dongguancun East Road 55, 100190, Beijing, China

    Songmao Zhang

  3. Department of Computer Science and Engineering, University of Minnesota, Union Street SE 200, 55455, Minneapolis, MN, USA

    George Karypis

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© 2012 Springer-Verlag Berlin Heidelberg

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Pu, J., Liu, S., Qu, H., Ni, L. (2012). Visual Fingerprinting: A New Visual Mining Approach for Large-Scale Spatio-temporal Evolving Data. In: Zhou, S., Zhang, S., Karypis, G. (eds) Advanced Data Mining and Applications. ADMA 2012. Lecture Notes in Computer Science(), vol 7713. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35527-1_42

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  • DOI: https://doi.org/10.1007/978-3-642-35527-1_42

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35526-4

  • Online ISBN: 978-3-642-35527-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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