Glenn S. Iwerks
Hanan Samet
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Abstract
Cars, aircraft, mobile cell phones, ships, tanks, and mobile robots all have the common property that they are moving objects. A kinematic representation can be used to describe the location of these objects as a function of time. For example, a moving point can be represented by the function p(t) = x→0 + (t - t0)v→, where x→0 is the start location, t0 is the start time, and v→ is its velocity vector. Instead of storing the location of the object at a given time in a database, the coefficients of the function are stored. When an object's behavior changes enough so that the function describing its location is no longer accurate, the function coefficients for the object are updated. Because the location of each object is represented as a function of time, spatial query results can change even when no transactions update the database. We present efficient algorithms to maintain k-nearest neighbor, and spatial join queries in this domain as time advances and updates occur. We assume no previous knowledge of what the updates will be before they occur. We experimentally compare these new algorithms with more straight forward adaptations of previous work to support updates. Experiments are conducted using synthetic uniformly distributed data, and real aircraft flight data. The primary metric of comparison is the number of I/O disk accesses needed to maintain the query results and the supporting data structures.
- Agarwal, P. K., Arge, L., and Erickson, J. 2000. Indexing moving points. In Proceedings of the 19th ACM Symposium on Principles of Database Systems (Dallas, TX). ACM, New York, 175--186.]] Google Scholar
Digital Library
- Agarwal, P. K. and Procopiuc, C. M. 2002. Advances in indexing for mobile objects. IEEE Bull. Tech. Comm. Data Eng. 25, 2, 25--34.]]Google Scholar
- Arge, L., Procopiuc, O., Ramaswamy, S., Suel, T., Vahrenhold, J., and Vitter, J. S. 2000. A unified approach for indexed and non-indexed spatial joins. In Proceedings of the 7th International Conference on Extending Database Technology---EDBT'2000 (Konstanz, Germany). 413--429.]] Google ScholarDigital Library
- Arya, S., Mount, D. M., Netanyahu, N. S., Silverman, R., and Wu, A. Y. 1998. An optimal algorithm for approximate nearest neighbor searching in fixed dimensions. J. ACM 45, 6 (Nov.), 891--923.]] Google ScholarDigital Library
- Basch, J., Guibas, L. J., and Hershberger, J. 1997. Data structures for mobile data. In Proceedings of the 8th Annual ACM-SIAM Symposium on Discrete Algorithms (New Orleans, LA). ACM, New York, 747--756.]] Google ScholarDigital Library
- Benetis, R., Jensen, C., Karciauskas, G., and Saltenis, S. 2002. Nearest neighbor and reverse nearest neighbor queries for moving objects. In Proceedings of the International Database Engineering and Applications Symposium (IDEAS) (Edmonton, Ont., Canada). 44--53.]] Google ScholarDigital Library
- Brinkhoff, T., Kriegel, H. P., and Seeger, B. 1993. Efficient processing of spatial joins using R-trees. In Proceedings of the ACM SIGMOD Conference (Washington, DC). ACM, New York, 237--246.]] Google ScholarDigital Library
- Douglas, D. and Peucker, T. 1973. Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. The Canadian Cartograph. 10, 2, 112--122.]]Google ScholarCross Ref
- Fujimoto, R. M. 1990. Parallel discrete event simulation. Commun. ACM 33, 10 (Oct.), 30--53.]] Google ScholarDigital Library
- Gedik, B., Wu, K., Yu, P., and Liu, L. 2004. Motion adaptive indexing for moving continual queries over moving objects. In Proceedings of the Conference on Information and Knowledge Management (Washington, DC). 427--436.]] Google ScholarDigital Library
- Gupta, A., Mumick, I. S., and Subrahmanian, V. S. 1993. Maintaining views incrementally. In Proceedings of the ACM SIGMOD Conference (Washington, DC). ACM, New York, 157--166.]] Google ScholarDigital Library
- Güting, R. H., Böhlen, M. H., Erwig, M., Jensen, C. S., Lorentzos, N. A., Schneider, M., and Vazirgiannis, M. 2000. A foundation for representing and querying moving objects. ACM Trans. Datab. Syst. (TODS) 25, 1 (Mar.), 1--42.]] Google ScholarDigital Library
- Güting, R. H. and Schneider, M. 2005. Moving Objects Databases. Morgan-Kaufmann, San Francisco, CA.]]Google Scholar
- Guttman, A. 1984. R-trees: a dynamic index structure for spatial searching. In Proceedings of the ACM SIGMOD Conference (Boston, MA). ACM, New York, 47--57.]] Google ScholarDigital Library
- Hellerstein, J. M., Naughton, J. F., and Pfeffer, A. 1995. Generalized search trees for database systems. In Proceedings of the 21st International Conference on Very Large Data Bases (Zurich, Switzerland). 562--573.]] Google ScholarDigital Library
- Hjaltason, G. R. and Samet, H. 1998. Incremental distance join algorithms for spatial databases. In Proceedings of the ACM SIGMOD Conference (Seattle, WA). ACM, New York, 237--248.]] Google ScholarDigital Library
- Hjaltason, G. R. and Samet, H. 1999. Distance browsing in spatial databases. ACM Trans. Datab. Syst. 24, 2 (June), 265--318. (Also University of Maryland Computer Science TR--3919).]] Google ScholarDigital Library
- Iwerks, G. S., Samet, H., and Smith, K. 2003. Continuous k-nearest neighbor queries for continuously moving points with updates. In Proceedings of the 29th International Conference on Very Large Data Bases (Berlin, Germany). 512--523.]]Google Scholar
- Iwerks, G. S., Samet, H., and Smith, K. 2004. Maintenance of spatial semijoin queries on moving points. In Proceedings of the 30th International Conference on Very Large Data Bases (Toronto, Ont., Canada).]]Google Scholar
- Jensen, C. S. and Saltenis, S. 2002. Towards increasingly update efficient moving-object indexing. IEEE Bull. Tech. Comm. Data Eng. 25, 2 (June), 35--40.]]Google Scholar
- Kalashnikov, D. V., Prabhakar, S., and Hambrusch, S. E. 2004. Main memory evaluation of monitoring queries over moving objects. Distrib. Para. Datab. 15, 2 (Mar.), 117--135.]] Google ScholarDigital Library
- Lema, J. A. C., Forlizzi, L., Güting, R. H., Nardelli, E., and Schneider, M. 2003. Algorithms for moving objects databases. Comput. J. 46, 6, 680--712.]]Google ScholarCross Ref
- Lo, M.-L. and Ravishankar, C. V. 1996. Spatial hash-joins. In Proceedings of the ACM SIGMOD Conference (Montréal, Ont., Canada). ACM, New York, 247--258.]] Google ScholarDigital Library
- Mokbel, M. F., Ghanem, T. M., and Aref, W. G. 2003. Spatio-temporal access methods. IEEE Bull. Tech. Comm. Data Eng. 26, 2 (June), 40--49.]]Google Scholar
- Mokbel, M. F., Xiong, X., and Aref, W. G. 2004. SINA: Scalable incremental processing of continuous queries in spatio-temporal databases. In Proceedings of the ACM SIGMOD Conference (Paris, France). ACM, New York.]] Google ScholarDigital Library
- Mokhtar, H., Su, J., and Ibarra, O. 2002. On moving object queries. In Proceedings of the 21st ACM Symposium on Principles of Database Systems (Madison, WI). ACM, New York, 188--198.]] Google ScholarDigital Library
- Nakamura, Y. and Yamane, K. 2000. Dynamics computation of structure-varying kinematic chains and its application to human figures. IEEE Trans. Rob. Automat. 16, 2 (Apr.), 124--134.]]Google ScholarCross Ref
- Nascimento, M. A., Silva, R., and Theodoridis, Y. 1999. Evaluation of access structures for discretely moving points. In Proceedings of the International Workshop on Spatio-Temporal Database Management (Edinburgh, UK), 171--188.]] Google ScholarDigital Library
- Özsoyoǧlu, G. and Snodgrass, R. T. 1995. Temporal and real-time databases: A survey. IEEE Trans. Knowl. Data Eng. 7, 4 (Aug.), 513--532.]] Google ScholarDigital Library
- Patel, J. M. and DeWitt, D. J. 1996. Partition based spatial-merge join. In Proceedings of the ACM SIGMOD Conference (Montréal, Ont., Canada). ACM, New York, 259--270.]] Google ScholarDigital Library
- Pfoser, D. 2002. Indexing the trajectories of moving objects. IEEE Bull. Tech. Comm. Data Eng 25, 2 (June), 3--9.]]Google Scholar
- Pfoser, D. and Jensen, C. S. 1999. Capturing the uncertainty of moving-object representations. In Proceedings of the Advances in Spatial Databases---Sixth International Symposium, SSD'99 (Hong Kong). Lecture Notes in Computer Science, vol 1651. Springer-Verlag, New York, 111--132.]] Google ScholarDigital Library
- Procopiuc, C. M., Agarwal, P. K., and Har, S.Peled. 2002. Star-tree: An efficient self-adjusting index for moving objects. In Proceedings of the Revised Papers from the 4th International Workshop on Algorithm Engineering and Experiments (San Francicsco, CA). 178--193.]] Google ScholarDigital Library
- Raptopoulou, K., Papadopoulos, A. N., and Manolopoulos, Y. 2003. Fast nearest-neighbor query processing in moving-object databases. GeoInformatica 7, 2, 113--137.]] Google ScholarDigital Library
- Roussopoulos, N., Kelley, S., and Vincent, F. 1995. Nearest neighbor queries. In Proceedings of the ACM SIGMOD Conference (San Jose, CA). ACM, New York, 71--79.]] Google ScholarDigital Library
- Saltenis, S., Jensen, C. S., Leutenegger, S. T., and Lopez, M. A. 2000. Indexing the positions of continuously moving objects. In Proceedings of the ACM SIGMOD Conference (Dallas, TX). ACM, New York, 331--342.]] Google ScholarDigital Library
- Samet, H. 1990. The Design and Analysis of Spatial Data Structures. Addison-Wesley, Reading, MA.]] Google ScholarDigital Library
- SCIS. 1996. IEEE Std. 1278 1-1995. IEEE Computer Society, Standards Committee on Interactive Simulation, USA.]]Google Scholar
- Sistla, A. P., Wolfson, O., Chamberlain, S., and Dao, S. 1997. Modeling and querying moving objects. In Proceedings of the 13th IEEE Conference on Data Engineering (ICDE) (Birmingham, UK). IEEE Computer Society Press, Los Alamitos, CA, 422--432.]] Google ScholarDigital Library
- Song, Z. and Roussopoulos, N. 2001. K-nearest neighbor search for moving query point. In Proceedings of the Advances in Spatial and Temporal Databases (SSTD) (Redondo Beach, CA). Lecture Notes in Computer Science, vol. 2121. Springer-Verlag, New York, 79--96.]] Google ScholarDigital Library
- Tao, Y. and Papadias, D. 2003. Spatial queries in dynamic environments. ACM Trans. Datab. Syst. (TODS) 28, 2 (June), 101--139.]] Google ScholarDigital Library
- Tao, Y., Papadias, D., and Sun, J. 2003a. The tpr*-tree: An optimized spatio-temporal access method for predictive queries. In Proceedings of the 29th International Conference on Very Large Data Bases (Berlin, Germany). 790--801.]]Google Scholar
- Tao, Y., Sun, J., and Papadias, D. 2003b. Selectivity estimation for predictive spatio-temporal queries. In Proceedings of 19th IEEE International Conference on Data Engineering (ICDE) (Bangalore, India). IEEE Computer Society Press, Los Alamitos, CA, 417--428.]]Google Scholar
- Tayeb, J., Ulusoy, Ö., and Wolfson, O. 1998. A quadtree-based dynamic attribute indexing method. Comput. J. 41, 3, 185--200.]]Google ScholarCross Ref
- Trajcevski, G., Scheuermann, P., Wolfson, O., and Nedungadi, N. 2004a. CAT: Correct answers of continuous queries using triggers. In Proceedings of the International Conference on Extending Database Technology (EDBT) (Heraklion, Greece). 837--840.]]Google Scholar
- Trajcevski, G., Wolfson, O., Hinrichs, K., and Chamberlain, S. 2004b. Managing uncertainty in moving objects databases. ACM Trans. Datab. Syst. (TODS) 29, 3 (Sept.), 463--507.]] Google ScholarDigital Library
- Wolfson, O., Chamberlain, S., Dao, S., Jiang, L., and Mendez, G. 1998. Cost and imprecision in modeling the position of moving objects. In Proceedings of the 14th IEEE International Conference on Data Engineering (ICDE). 588--596.]] Google ScholarDigital Library
- Xiong, X., Mokbel, M. F., and Aref, W. G. 2005. SEA-CNN: Scalable processing of continuous k-nearest neighbor queries in spatio-temporal databases. In Proceedings of the International Conference of Data Engineering (Tokyo, Japan).]] Google ScholarDigital Library
- Xiong, X., Mokbel, M. F., Aref, W. G., Hambrusch, S. E., and Prabhakar, S. 2004. Scalable spatio-temporal continuous query processing for location-aware services. In Proceedings of the International Conference on Scientific and Statistical Database (Santorini Island, Greece).]] Google ScholarCross Ref
Index Terms
- Maintenance of K-nn and spatial join queries on continuously moving points
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