PIST: An Efficient and Practical Indexing Technique for Historical Spatio-Temporal Point Data
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Despite pressing need, current relational database management systems (RDBMS) support for spatio-temporal data is limited and inadequate, and most existing spatio-temporal indices cannot be readily integrated into existing RDBMSs. This paper proposes a practical index for spatio-temporal (PIST) data, an indexing technique, rather than a new indexing structure, for historical spatio-temporal data points that can be fully integrated within existing RDBMSs. PIST separates the spatial and temporal components of the data. For the spatial component, we develop a formal cost model and a partitioning strategy that leads to an optimal space partitioning for uniformly distributed data and an efficient heuristic partitioning for arbitrary data distributions. For the temporal component of the data a B + -tree is used. We show that this layer’s performance can be maximized if an optimal maximal temporal range is enforced, and we present a procedure to determine such an optimal value. Being fully mapped onto a RDBMS, desirable and important properties, such as concurrency control, are immediately inherited by PIST. Using ORACLE as our implementation platform we perform extensive experiments with both real and synthetic datasets comparing its performance against other RDBMS-based options, as well as the MV3R-tree. PIST outperforms the former by at least one order of magnitude, and is competitive or better with respect to the latter, with the unarguable advantage that it can readily used on top of virtually any existing RDBMS.
Keywordsrelational database management system spatio-temporal data rdbms indexing
- 1.M. Abdelguerfi et al. “The 2-3TR-tree, a trajectory-oriented index structure for fully evolving valid-time spatio-temporal datasets,” in Proc. of ACM GIS, pp. 29–34, 2002.Google Scholar
- 2.M.J. Carey et al. “Shoring up persistent applications,” in Proc. of the ACM SIGMOD Conf., pp. 383–394, 1994.Google Scholar
- 3.V.P. Chakka et al. “Indexing large trajectory data sets with SETI ,” in Online Proc. of CIDR, 2003. http://www-db.cs.wisc.edu/cidr/cidr2003/program/p15.pdf
- 4.A. Guttman. “R-trees: a dynamic index structure for spatial searching,” in Proc. of the ACM SIGMOD Conf., pp. 47–57, 1984.Google Scholar
- 5.C.S. Jensen et al. “The INFATI data,” Technical Report TR-79, TimeCenter, 2004. http://arxiv.org/abs/cs.DB/0410001.
- 6.C.S. Jensen, D. Lin, and B.-C. Ooi. “Query and update efficient B + -Tree based indexing of moving objects,” in Proc. of VLDB, pp. 768–779, 2004.Google Scholar
- 7.R.V. Kothuri and S. Ravada. “Spatio-temporal indexing in oracle: issues and challenges,” IEEE TCDE Bulletin, Vol. 25(2):56–60, 2002.Google Scholar
- 8.H.-P. Kriegel, M. Pötke, and T. Seidl. “Managing intervals efficiently in object-relational databases,” in Proc. of VLDB, pp. 407–418, 2000.Google Scholar
- 9.P.M. Lewis, A.B., and M. Kifer. Database and transaction processing. Addison-Wesley, 2002.Google Scholar
- 10.D. Mallett. “Relational database support for spatio-temporal data,” Technical Report TR04-21 (M.Sc. Thesis), Dept. of Computing Science, Univ. of Alberta, 2004. http://www.cs.ualberta.ca/TechReports/2004/TR04-21/TR04-21.pdf.
- 11.M.F. Mokbel, T.M. Ghanem, and W.G. Aref. “Spatio-temporal access methods,” IEEE TCDE Bulletin, Vol. 26(2):40–49, 2003.Google Scholar
- 12.M.A. Nascimento and J.R.O. Silva. “Towards historical R-trees,” in Proc. ACM SAC, pp. 235–240, 1998.Google Scholar
- 13.D. Pfoser, C.S. Jensen, and Y. Theodoridis. “Novel approaches in query processing for moving object trajectories,” in Proc. of VLDB, pp. 395–406, 2000.Google Scholar
- 14.S.M. Ross. Introductory statistics. McGraw-Hill, 1996.Google Scholar
- 15.S. Saltenis et al. “Indexing the positions of continuously moving objects,” in Proc. of the ACM SIGMOD Conf., pp. 331–342, 2000.Google Scholar
- 17.Y. Tao and D. Papadias. “MV3R-Tree: a spatio-temporal access method for timestamp and interval queries,” in Proc. of VLDB, pp. 431–440, 2001.Google Scholar
- 18.Y. Tao, D. Papadias, and J. Sun. “The TPR*-Tree: an optimized spatio-temporal access method for predictive queries,” in Proc. of VLDB, pp. 790–801, 2003.Google Scholar
- 19.Y. Theodoridis and T. Sellis. “A model for the prediction of R-tree rerformance,” in Proc. of PODS, pp. 161–171, 1996.Google Scholar
- 20.Y. Theodoridis, J. R. O. Silva, and M. A. Nascimento. “On the generation of spatiotemporal datasets,” in Proc. of SSD, pp. 147–164, 1999.Google Scholar
- 21.Y. Theodoridis, M. Vazirgiannis, and T.K. Sellis. “Spatio-temporal indexing for large multimedia applications,” in Proc. of IEEE ICMCS, pp. 441–448, 1996.Google Scholar