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Range and kNN Query Processing for Moving Objects in Grid Model

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Abstract

With the growing popularity of mobile computing devices and wireless communications, managing dynamically changing information about moving objects is becoming feasible. In this paper, we implement a system that manages such information and propose two query algorithms: a range query algorithm and a k nearest neighbor algorithm. The range query algorithm is combined with an efficient filtering technique which determines if a polyline corresponding to the trajectory of a moving object intersects with a given range. We study the performance of the system, which shows that despite the filtering step, for moderately large ranges, the range query algorithm we propose outperforms the algorithm without filtering.

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References

  1. N. Beckmann, H.-P. Kriegel, R. Schneider and B. Seeger, The R*-tree: an efficient and robust access method for points and rectangles, in: Proc. ACM SIGMOD Internat. Conf. on Management of Data (1990) pp. 322–331.

  2. K.S. Beyer, J. Goldstein, R. Ramakrishnan and U. Shaft, When is “Nearest Neighbor” meaningful, in: Internat. Conf. on Database Theory (1999) pp. 217–235.

  3. H.D. Chon, D. Agrawal and A. El Abbadi, Storage and retrieval of moving objects, in: Proc. of the Internat. Conf. on Mobile Data Management (2001) pp. 173–184.

  4. H.D. Chon, D. Agrawal and A. El Abbadi, Using space‐time grid for efficient management of moving objects, in: MobiDE (2001) pp. 59–65.

  5. H. Ferhatosmanoglu, E. Tuncel, D. Agrawal and A. El Abbadi, Approximate nearest neighbor searching in multimedia databases, in: Proc. of Internat. Conf. on Data Engineering (2001) pp. 503–511.

  6. S. Handley, P. Langley and F. Rauscher, Learning to predict the duration of an automobile trip, in: Proc. of the Internat. Conf. on Knowledge Discovery and Data Mining (1998) pp. 219–223.

  7. E.G. Hoel and H. Samet, Efficient processing of spatial queries in line segment databases, in: Advances in Spatial Database ‐ 2nd Symposium (1991) pp. 237–256.

  8. P. Hough, Method and means for recognizing complex patterns, U.S. Patent No. 3069654 (1962).

  9. H.V. Jagadish, On indexing line segments, in: Proc. of the Internat. Conf. on Very Large Data Bases (1990) pp. 614–625.

  10. G. Kollios, D. Gunopulos and V.J. Tsotras, On indexing moving objects, in: Proc. of ACM Sympos. on Principles of Database Systems (1999) pp. 261–272.

  11. R.C. Nelson and H. Samet, A consistent hierarchical representation for vector data, in: ACM SIGGRAPH (1986) pp. 197–206.

  12. D. Pfoser, C.S. Jensen and Y. Theodoridis, Novel approaches to the indexing of moving object trajectories, in: Proc. of the Internat. Conf. on Very Large Data Bases (2000) pp. 395–406.

  13. K. Porkaew, I. Lazaridis and S. Mehrotra, Querying mobile objects in spatio-temporal databases, in: Internat. Sympos. on Spatial and Temporal Databases (2001) pp. 59–78.

  14. W. Pugh, Skip lists: a probabilistic alternative to balanced trees, Comm. of ACM 33(6) (1990) 668–676.

    Google Scholar 

  15. N. Roussopoulos, S. Kelley and F. Vincent, Nearest neighbor queries, in: Proc. ACM SIGMOD Internat. Conf. on Management of Data (1995) pp. 71–79.

  16. S. Saltenis, C.S. Jensen, S.T. Leutenegger and M.A. Lopez, Indexing the positions of continuously moving objects, in: Proc. ACM SIGMOD Internat. Conf. on Management of Data (2000) pp. 331–342.

  17. D. Schrank and T. Lomax, The 2001 urban mobility report, Technical Report, Texas Transportation Institute (2001).

  18. A.P. Sistla, O. Wolfson, S. Chamberlain and S. Dao, Modeling and querying moving objects, in: Proc. of the Internat. Conf. on Data Engineering (1997) pp. 422–432.

  19. Z. Song and N. Roussopoulos, k-nearest neighbor search for moving query point, in: Internat. Sympos. on Spatial and Temporal Databases (2001) pp. 79–96.

  20. J. Tayeb, O. Ulusoy and O. Wolfson, A quadtree based dynamic attribute indexing method, The Computer Journal 41(3) (1998) 185–200.

    Google Scholar 

  21. D. White and R. Jain, Similarity indexing with the SS-tree, in: Proc. of the Internat. Conf. on Data Engineering (1996) pp. 516–523.

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Authors and Affiliations

  1. Department of Computer Science, University of California, Santa Barbara, CA, USA

    Hae Don Chon, Divyakant Agrawal & Amr El Abbadi

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  1. Hae Don Chon
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  2. Divyakant Agrawal
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  3. Amr El Abbadi
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Chon, H.D., Agrawal, D. & Abbadi, A.E. Range and kNN Query Processing for Moving Objects in Grid Model. Mobile Networks and Applications 8, 401–412 (2003). https://doi.org/10.1023/A:1024535730539

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