Advertisement

Spatiotemporal Access Methods

  • Yannis Manolopoulos
  • Yannis Theodoridis
  • Vassilis J. Tsotras
Part of the Advances in Database Systems book series (ADBS, volume 17)

Abstract

Spatiotemporal Database Management Systems (STDBMSs) manage data whose geometry changes over time. There are many applications that create such data, including global change (as in climate or land cover changes), transportation (traffic surveillance data, intelligent transportation systems), social (demographic, health, etc.), and multimedia (animated movies) applications. For simplicity we consider two spatial attributes, the object position and extent, either (or both) of which can change with time. Based on the rate that spatial attributes change, we identify two cases: the discrete and the continuous spatiotemporal environments. We first introduce the basic characteristics and interesting queries for each environment and then present efficient spatiotemporal indexing techniques.

Keywords

Access Method Spatial Object Large Data Base Spatiotemporal Data Query Region 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Agarwal, P.K. and Erickson, J. (1998). Geometric Range Searching and its Relatives: Advances in Discrete and Computational Geometry. In Contemporary Mathematics, by Cha-zelle, B., Goodman, J.E., and Pollack, R. (eds), Vol.223, pages 1–56. American Mathematical Society.Google Scholar
  2. Agarwal, P.K., Arge, L., Erickson, J., Franciosa, P., and Vitter, J.S. (1998). Efficient Searching with Linear Constraints. In Proceedings of ACM SIGMOD Conference on Management of Data, pages 169–178.Google Scholar
  3. Agouris, P., Stefanidis, A., and Carswell, J. (1998). Digital Image Retrieval Using Shape-based Queries. In Proceedings of the International Conference on Spatial Data Handling, pages 613–625.Google Scholar
  4. Agrawal, R., Lin, K.-L, Sawhney, H.S., and Shim, K. (1995). Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases. In Proceedings of the 21 st International Conference on Very Large Data Bases, pages 490–501.Google Scholar
  5. Arge, L. and Vitter, J.S. (1996). Optimal Dynamic Interval Management in External Memory. In Proceedings of the 37 th IEEE Symposium on Foundations of Computer Science, pages 560–569.Google Scholar
  6. Basch, J., Guibas, L., and Hershberger, J. (1997). Data Structures for Mobile Data. In Proceedings of the 8 th A CM-SIAM Symposium on Discrete Algorithms, pages 747–756.Google Scholar
  7. Becker, B., Gschwind, S., Ohler, T., Seeger, B., and Widmayer, P. (1996). An Asymptotically Optimal Multiversion B-tree. The VLDB Journal, 5(4):264–275.CrossRefGoogle Scholar
  8. Beckmann, N., Kriegel, H.-P., Schneider, R., and Seeger, B. (1990). The R*-tree: an Efficient and Robust Access Method for Points and Rectangles. In Proceedings of ACM SIGMOD Conference on Management of Data, pages 322–331.Google Scholar
  9. Berchtold, S., Keim, D.A., and Kriegel, H-P. (1996). The X-tree: an Index Structure for High-Dimensional Data. In Proceedings of the 22 nd Conference on Very Large Data Bases, pages 28–39.Google Scholar
  10. Bertino, E., Catania, B., and Shidlovsky, B. (1997). Towards Optimal Two-dimensional Indexing for Constraint Databases. Information Processing Letters, 64(1):1–8.MathSciNetCrossRefGoogle Scholar
  11. Chazelle, B. and Rosenberg, B. (1992). Lower Bounds on the Complexity of Simplex Range Reporting on a Pointer Machine. In Proceedings of the 19 th International Colloquium on Automata, Languages and Programming, pages 439–449.Google Scholar
  12. Chomicki, J. and Revesz, P. (1997). Constraint-based Interoperability of Spatiotemporal Databases. In Proceedings of the 5 th International Symposium on Spatial Databases, pages 142–161.Google Scholar
  13. Chomicki, J. and Revesz, P. (1999). A Geometric Framework for Specifying Spatiotemporal Objects. In Proceedings of the 6 th International Workshop on Time Representation and Reasoning, pages 41–46.Google Scholar
  14. Comer, D. (1979). The Ubiquitous B-tree. ACM Computing Surveys, 11(2):121–137.zbMATHCrossRefGoogle Scholar
  15. Das, G., Gunopulos, D., and Mannila, H. (1997). Finding Similar Time Series. In Proceedings of the 1 st European Symposium on Principles of Data Mining and Knowledge Discovery, pages 88–100.Google Scholar
  16. Delis, A., Kanitkar, V., and Park, J.H. (1999). Client-Server Computing. In Encyclopedia of Distributed Computing, by Urban, J. and Dasgupta, P. (eds.). Kluwer Academic Publishers.Google Scholar
  17. Devillers, O., Golin, M., Kedem, K., and Schirra, S. (1994). Revenge of the Dog: Queries on Voronoi Diagrams of Moving Points. In Proceedings of the 6 th Canadian Conference on Computational Geometry, pages 122–127.Google Scholar
  18. Driscoll, J.R., Sarnak, N., Sleator, D., and Tarjan, R.E. (1989). Making Data Structures Persistent. Journal of Computer and System Sciences, 38(1):86–124.MathSciNetzbMATHCrossRefGoogle Scholar
  19. Egenhofer, M.J. (1993). What’s Special about Spatial? Database Requirements for Vehicle Navigation in Geographic Space. In Proceedings of ACM SIGMOD Conference on Management of Data, pages 398–402.Google Scholar
  20. Elmasri, R., Wuu, G., and Kim, Y. (1990). The Time Index: an Access Structure for Temporal Data. In Proceedings of the 16 th International Conference on Very Large Data Bases, pages 1–12.Google Scholar
  21. Erwig, M., Gueting, R.H., Schneider, M., and Vazirgiannis, M. (1998). Abstract and Discrete Modeling of Spatio-Temporal Data Types. In Proceedings of the 6 th ACM International Workshop on Advances in Geographic Information Systems, pages 131–136.Google Scholar
  22. Evangelidis, G., Lomet, D., and Salzberg, B. (1995). The hBII-tree: a Modified hB-tree Supporting Concurrency, Recovery and Node Consolidation. In Proceedings of the 21 st International Conference on Very Large Data Bases, pages 551–561.Google Scholar
  23. Faloutsos, C, Ranganathan, M., and Manolopoulos, Y. (1994). Fast Subsequence Matching in Time-Series Databases. In Proceedings of ACM SIGMOD Conference on Management of Data, pages 419–429.Google Scholar
  24. Gaede, V. and Guenther, O. (1998). Multidimensional Access Methods. ACM Computing Surveys, 30(2):170–231.CrossRefGoogle Scholar
  25. Goldstein, J., Ramakrishnan, R., Shaft, U., and Yu, J.B. (1997). Processing Queries By Linear Constraints. In Proceedings of the 16 th ACM Symposium on Principles of Database Systems, pages 257–267.Google Scholar
  26. Gruen, A., Agouris, P., Stallmann, D., and Li, H. (1994). Algorithms for Automated Extraction of Man-Made Objects from Raster Image Data in a GIS. In Proceedings of the 2 nd ACM International Workshop on Advances in Geographic Information Systems, pages 123–132.Google Scholar
  27. Guenther, O. (1989). The Design of the Cell Tree: an Object-Oriented Index Structure for Geometric Databases. In Proceedings of the 5 th IEEE International Conference on Data Engineering, pages 598–605.Google Scholar
  28. Guttman, A. (1984). R-trees: a Dynamic Index Structure for Spatial Searching. In Proceedings of ACM SIGMOD Conference on Management of Data, pages 47–57.Google Scholar
  29. Hellerstein, J.M., Naughton, J., and Pfeffer, A. (1995). Generalized Search Trees for Database Systems. In Proceedings of the 21 st International Conference on Very Large Data Bases, pages 562–573.Google Scholar
  30. Hellerstein, J.M., Koutsoupias, E., and Papadimitriou, C.H. (1997). On the Analysis of Indexing Schemes. In Proceedings of the 16 th ACM Symposium on Principles of Database Systems, pages 249–256.Google Scholar
  31. Henrich, A., Six, H.-W., and Widmayer, P. (1989). The LSD tree: Spatial Access to Multidimensional Point and non Point Objects. In Proceedings of the 15 th International Conference on Very Large Data Bases, pages 45–53.Google Scholar
  32. Imielinski, T. and Badrinath, B.R. (1992). Querying in Highly Mobile Distributed Environments. In Proceedings of the 18 th International Conference on Very Large Data Bases, pages 41–52.Google Scholar
  33. Jagadish, H.V. (1990). On Indexing Line Segments. In Proceedings of the 16 th International Conference on Very Large Data Bases, pages 614–625.Google Scholar
  34. Jensen, C.S. and Snodgrass, R.T. (1999). Temporal Data Management. IEEE Transactions on Knowledge and Data Engineering, 11(1):36–44.CrossRefGoogle Scholar
  35. Jensen, C.S., et al. (1994). A Consensus Glossary of Temporal Database Concepts. ACM SIGMOD Record, 23(1):52–64.CrossRefGoogle Scholar
  36. Kamel, I. and Faloutsos, C. (1993). On Packing R-trees. In Proceedings of the 2 nd International Conference on Information and Knowledge Management, pages 490–499.Google Scholar
  37. Kanellakis, P., Ramaswamy, S., Vengroff, D., and Vitter, J.S. (1993). Indexing for Data Models with Constraint and Classes. In Proceedings of the 12 th ACM Symposium on Principles of Database Systems, pages 233–243.Google Scholar
  38. Kollios, G., Gunopulos, D., and Tsotras, V.J. (1999a). On Indexing Mobile Objects. In Proceedings of the 18 th ACM Symposium on Principles of Database Systems, pages 261–272.Google Scholar
  39. Kollios, G., Gunopulos, D., and Tsotras, V.J. (1999b). Nearest Neighbor Queries in a Mobile Environment. In Proceedings of the International Workshop on Spatio-Temporal Data-base Management, pages 119–134.Google Scholar
  40. Kollios, G., Gunopulos, D., and Tsotras, V.J. (1999c). Indexing Animated Objects. In Proceedings of the 5 th International Workshop on Multimedia Information Systems, to appear.Google Scholar
  41. Kumar, A., Tsotras, V.J., and Faloutsos, C. (1998). Designing Access Methods for Bitemporal Databases. IEEE Transactions on Knowledge and Data Engineering, 10(1):1–20.CrossRefGoogle Scholar
  42. Lakshmanan, L., Leone, N., Ross, R., and Subrahmanian, V.S. (1997). Prob View: A Flexible Probabilistic Database System. ACM Transactions on Database Systems, 22(3):419–469.CrossRefGoogle Scholar
  43. Lanka, S. and Mays, E. (1991). Fully Persistent B+-trees. In Proceedings of ACM SIGMOD Conference on Management of Data, pages 426–435.Google Scholar
  44. Leung, T.Y.C. and Muntz, R.R. (1992). Generalized Data Stream Indexing and Temporal Query Processing. In Proceedings of the 2 nd International Workshop on Research Issues in Data Engineering, pages 124–131.Google Scholar
  45. Lomet, D. and Salzberg, B. (1989). Access Methods for Multiversion Data. In Proceedings of ACM SIGMOD Conference on Management of Data, pages 315–324.Google Scholar
  46. Matousek, J. (1992). Efficient Partition Trees. Discrete Computational Geometry, 8:432–448.MathSciNetCrossRefGoogle Scholar
  47. Matousek, J. (1994). Geometric range searching. ACM Computing Surveys, 26(4):421–461.CrossRefGoogle Scholar
  48. Nascimento, M. and Silva, J.R.O. (1998). Towards Historical R-trees. In Proceedings of ACM Symposium on Applied Computing, pages 235–240.Google Scholar
  49. Nascimento, M., Silva, J.R.O., and Theodoridis, Y. (1999). Evaluation of Access Structures for Discretely Moving Points. In Proceedings of the International Workshop on Spatio-Temporal Database Management, pages 171–188.Google Scholar
  50. Pagel, B.-U., Six, H.-W., Toben, H., and Widmayer, P. (1993). Towards an Analysis of Range Query Performance in Spatial Data Structures. In Proceedings of the 12 th ACM Symposium on Principles of Database Systems, pages 214–221.Google Scholar
  51. Rafiei, D. and Mendelzon, A.O. (1997). Similarity-based Queries for Time Series Data. In Proceedings of ACM SIGMOD Conference on Management of Data, pages 13–25.Google Scholar
  52. Ramaswamy, S. (1997). Efficient Indexing for Constraint and Temporal Databases. In Proceedings of the 6 ( International Conference on Database Theory, pages 419–431.Google Scholar
  53. Roussopoulos, N., Kelley, S., and Vincent, F. (1995). Nearest Neighbor Queries. In Proceedings of ACM SIGMOD Conference on Management of Data, pages 71–79.Google Scholar
  54. Salzberg, B. and Tsotras, V.J. (1999). A Comparison of Access Methods for Time-Evolving Data. A CM Computing Surveys, to appear. Also available as TimeCenter TR-18, http:// www.cs.auc.dk/research/DBS/tdb/TimeCenter/publications2.htmlGoogle Scholar
  55. Samet, H. (1990). The Design and Analysis of Spatial Data Structures. Addison-Wesley.Google Scholar
  56. Sellis, T., Roussopoulos, N., and Faloutsos, C. (1987). The R+-tree: a Dynamic Index for Multidimensional Objects. In Proceedings of the 13 th International Conference on Very Large Data Bases, pages 507–518.Google Scholar
  57. Sistla, A.P., Wolfson, O., Chamberlain, S., and Dao, S. (1997). Modeling and Querying Moving Objects. In Proceedings of the 13 th IEEE International Conference on Data Engineering, pages 422–432.Google Scholar
  58. Stonebraker, M., Frew, J., Gardels, K., and Meredith, J. (1993). The Sequoia 2000 Benchmark. In Proceedings of ACM SIGMOD Conference on Management of Data, pages 2–11.Google Scholar
  59. Su, C.-J., Tassiulas, L., and Tsotras, V.J. (1999). Broadcast Scheduling for Information Distribution. ACM/Baltzer Journal of Wireless Networks, 5(2):137–147.CrossRefGoogle Scholar
  60. Subramanian, S. and Ramaswamy, S. (1995). The P-range Tree: a New Data Structure for Range Searching in Secondary Memory. In Proceedings of the 6 th ACM-SIAM Symposium on Discrete Algorithms, pages 378–387.Google Scholar
  61. Tayeb, J., Ulusoy, O., and Wolfson, O. (1998). A Quadtree-based Dynamic Attribute Indexing Method. The Computer Journal, 41(3):185–200.zbMATHCrossRefGoogle Scholar
  62. Theodoridis, Y., Sellis, T., Papadopoulos, A., Manolopoulos, Y. (1998). Specifications for Efficient Indexing in Spatiotemporal Databases. In Proceedings of the 10 th International Conference on Scientific and Statistical Database Management, pages 123–132.Google Scholar
  63. Tsotras, V.J., Gopinath, B., and Hart, G.W. (1995). Efficient Management of Time-Evolving Databases. IEEE Transactions on Knowledge and Data Engineering, 7(4):591–608.CrossRefGoogle Scholar
  64. Tzouramanis, T., Vassilakopoulos, M., and Manolopoulos, Y. (1998). Overlapping Linear Quadtrees: a Spatio-Temporal Access Method. In Proceedings of the 6 th ACM International Workshop on Advances in Geographic Information Systems, pages 1–7.Google Scholar
  65. Tzouramanis, T., Vassilakopoulos, M., and Manolopoulos, Y. (1999). Processing of Spatio-temporal Queries in Image Databases. In Proceedings of the 3 rd East-European Conference on Advanced Databases and Information Systems, to appear.Google Scholar
  66. Vazirgiannis, M., Theodoridis, Y., and Sellis, T. (1998). Spatio-Temporal Composition and Indexing for Large Multimedia Applications. ACM/Springer Multimedia Systems, 6(4):284–298.CrossRefGoogle Scholar
  67. Wolfson, O., Chamberlain, S., Dao, S., Jiang, L., and Mendez, G. (1998a). Cost and Imprecision in Modeling the Position of Moving Objects. In Proceedings of the 14 th IEEE International Conference on Data Engineering, pages 588–596.Google Scholar
  68. Wolfson, O., Xu, B., Chamberlain, S., and Jiang, L. (1998b). Moving Objects Databases: Issues and Solutions. In Proceedings of the 10 th International Conference on Scientific and Statistical Database Management, pages 111–122.Google Scholar
  69. Worboys, M. (1994). A Unified Model for Spatial and Temporal Information. The Computer Journal, 37(1):26–34.CrossRefGoogle Scholar
  70. Xu, X., Han, J., and Lu, W. (1990). RT-tree: an Improved R-tree Index Structure for Spatio-temporal Databases. In Proceedings of the 4 th International Symposium on Spatial Data Handling, pages 1040–1049.Google Scholar

Copyright information

© Springer Science+Business Media New York 2000

Authors and Affiliations

  • Yannis Manolopoulos
    • 1
  • Yannis Theodoridis
    • 2
  • Vassilis J. Tsotras
    • 3
  1. 1.Aristotle UniversityGreece
  2. 2.Greece
  3. 3.University of CaliforniaRiversideUSA

Personalised recommendations