Petko Bakalov
Marios Hadjieleftheriou
ABSTRACT
Efficiently and accurately discovering similarities among moving object trajectories is a difficult problem that appears in many spatiotemporal applications. In this paper we consider how to efficiently evaluate trajectory joins, i.e., how to identify all pairs of similar trajectories between two datasets. Our approach represents an object trajectory as a sequence of symbols (i.e., a string). Based on special lower-bounding distances between two strings, we propose a pruning heuristic for reducing the number of trajectory pairs that need to be examined. Furthermore, we present an indexing scheme designed to support efficient evaluation of string similarities in secondary storage. Through a comprehensive experimental evaluation we present the advantages of the proposed techniques.
- L. Arge, O. Procopiuc, S. Ramaswamy, T. Suel, and J. S. Vitter. Scalable sweeping-based spatial join. In Proc. of Very Large Data Bases (VLDB), pages 570--581, 1998.]] Google Scholar
Digital Library
- T. Brinkhoff, H. P. Kriegel, and B. Seeger. Efficient processing of spatial joins using r-trees. In Proc. of ACM Management of Data (SIGMOD), pages 237--246, 1993.]] Google ScholarDigital Library
- V. P. Chakka, A. Everspaugh, and J. M. Patel. Indexing large trajectory data sets with seti. In Proc. of Biennial Conference on Innovative Data Systems Research (CIDR), 2003.]]Google Scholar
- K. Chakrabarti, E. Keogh, S. Mehrotra, and M. Pazzani. Locally adaptive dimensionality reduction for indexing large time series databases. ACM Transactions on Database Systems (TODS), 27(2):188--228, 2002.]] Google ScholarDigital Library
- L. Chen, M. T. zsu, and V. Oria. Symbolic representation and retrieval of moving object trajectories. In Proc. of the ACM SIGMM international workshop on multimedia information retrieval, pages 227--234, 2004.]] Google ScholarDigital Library
- H. D. Chon, D. Agrawal, and A. El Abbadi. Storage and retrieval of moving objects. In Proc. of the International Conference on Mobile Data Management (MDM), pages 173--184, 2001.]] Google ScholarDigital Library
- H. Gunadhi and A. Segev. Query processing algorithms for temporal intersection joins. In Proc. of International Conference on Data Engineering (ICDE), pages 336--344, 1991.]] Google ScholarDigital Library
- R. H. Güting, M. H. Bhlen, M. Erwig, C. S. Jensen, N. A. Lorentzos, M. Schneider, and M. Vazirgiannis. A foundation for representing and querying moving objects. ACM Transactions on Database Systems (TODS), 25(1):1--42, 2000.]] Google ScholarDigital Library
- M. Hadjieleftheriou. Spatio-temporal generators. http://www.cs.ucr.edu/~marioh/generators/index.html.]]Google Scholar
- M. Hadjieleftheriou, G. Kollios, V. J. Tsotras, and D. Gunopulos. Efficient indexing of spatiotemporal objects. In Proc. of Extending Database Technology (EDBT), pages 251--268, 2002.]] Google ScholarDigital Library
- G. R. Hjaltason and H. Samet. Incremental distance join algorithms for spatial databases. In Proc. of ACM Management of Data (SIGMOD), pages 237--248, 1998.]] Google ScholarDigital Library
- E. Keogh, K. Chakrabarti, S. Mehrotra, and M. Pazzani. Locally adaptive dimensionality reduction for indexing large time series databases. In Proc. of ACM Management of Data (SIGMOD), pages 151--162, 2001.]] Google ScholarDigital Library
- E. J. Keogh and M. J. Pazzani. A simple dimensionality reduction technique for fast similarity search in large time series databases. In Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications, pages 122--133, 2000.]] Google ScholarDigital Library
- G. Kollios, V. J. Tsotras, D. Gunopulos, A. Delis, and M. Hadjieleftheriou. Indexing animated objects using spatiotemporal access methods. IEEE Transactions on Knowledge and Data Engineering (TKDE), 13(5):758--777, 2001.]] Google ScholarDigital Library
- N. Koudas and K. C. Sevcik. Size separation spatial join. In Proc. of ACM Management of Data (SIGMOD), pages 324--335, 1997.]] Google ScholarDigital Library
- J. Lin, E. Keogh, S. Lonardi, and B. Chiu. A symbolic representation of time series, with implications for streaming algorithms. In Proc. of ACM SIGMOD workshop on research issues in data mining and knowledge discovery, pages 2--11, 2003.]] Google ScholarDigital Library
- M.-L. Lo and C. V. Ravishankar. Spatial joins using seeded trees. In Proc. of ACM Management of Data (SIGMOD), pages 209--220, 1994.]] Google ScholarDigital Library
- M.-L. Lo and C. V. Ravishankar. Spatial hash-joins. In Proc. of ACM Management of Data (SIGMOD), pages 247--258, 1996.]] Google ScholarDigital Library
- N. Mamoulis and D. Papadias. Multiway spatial joins. ACM Transactions on Database Systems (TODS), 26(4):424--475, 2001.]] Google ScholarDigital Library
- N. Mamoulis and D. Papadias. Slot index spatial join. IEEE Transactions on Knowledge and Data Engineering (TKDE), 15(1):211--231, 2003.]] Google ScholarDigital Library
- M. Nascimento and J. Silva. Towards historical R-trees. In Proc. of ACM Symposium on Applied Computing (SAC), 1998.]] Google ScholarDigital Library
- D. Papadias, Y. Tao, J. Zhang, N. Mamoulis, Q. Shen, and J. Sun. Indexing and retrieval of historical aggregate information about moving objects. IEEE Data Engineering Bulletin, 25(2), June 2002.]]Google Scholar
- A. Papadopoulos, P. Rigaux, and M. Scholl. A performance evaluation of spatial join processing strategies. In Proc. of Symposium on Advances in Spatial Databases (SSD), pages 286--307, 1999.]] Google ScholarDigital Library
- J. M. Patel and D. J. DeWitt. Partition based spatial-merge join. In Proc. of ACM Management of Data (SIGMOD), pages 259--270, 1996.]] Google ScholarDigital Library
- D. Pfoser, C. S. Jensen, and Y. Theodoridis. Novel approaches in query processing for moving object trajectories. In Proc. of Very Large Data Bases (VLDB), pages 395--406, 2000.]] Google ScholarDigital Library
- J. Shan, D. Zhang, and B. Salzberg. On spatial-range closest-pair query. In Proc. of Symposium on Advances in Spatial and Temporal Databases (SSTD), pages 252--269, 2003.]]Google ScholarCross Ref
- A. P. Sistla, O. Wolfson, S. Chamberlain, and S. Dao. Modeling and querying moving objects. In Proc. of International Conference on Data Engineering (ICDE), pages 422--432, 1997.]] Google ScholarDigital Library
- Y. Tao and D. Papadias. MV3R-Tree: A spatio-temporal access method for timestamp and interval queries. In Proc. of Very Large Data Bases (VLDB), pages 431--440, 2001.]] Google ScholarDigital Library
- M. Vlachos, M. Hadjieleftheriou, D. Gunopulos, and E. Keogh. Indexing multi-dimensional time-series with support for multiple distance measures. In Proc. of ACM Knowledge Discovery and Data Mining (SIGKDD), pages 216--225, 2003.]] Google ScholarDigital Library
- M. Vlachos, G. Kollios, and D. Gunopulos, Discovering similar multidimensional trajectories. In Proc. of International Conference on Data Engineering (ICDE), pages 673--684, 2002.]] Google ScholarDigital Library
- B.-K. Yi and C. Faloutsos. Fast time sequence indexing for arbitrary lp norms. In Proc. of Very Large Data Bases (VLDB), pages 385--394, 2000.]] Google ScholarDigital Library
- D. Zhang, V. J. Tsotras, and D. Gunopulos. Efficient aggregation over objects with extent. In Proc. of ACM Symposium on Principles of Database Systems (PODS), pages 121--132, 2002.]] Google ScholarDigital Library
- H. Zhu, J. Su, and O. H. Ibarra. Trajectory queries and octagons in moving object databases. In Proc. of Conference on Information and Knowledge Management (CIKM), pages 413--421, 2002.]] Google ScholarDigital Library
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- Efficient trajectory joins using symbolic representations
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