Skip to main content

Towards Trajectory Data Warehouses

  • Chapter
Book cover Mobility, Data Mining and Privacy

Data warehouses have received the attention of the database community as a technology for integrating all sorts of transactional data, dispersed within organisations whose applications utilise either legacy (non-relational) or advanced relational database systems. Data warehouses form a technological framework for supporting decision-making processes by providing informational data. A data warehouse is defined as a subject-oriented, integrated, time-variant, non-volatile collection of data in support of management of decision-making process [10].

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. S. Agarwal, R. Agrawal, P. Deshpande, A. Gupta, J. Naughton, R. Ramakrishnan, and S. Sarawagi. On the computation of multidimensional aggregates. In Proceeding on 22th International Conference on Very Large Data Bases (VLDB’96), pp. 506–521, 1996.

    Google Scholar 

  2. Y. Bédard, T. Merrett, and J. Han. Fundamentals of spatial data warehousing for geographic knowledge discovery. In Geographic Data Mining and Knowledge Discovery, pp. 53–73. Taylor & Francis, 2001.

    Google Scholar 

  3. S. Bimonte, A. Tchounikine, and M. Miquel. Towards a spatial multi-dimensional model. In Proceedings of ACM 8th International Workshop on Data Warehousing and OLAP (DOLAP’05), pp. 39–46, 2005.

    Google Scholar 

  4. F. Braz, S. Orlando, R. Orsini, A. Raffaetà, A. Roncato, and C. Silvestri. Approximate aggregations in trajectory data warehouses. In Proceedings of ICDE Workshop on Spatio-Temporal Data Mining, pp. 536–545, 2007.

    Google Scholar 

  5. M.-L. Damiani and S. Spaccapietra. Spatial data warehouse modelling. In Processing and Managing Complex Data for Decision Support, pp. 12–27. Idea Group Publishing, 2006.

    Google Scholar 

  6. P. Flajolet and G. Martin. Probabilistic counting algorithms for data base applications. Journal of Computer and System Sciences, 31(2):182–209, 1985.

    Article  MATH  MathSciNet  Google Scholar 

  7. J. Gray, A. Bosworth, A. Layman, and H. Pirahesh. Data cube: A relational aggregation operator generalizing group-by, cross-tab, and sub-total. In Proceedings of the 12th International Conference on Data Engineering (ICDE’96), pp. 152–159, 1996.

    Google Scholar 

  8. J. Han, N. Stefanovic, and K. Kopersky. Selective materialization: An efficient method for spatial data cube construction. In Proceedings of Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 144–158, 1998.

    Google Scholar 

  9. K. Hornsby and M. Egenhofer. Modeling moving objects over multiple granularities. Annals of Mathematics and Artificial Intelligence, 36(1–2):177–194, 2002.

    Article  MATH  MathSciNet  Google Scholar 

  10. W. Inmon. Building the Data Warehouse, 2nd edn. Wiley, 1996.

    Google Scholar 

  11. C. Jensen, A. Kligys, T. Pedersen, C. Dyreson, and I. Timko. Multidimensional data modeling for location-based services. The Very Large Data Bases Journal, 13:1–21, 2004.

    Article  Google Scholar 

  12. I. Lopez, R. Snodgrass, and B. Moon. Spatiotemporal aggregate computation: A survey. IEEE Transactions o Knowledge Data Engeneering, 2(17):271–286, 2005.

    Article  Google Scholar 

  13. E. Malinowski and E. Zimányi. OLAP hierarchies: A conceptual perspective. In Proceedings of the 16th International Conference on Advanced Information Systems Engineering (CAiSE’04), pp. 477–491, 2004.

    Google Scholar 

  14. E. Malinowski and E. Zimányi. Representing spatiality in a conceptual multidimensional model. In Proceedings of the 12th annual International Workshop on Geographic Information Systems (GIS’04), pp. 12–21, 2004.

    Google Scholar 

  15. E. Malinowski and E. Zimányi. Hierarchies in a multidimensional model: From conceptual modeling to logical representation. Data and Knowledge Engineering, 59(2):348–377, 2006.

    Article  Google Scholar 

  16. OpenGIS Consortium. Abstract Specification, Topic 1: Feature Geometry (ISO 19107 Spatial Schema), 2001. http://www.opengeospatial.org.

  17. D. Papadias, Y. Tao, P. Kalnis, and J. Zhang. Indexing spatio-temporal data warehouses. In Proceedings of the 18th International Conference on Data Engineering (ICDE’02), pp. 166–175, 2002.

    Google Scholar 

  18. T. Pedersen and N. Tryfona. Pre-aggregation in spatial data warehouses. In Proceedings of the 5th International Symposium on Spatial and Temporal Databases (SSTD’01), vol. 2121 of LNCS, pp. 460–480, 2001.

    Google Scholar 

  19. F. Rao, L. Zhang, X. Yu, Y. Li, and Y. Chen. Spatial hierarchy and OLAP-favored search in spatial data warehouse. In Proceedings of ACM 6th International Workshop on Data Warehousing and OLAP (DOLAP’03), pp. 48–55, 2003.

    Google Scholar 

  20. S. Rivest, Y. Bédard, and P. Marchand. Towards better support for spatial decision making: Defining the characteristics of spatial on-line analytical processing (SOLAP). Geomatica, 55(4):539–555, 2001.

    Google Scholar 

  21. S. Rivest, Y. Bédard, M. Proulx, M. Nadeau, F. Hubert, and J. Pastor. SOLAP: Merging business intelligence with geospatial technology for interactive spatio-temporal exploration and analysis of data. Journal of International Society for Photogrammetry & Remote Sensing, 60(1):17–33, 2005.

    Article  Google Scholar 

  22. S. Rizzi. Open problems in data warehousing: Eight years later. In Proceedings of the 5th Workshop on Design and Management of Data Warehouses (DMDW’03), 2003.

    Google Scholar 

  23. S. Rizzi and M. Golfarelli. Date warehouse design. In Proceedings of International Conference on Enterprise Information Systems (ICEIS’00), pp. 39–42, 2000.

    Google Scholar 

  24. S. Shekhar, C. Lu, S. Chawla, and P. Zhang. Data mining and visualization of twin-cities traffic data. Technical Report, University of Minnesota, 2002.

    Google Scholar 

  25. N. Stefanovic, J. Han, and K. Koperski. Object-based selective materialization for efficient implementation of spatial data cubes. IEEE Transactions on Knowledge and Data Engineering, 12(6):938–958, 2000.

    Article  Google Scholar 

  26. Y. Tao, G. Kollios, J. Considine, F. Li, and D. Papadias. Spatio-temporal aggregation using sketches. In Proceedings of the 20th International Conference on Data Engineering (ICDE’04), pp. 214–225, 2004.

    Google Scholar 

  27. Y. Tao and D. Papadias. Historical spatio-temporal aggregation. ACM Transactions on Information Systems, 23:61–102, 2005.

    Article  Google Scholar 

  28. G. Trajcevski, O. Wolfson, K. Hinrichs, and S. Chamberlain. Managing uncertainty in moving objects databases. ACM Transactions on Database System, 29(3):463–507, 2004.

    Article  Google Scholar 

  29. G. Trajcevski, O. Wolfson, F. Zhang, and S. Chamberlain. The geometry of uncertainty in moving objects databases. In Proceedings of 7th International Conference on Extending Database Technology (EDBT’02), pp. 233–250, 2002.

    Google Scholar 

  30. J. Trujillo, M. Palomar, J. Gómez, and I. Song. Designing data warehouses with OO conceptual models. IEEE Computer, Special Issue on Data Warehouses, 34(12):66–75, 2001.

    Google Scholar 

  31. P. Vassiliadis and T. Sellis. A survey of logical models for OLAP databases. SIGMOD Record, 28(4):64–69, 1999.

    Article  Google Scholar 

  32. D. Zhang and V. Tsotras. Optimizing spatial Min/Max aggregations. The Very Large Data Bases Journal, 14:170–181, 2005.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Pelekis, N. et al. (2008). Towards Trajectory Data Warehouses. In: Giannotti, F., Pedreschi, D. (eds) Mobility, Data Mining and Privacy. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75177-9_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-75177-9_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-75176-2

  • Online ISBN: 978-3-540-75177-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics