Abstract
The uncertainty management problem is one of the key issues associated with moving objects (MOs). Minimizing the uncertainty region size can increase both query accuracy and system performance. In this paper, we propose an uncertainty model called the Truncated Tornado model as a significant advance in minimizing uncertainty region sizes. The Truncated Tornado model removes uncertainty region sub-areas that are unreachable due to the maximum velocity and acceleration of the MOs. To make indexing of the uncertainty regions more tractable we utilize an approximation technique called Tilted Minimum Bounding Box (TMBB) approximation. Through experimental evaluations we show that Truncated Tornado in TMBB results in orders of magnitude reduction in volume compared to a recently proposed model called the Tornado model and to the standard “Cone” model when approximated by axis-parallel MBB.
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Alkobaisi, S., Bae, W.D., Kim, S.H., Yu, B.: MBR models for uncertainty regions of moving objects. In: Haritsa, J.R., Kotagiri, R., Pudi, V. (eds.) DASFAA 2008. LNCS, vol. 4947, pp. 126–140. Springer, Heidelberg (2008)
Barequet, G., Har-Peled, S.: Efficiently approximating the minimum-volume bounding box of a point set in three dimensions. Journal of Algorithms 38(1), 91–109 (2001)
Brinkoff, T., Kriegel, H.-P., Schneider, R.: Comparison of approximations of complex objects used for approximation-based query processing in spatial database systems. In: Proceedings of Int. Conf. on Data Engineering, pp. 40–49 (1993)
Gottschalk, S., Lin, M.C., Manocha, D.: OBB-tree: A hierarchical structure for rapid interference detection. In: Proceedings of ACM Siggraph, pp. 171–180 (1996)
Hornsby, K., Egenhofer, M.J.: Modeling moving objects over multiple granularities. Annals of Mathematics and Artificial Intelligence 36(1-2), 177–194 (2002)
O’Rourke, J.: Finding minimal enclosing boxes. International Journal of Parallel Programming 14(3), 183–199 (1985)
Pfoser, D., Jensen, C.S.: Capturing the uncertainty of moving-objects representations. In: Proceedings of Int. Symposium on Advances in Spatial Databases, pp. 111–132 (1999)
Theodoridis, Y., Silva, J.R.O., Nascimento, M.A.: On the generation of spatiotemporal datasets. In: Proceedings of Int. Symposium on Advances in Spatial Databases, pp. 147–164 (1999)
Toussaint, G.T.: Solving geometric problems with the rotating calipers. In: Proceedings of IEEE MELECON, pp. A10.02/1–4(1983)
Trajcevski, G., Wolfson, O., Hinrichs, K., Chamberlain, S.: Managing uncertainty in moving objects databases. ACM Trans. on Databases Systems 29(3), 463–507 (2004)
Yu, B.: A spatiotemporal uncertainty model of degree 1.5 for continuously changing data objects. In: Proceedings of ACM Int. Symposium on Applied Computing, Mobile Computing and Applications, pp. 1150–1155 (2006)
Yu, B., Kim, S.H., Alkobaisi, S., Bae, W.D., Bailey, T.: The Tornado model: Uncertainty model for continuously changing data. In: Kotagiri, R., Radha Krishna, P., Mohania, M., Nantajeewarawat, E. (eds.) DASFAA 2007. LNCS, vol. 4443, pp. 624–636. Springer, Heidelberg (2007)
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Alkobaisi, S., Vojtěchovský, P., Bae, W.D., Kim, S.H., Leutenegger, S.T. (2008). The Truncated Tornado in TMBB: A Spatiotemporal Uncertainty Model for Moving Objects. In: Bhowmick, S.S., Küng, J., Wagner, R. (eds) Database and Expert Systems Applications. DEXA 2008. Lecture Notes in Computer Science, vol 5181. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85654-2_4
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DOI: https://doi.org/10.1007/978-3-540-85654-2_4
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