Skip to main content
SpringerLink
Log in

Temporal Aggregation

  • Reference work entry
Encyclopedia of Database Systems

Definition

In database management, aggregation denotes the process of consolidating or summarizing a database instance; this is typically done by creating so-called aggregation groups of elements in the argument database instance and then applying an aggregate function to each group, thus obtaining an aggregate value for each group that is then associated with each element in the group. In a relational database context, the instances are relations and the elements are tuples. Aggregation groups are then typically formed by partitioning the tuples based on the values of one or more attributes so that tuples with identical values for these attributes are assigned to the same group. An aggregate function, e.g., sum, avg, or min, is then applied to another attribute to obtain a single value for each group that is assigned to each tuple in the group as a value of a new attribute. Relational projection is used for eliminating detail from aggregation results.

In temporal relational...

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 2,500.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Recommended Reading

  1. Ben-Zvi J. The Time Relational Model. Ph.D. thesis, Computer Science Department, UCLA, 1982.

    Google Scholar 

  2. Böhlen M.H., Gamper J., and Jensen C.S. How would you like to aggregate your temporal data? In Proc. 13th Int. Symp. on Temporal Representation and Reasoning, 2006, pp. 121–136.

    Google Scholar 

  3. Böhlen M.H., Gamper J., and Jensen C.S. Multi-dimensional aggregation for temporal data. In Advances in Database Technology, Proc. 10th Int. Conf. on Extending Database Technology, 2006, pp. 257–275.

    Google Scholar 

  4. Gao D., Gendrano J.A.G., Moon B., Snodgrass R.T., Park M., Huang B.C., and Rodrigue J.M. Main memory-based algorithms for efficient parallel aggregation for temporal databases. Distrib. Parallel Dat., 16(2):123–163, 2004.

    Article  Google Scholar 

  5. Gao D. and Snodgrass R.T. Temporal slicing in the evaluation of XML queries. In Proc. 29th Int. Conf. on Very Large Data Bases, 2003, pp. 632–643.

    Google Scholar 

  6. Kline N. and Snodgrass R.T. Computing temporal aggregates. In Int. Conf. on Data Engineering, 1995, pp. 222–231.

    Google Scholar 

  7. Moon B., Vega Lopez I.F., and Immanuel V. Efficient algorithms for large-scale temporal aggregation. IEEE Trans. Knowl. Data Eng., 15(3):744–759, 2003.

    Article  Google Scholar 

  8. Navathe S.B. and Ahmed R. A temporal relational model and a query language. Inf. Sci., 49(1–3):147–175, 1989.

    Article  MATH  Google Scholar 

  9. Snodgrass R.T., Gomez S., and McKenzie L.E. Aggregates in the temporal query language TQuel. IEEE Trans. Knowl. Data Eng., 5(5):826–842, 1993.

    Article  Google Scholar 

  10. Tansel A.U. A statistical interface to historical relational databases. In Proc. Int. Conf. on Data Engineering, 1987, pp. 538–546.

    Google Scholar 

  11. Tao Y., Papadias D., and Faloutsos C. Approximate temporal aggregation. In Proc. 20th Int. Conf. on Data Engineering, 2004, pp. 190–201.

    Google Scholar 

  12. Tuma P.A. Implementing Historical Aggregates in TempIS. M.Sc. thesis, Wayne State University, 1992.

    Google Scholar 

  13. Vega Lopez I.F., Snodgrass R.T., and Moon B. Spatiotemporal aggregate computation: a survey. IEEE Trans. Knowl. Data Eng., 17(2):271–286, 2005.

    Article  Google Scholar 

  14. Yang J. and Widom J. Incremental computation and maintenance of temporal aggregates. VLDB J., 12(3):262–283, 2003.

    Article  Google Scholar 

  15. Zhang D., Markowetz A., Tsotras V., Gunopulos D., and Seeger B. Efficient computation of temporal aggregates with range predicates. In Proc. 20th ACM SIGACT-SIGMOD-SIGART Symp. on Principles of Database Systems, 2001, pp. 237–245.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Free University of Bozen-Bolzano, Bolzano, Italy

    Johann Gamper & Michael Böhlen

  2. Aalborg University, Aalborg, Denmark

    Christian S. Jensen

Authors
  1. Johann Gamper
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michael Böhlen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Christian S. Jensen
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. College of Computing, Georgia Institute of Technology, 266 Ferst Drive, 30332-0765, Atlanta, GA, USA

    LING LIU (Professor) (Professor)

  2. Database Research Group David R. Cheriton School of Computer Science, University of Waterloo, 200 University Avenue West, N2L 3G1, Waterloo, ON, Canada

    M. TAMER ÖZSU (Professor and Director, University Research Chair) (Professor and Director, University Research Chair)

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer Science+Business Media, LLC

About this entry

Cite this entry

Gamper, J., Böhlen, M., Jensen, C.S. (2009). Temporal Aggregation. In: LIU, L., ÖZSU, M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-39940-9_386

Download citation

Publish with us

Policies and ethics

Search

Navigation