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Modeling and prediction of moving region trajectories

Published:02 November 2010Publication History

ABSTRACT

Data about moving objects is being collected in many different application domains with the help of sensor networks, GPS-enabled devices, and in particular airborne sensors and satellites. Such moving objects often represent not just point-based objects, but rather moving regions like hurricanes, oil-spills, or animal herds. One key application feature users are often interested in is the exploration and prediction of moving object trajectories. While there exist models and techniques that help to predict the movement of moving point objects, no such method for moving regions has been proposed yet.

In this paper, we present an approach to model and predict the development of moving regions. Our method not only predicts the trajectory of regions, but also the evolution of a region's spatial extent and orientation. For this, moving regions are modelled using minimum enclosing boxes, and evolution patterns of regions are determined using linear regression and a recursive motion function. We demonstrate the functionality and effectiveness of the proposed technique using real-world sensor data from different application domains.

References

  1. J. Chen, X. Meng, Y. Guo, S. Grumbach, and H. Sun. Modeling and predicting future trajectories of moving objects in a constrained network. In Mobile Data Management, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. M. Duckham, S. Nittel, and M. F. Worboys. Monitoring dynamic spatial fields using responsive geosensor networks. In ACM GIS, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. C. Franke and M. Gertz. Detection and exploration of outlier regions in sensor data streams. In International Workshop on Spatial and Spatiotemporal Data Mining, with ICDM 2008, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. H. Freeman and R. Shapira. Determining the minimum-area encasing rectangle for an arbitrary closed curve. Commun. ACM, 18(7), 1975. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. P. Kalnis, N. Mamoulis, and S. Bakiras. On discovering moving clusters in spatio-temporal data. In SSTD, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. R. Nowak and U. Mitra. Boundary estimation in sensor networks: Theory and methods. LNCS: Information Processing in Sensor Networks, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. M. Pelanis, S. Saltenis, and C. S. Jensen. Indexing the past, present, and anticipated future positions of moving objects. ACM Trans. Database Syst., 31(1), 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. R. Praing and M. Schneider. Modeling historical and future movements of spatio-temporal objects in moving objects databases. In CIKM. ACM, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. C. Robertson, T. Nelson, B. Boots, and M. Wulder. STAMP: spatial-temporal analysis of moving polygons. Journal of Geographical Systems, 9(3), 2007.Google ScholarGoogle ScholarCross RefCross Ref
  10. S. Subramaniam, V. Kalogeraki, and T. Palpanas. Distributed real-time detection and tracking of homogeneous regions in sensor networks. In Real-Time Systems Symposium, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Y. Tao, C. Faloutsos, D. Papadias, and B. Liu. Prediction and indexing of moving objects with unknown motion patterns. In SIGMOD, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Y. Tao, D. Papadias, and J. Sun. The TPR*-tree: an optimized spatio-temporal access method for predictive queries. In VLDB, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. W.-G. Teng, M.-S. Chen, and P. S. Yu. A regression-based temporal pattern mining scheme for data streams. In VLDB, pages 93--104, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. E. Tøssebro and R. H. Güting. Creating representations for continuously moving regions from observations. In SSTD, 2001.Google ScholarGoogle ScholarCross RefCross Ref
  15. V. J. Tsotras. Recent advances on querying and managing trajectories. In Tutorial at SSTD, 2007.Google ScholarGoogle Scholar
  16. M. F. Worboys and M. Duckham. Monitoring qualitative spatiotemporal change for geosensor networks. International Journal of Geographical Information Science, 20(10), 2006.Google ScholarGoogle Scholar
  17. W. Xue, Q. Luo, L. C. 0002, and Y. Liu. Contour map matching for event detection in sensor networks. In SIGMOD, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. J. Zhao, C.-T. Lu, and Y. Kou. Detecting region outliers in meteorological data. In ACM GIS, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library

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        • Published in

          cover image ACM Conferences
          IWGS '10: Proceedings of the ACM SIGSPATIAL International Workshop on GeoStreaming
          November 2010
          67 pages
          ISBN:9781450304313
          DOI:10.1145/1878500

          Copyright © 2010 ACM

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          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 2 November 2010

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          Overall Acceptance Rate7of9submissions,78%

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