Computing temporal aggregates

Research output: Contribution to conferencePaper

73 Scopus citations

Abstract

Aggregate computation, such as selecting the minimum attribute value of a relation, is expensive, especially in a temporal database. We describe the basic techniques behind computing aggregates in conventional databases and show that these techniques are not efficient when applied to temporal databases. We examine the problem of computing constant intervals (intervals of time for which the aggregate value is constant) used for temporal grouping. We introduce two new algorithms for computing temporal aggregates: the aggregation tree and the k-ordered aggregation tree. An empirical comparison demonstrates that the choice of algorithm depends in part on the amount of memory available, the number of tuples in the underlying relation, and the degree to which the tuples are ordered. This study shows that the simplest strategy is to first sort the underlying relation, then apply the k-ordered aggregation tree algorithm with k = 1.

Original languageEnglish (US)
Pages222-231
Number of pages10
StatePublished - Jan 1 1995
EventProceedings of the 1995 IEEE 11th International Conference on Data Engineering - Taipei, Taiwan
Duration: Mar 6 1995Mar 10 1995

Other

OtherProceedings of the 1995 IEEE 11th International Conference on Data Engineering
CityTaipei, Taiwan
Period3/6/953/10/95

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems

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    Kline, N., & Snodgrass, R. T. (1995). Computing temporal aggregates. 222-231. Paper presented at Proceedings of the 1995 IEEE 11th International Conference on Data Engineering, Taipei, Taiwan, .