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Indexing Animated Objects Using Spatiotemporal Access Methods
September/October 2001 (vol. 13 no. 5)
pp. 758-777

Abstract—We present a new approach for indexing animated objects and efficiently answering queries about their position in time and space. In particular, we consider an animated movie as a spatiotemporal evolution. A movie is viewed as an ordered sequence of frames, where each frame is a 2D space occupied by the objects that appear in that frame. The queries of interest are range queries of the form, “find the objects that appear in area $S$ between frames $f_i$ and $f_j$” as well as nearest neighbor queries such as, “find the $q$ nearest objects to a given position $A$ between frames $f_i$ and $f_j$.” The straightforward approach to index such objects considers the frame sequence as another dimension and uses a 3D access method (such as, an R-Tree or its variants). This, however, assigns long “lifetime” intervals to objects that appear through many consecutive frames. Long intervals are difficult to cluster efficiently in a 3D index. Instead, we propose to reduce the problem to a partial-persistence problem. Namely, we use a 2D access method that is made partially persistent. We show that this approach leads to faster query performance while still using storage proportional to the total number of changes in the frame evolution. What differentiates this problem from traditional temporal indexing approaches is that objects are allowed to move and/or change their extent continuously between frames. We present novel methods to approximate such object evolutions. We formulate an optimization problem for which we provide an optimal solution for the case where objects move linearly. Finally, we present an extensive experimental study of the proposed methods. While we concentrate on animated movies, our approach is general and can be applied to other spatiotemporal applications as well.

[1] S. Adali, K.S. Candan, S.-S. Chen, K. Erol, and V.S. Subrahmanian, “Advanced Video Information Systems,” ACM Multimedia Systems J., vol. 4, pp. 172-186, 1996.
[2] P.K. Agarwal, L. Arge, and J. Erickson, “Indexing Moving Points,” Proc. 2000 ACM SIGACT-SIGMOD-SIGART Symp. Principles of Database Systems (PODS), May 2000.
[3] L. Arge, “The Buffer Tree: A New Technique for Optimal I/O Algorithms,” Proc. Workshop Algorithms and Data Structures, LNCS 955, pp. 334-345, 1995.
[4] L. Arge, “External-Memory Algorithms with Applications in Geographic Information Systems,” Algorithmic Foundations of Geographic Information Systems, LNCS 1340, 1997.
[5] B. Becker, S. Gschwind, T. Ohler, B. Seeger, and P. Widmayer, "An Asymptotically Optimal Multiversion B-Tree," Very Large Data Bases J., vol. 5, no. 4, pp. 264-275, 1996.
[6] N. Beckmann, H.-P. Kriegel, R. Schneider, and B. Seeger, “The R*-Tree: An Efficient and Robust Access Method for Points and Rectangles,” Proc. ACM SIGMOD Conf. Management of Data, 1990.
[7] J.L. Bentley, “Algorithms for Klee's Rectangle Problems,” technical report, Computer Science Department, Carnegie-Mellon Univ., Pittsburgh, Penn. 1977.
[8] S.-F. Chang, W. Chen, H.E. Meng, H. Sundaram, and D. Zong, "VideoQ: An Automated Content Based Video Search System Using Visual Cues," Proc. ACM Multimedia, pp. 313-324,Seattle, 1994.
[9] S.-F. Chang, W. Chen, H. Meng, H. Sundaram, and D. Zhong, A Fully Automated Content-Based Video Search Engine Supporting Spatiotemporal Queries IEEE Trans. Circuits and Systems for Video Technology, vol. 8, no. 5, pp. 602-615, 1998.
[10] K.L. Cheung and A. Wai-Chee Fu, “Enhanced Nearest Neighbor Search on the R-Tree,” SIGMOD Record, vol. 27, no. 3, pp. 16-21, 1998.
[11] T.H. Cormen,C.E. Leiserson, and R.L. Rivest,Introduction to Algorithms.Cambridge, Mass.: MIT Press/McGraw-Hill, 1990.
[12] S. Dagtas, W. Al-Khatib, A. Ghafoor, and A. Khokhar, “Trail-Based Approach for Video Data Indexing and Retrieval,” Proc. IEEE Int'l Conf. Multimedia Computing and Systems, pp. 235-239, 1999.
[13] L.G. Clark, L.I. Perlovsky, W.H, Schoendorf, C.P. Plum, and T.L. Keller, "Evaluation of FLIR Sensors for Automatic Target Recognition Using an Information-Theoretic Approach," IEEE Proc. NAECON, vol. 2, pp. 1,048-1,055, 1993.
[14] J. Driscoll, N. Sarnak, D. Sleator, and R.E. Tarjan, “Making Data Structures Persistent,” Proc. 18h Ann. ACM Symp. Theory of Computing, 1986.
[15] M. Erwig, R.H. Goting, M. Schneider, and M. Vazirgiannis, “Spatio-Temporal Data Types: An Approach to Modeling and Querying Moving Objects in Databases,” GeoInformatica, vol. 3, no. 3, 1999.
[16] C. Faloutsos, R. Barber, M. Flicker, J. Hafner, W. Niblack, and W. Equitz, "Efficient and effective querying by image content," J. Intell. Information Systems," vol. 3, pp. 231-262, 1994.
[17] A. Guttman, “R-Trees: A Dynamic Index Structure for Spatial Searching,” Proc. ACM SIGMOD Conf. Management of Data, 1984.
[18] A. Hamrapur, A. Gupta, B. Horowitz, C.F. Shu, C. Fuller, J. Bach, M. Gorkani, and R. Jain, “Virage Video Engine,” Proc. SPIE, pp. 188-197, 1997.
[19] J. Hellerstein, E. Koutsoupias, and C. Papadimitriou, “On the Analysis of Indexing Schemes,” Proc. Principles of Database Systems (PODS '97), pp. 249–256, May 1997.
[20] H. Jiang and A. Elmagarmid, “Spatial and Temporal Content-Based Access to Hypervideo Databases,” Very Large Database J., vol. 7, no. 4, pp. 226-238, 1998.
[21] C.S. Jensen and R.T. Snodgrass, “Temporal Data Management,” IEEE Trans. Knowledge and Data Eng., vol. 11, no. 1, pp. 36–45, 1999.
[22] I. Kamel and C. Faloutsos, "Hilbert R-Tree: An Improved R-Tree using Fractals," Proc. Int'l Conf. Very Large Data Bases, 1994.
[23] G. Kollios, D. Gunopulos, and V.J. Tsotras, “On Indexing Mobile Objects,” Proc. 18th ACM SIGACT-SIGMOD-SIGART Symp. Principles of Database Systems, 1999.
[24] C. Kolovson and M. Stonebraker, "Segment Indexes: Dynamic Indexing Techniques for Multi-Dimensional Interval Data," Proc. ACM SIGMOD Conf., pp. 138-147, 1991.
[25] A. Kumar, V.J. Tsotras, and C. Faloutsos, "Designing Access Methods for Bitemporal Databases," IEEE Trans. Knowledge and Data Eng., vol. 10, no. 1, pp. 1-20, 1998.
[26] S.T. Leutenegger, M.A. Lopez, and J.M. Edgington, “Str: A Simple and Efficient Algorithm for R-Tree Packing,” Proc. Int'l Conf. Data Eng. (ICDE '97), pp. 497–506, Apr. 1997.
[27] J.Z. Li, I. Goralwalla, M.T. Ozsu, and D. Szafron, “Modeling Video Temporal Relationship in an Object Database Management System,” IS&T/SPIE Int'l Symp. Electronic Imaging: Multimedia Computing and Networking, pp. 80-91, 1997.
[28] D. Lomet and B. Salzberg, "Access Methods for Multiversion Data," Proc. ACM SIGMOD Conf., pp. 315-324, 1989.
[29] M. Nascimento and J. Silva, “Towards Historical R-Trees,” Proc. ACM Symp. Applied Computing, pp. 235-240, 1998.
[30] M. Nascimento, J. Silva, Y. Theodoridis, “Evaluation of Access Structures for Discretely Moving Points,” Proc. Spatiotemporial Database Management, (STDBM '99), LCNS 1678, pp. 171-188, 1999.
[31] J.A. Orenstein, “Redundancy in Spatial Databases,” Proc. ACM SIGMOD Conf., pp. 326-336, 1986.
[32] J.A. Orenstein, "A Comparison of Spatial Query Processing Techniques for Native and Parameter Spaces," Proc. SIGMOD Int'l Conf. Management Data, pp. 343-352, ACM, 1990.
[33] D. Pfoser, C.S. Jensen, and Y. Theodoridis, “Novel Approaches in Query Processing for Moving Objects,” Proc. 26th Int'l Conf. Very Large Databases (VLDB), Sept. 2000.
[34] S.V. Raghavan and S.K. Tripathy, Networked Multimedia Systems. Prentice-Hall, 1998.
[35] N. Roussopoulos, S. Kelley, and F. Vincent, “Nearest Neighbor Queries,” Proc. ACM SIGMOD Int'l Conf. Management of Data, pp. 71-79, 1995.
[36] S. Saltenis and C. Jensen, “R-Tree Based Indexing of General Spatio-Temporal Data,” Technical Report, TR-45, Time Center, 1999.
[37] S. Saltenis, C.S. Jensen, S.T. Leutenegger, and M.A. Lopez, “Indexing the Positions of Continuously Moving Objects,” Proc. ACM SIGMOD Int'l Conf. Management of Data, pp. 331-342, May 2000.
[38] B. Salzberg and V.J. Tsotras, "A Comparison of Access Methods for Time-Evolving Data," ACM Computing Surveys, to appear; also available as Technical Report No. TR-18, TimeCenter, Aalborg Univ., 1997: .
[39] H. Samet, The Design and Analysis of Spatial Data Structures. Addison-Wesley, 1990.
[40] T. Sellis, N. Roussopoulos, and C. Faloutsos, “The R+-Tree: A Dynamic Index for Multidimensional Objects,” Proc. 13th Int'l Conf. Very Large Data Bases (VLDB), 1987.
[41] J.R. Smith and S.F. Chang, “VisualSEEk: A Fully Automated Content-Based Image Query System,” ACM Multimedia '96, Nov. 1996.
[42] A.P. Sistla, C. Yu, and R. Venkatasubrahmanian, "Similarity Based Retrieval of Videos," Proc. IEEE Data Eng. Conf., 1997.
[43] V.S. Subrahmanian, Principles of Multimedia Database Systems. Morgan Kaufmann, 1998.
[44] H. Sundaram, S.F. Chang, “Efficient Video Sequence Retrieval in Large Repositories,” Proc. SPIE Storage and Retrieval for Image and Video Databases, 1999.
[45] Y. Theodoridis and T. Sellis, “A Model for the Prediction of R-tree Performance,” Proc. 15th ACM Symp. Principles of Database Systems (PODS), 1996.
[46] Y. Theodoridis, T. Sellis, A.N. Papadopoulos, and Y. Manolopoulos, Specifications for Efficient Indexing in Spatiotemporal Databases Proc. 11th Int'l Conf. Scientific and Statistical Database Management, 1999.
[47] Y. Theodoridis, J. Silva, and M. Nascimento, “On the Generation of Spatiotemporal Datasets,” Proc. Symp. Large Spatial Databases (SSD), pp. 147-164, 1999.
[48] V.J. Tsotras and N. Kangelaris, "The Snapshot Index: An I/O-Optimal Access Method for Timeslice Queries," Information Systems, vol. 20, no. 3, pp. 237-260, 1995.
[49] T. Tzouramanis, M. Vassilakopoulos, and Y. Manolopoulos, “Overlapping Linear Quadtrees: A Spatio-Temporal Access Method,” Proc. ACM-Georgaphical Information Systems, pp. 1-7, 1998.
[50] M. Vazirgiannis, Y. Theodoridis, and T. Sellis, “Spatio-Temporal Composition and Indexing for Large Multimedia Applications,” ACM/Springer Multimedia Systems, vol. 6, no. 4, pp. 284-298, July 1998.
[51] P.J. Varman and R.M. Verma, "An Efficient Multiversion Access Structure," IEEE Trans. Knowledge and Data Eng., vol. 9, no. 3, pp. 391-409, May/June 1997.
[52] X. Xu, J. Han, and W. Lu, “RT-Tree: An Improved R-Tree Index Structure for Spatiotemporal Databases,” Proc. Int'l Symp. Spatial Data Handling (SDH), 1990.

Index Terms:
Access methods, spatiotemporal databases, animated objects, multimedia.
Citation:
George Kollios, Vassilis J. Tsotras, Dimitrios Gunopulos, Alex Delis, Marios Hadjieleftheriou, "Indexing Animated Objects Using Spatiotemporal Access Methods," IEEE Transactions on Knowledge and Data Engineering, vol. 13, no. 5, pp. 758-777, Sept.-Oct. 2001, doi:10.1109/69.956099
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