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Spatio-Temporal Data Warehouses

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Encyclopedia of Database Systems

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Synonyms

Spatio-temporal online analytical processing; Spatio-Temporal OLAP

Definition

ConsiderNregions R 1 , R 2 ,…,R N and a time axis consisting of discrete timestamps 1, 2,…,T, where T represents the total number of recorded timestamps (i.e., the length of history). The position and area of a region R i may vary along with time, and its extent at timestamp t is denoted as R i (t). Each region carries a set of measures R i (t).ms, also called the aggregate data of R i (t). The measures of regions change asynchronously with their extents. In other words, the measure of R i (1 ≤ i ≤ N) may change at a timestamp t (i.e., R i (t).ms ≠ R i (t − 1).ms), while its extent remains the same (i.e., \( {R}_i(t) = {R}_i\left(t-1\right) \)), and vice versa.

A spatio-temporal data warehouse stores the above information, and efficiently answers the spatio-temporal window aggregate query, which specifies an area q R and a time interval q T of continuous timestamps. The goal is to return the...

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Recommended Reading

  1. Baralis E, Paraboschi S, Teniente E. Materialized view selection in a multidimensional database. In: Proceedings of 23th international conference on very large data bases. 1997. p. 156–65.

    Google Scholar 

  2. Gray J, Bosworth A, Layman A, Pirahesh H. Data cube: a relational aggregation operator generalizing group-by, cross-tabs and subtotals. In: Proceedings of 12th international conference on data engineering. 1996. p. 152–9.

    Google Scholar 

  3. Han J, Stefanovic N, Koperski K. Selective materialization: an efficient method for spatial data cube construction. In: Proceedings of Pacific-Asia conference on knowledge discovery and data mining. 1998. p. 144–58.

    Google Scholar 

  4. Harinarayan V, Rajaraman A, Ullman J. Implementing data cubes efficiently. In: Proceedings of ACM SIGMOD international conference on management of data. 1996. p. 205–16.

    Google Scholar 

  5. Jurgens M, Lenz H. The Ra*-tree: an improved R-tree with materialized data for supporting range queries on OLAP-data. In: Proceedings of international workshop on database and expert systems applications. 1998. p. 186–91.

    Google Scholar 

  6. Kimball R. The data warehouse toolkit. New York: Wiley; 1996.

    Google Scholar 

  7. Lopez I, Snodgrass R, Moon B. Spatiotemporal aggregate computation: a survey. IEEE Trans Knowl Data Eng. 2005;17(2):271–86.

    Article  Google Scholar 

  8. Mendelzon A, Vaisman A. Temporal queries in OLAP. In: Proceedings of 26th international conference on very large data bases. 2000. p. 242–53.

    Google Scholar 

  9. Papadias D, Kalnis P, Zhang J, Tao Y. Efficient OLAP operations in spatial data warehouses. In: Proceedings of 7th international symposium, advances in spatial and temporal databases. 2001. p. 443–59.

    Google Scholar 

  10. Stefanovic N, Han J, Koperski K. Object-based selective materialization for efficient implementation of spatial data cubes. IEEE Trans Knowl Data Eng. 2000;12(6):938–58.

    Article  Google Scholar 

  11. Sun J, Papadias D, Tao Y, Liu B. Querying about the past, the present and the future in spatio-temporal databases. In: Proceedings of 20th international conference on data engineering. 2004. p. 202–13.

    Google Scholar 

  12. Tao Y, Papadias D. Range aggregate processing in spatial databases. IEEE Trans Knowl Data Eng. 2004;16(12):1555–70.

    Article  Google Scholar 

  13. Tao Y, Papadias D. Historical spatio-temporal aggregation. ACM Trans Inf Syst. 2005;23(1):61–102.

    Article  Google Scholar 

  14. Tao Y, Kollios G, Considine J, Li F, Papadias D. Spatio-temporal aggregation using sketches. In: Proceedings of 20th international conference on data engineering. 2004. p. 214–25.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Chinese University of Hong Kong, Hong Kong, China

    Yufei Tao

  2. Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, Hong Kong SAR

    Dimitris Papadias

Authors
  1. Yufei Tao
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  2. Dimitris Papadias
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Corresponding author

Correspondence to Yufei Tao .

Editor information

Editors and Affiliations

  1. Georgia Institute of Technology College of Computing, Atlanta, Georgia, USA

    Ling Liu

  2. University of Waterloo School of Computer Science, Waterloo, Ontario, Canada

    M. Tamer Özsu

Section Editor information

  1. Dept. of Computer Science and Eng., Hong Kong Univ. of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, Hong Kong SAR

    Dimitris Papadias

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Tao, Y., Papadias, D. (2014). Spatio-Temporal Data Warehouses. In: Liu, L., Özsu, M. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4899-7993-3_362-2

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  • DOI: https://doi.org/10.1007/978-1-4899-7993-3_362-2

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  • Publisher Name: Springer, New York, NY

  • Online ISBN: 978-1-4899-7993-3

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