Efficiently supporting temporal granularities

Curtis E. Dyreson, William S. Evans, Hong Lin, Richard Thomas Snodgrass

Research output: Contribution to journalArticle

39 Citations (Scopus)

Abstract

Granularity is an integral feature of temporal data. For instance, a person's age is commonly given to the granularity of years and the time of their next airline flight to the granularity of minutes. A granularity creates a discrete image, in terms of granules, of a (possibly continuous) time-line. We present a formal model for granularity in temporal operations that is integrated with temporal indeterminacy, or "don't know when" information. We also minimally extend the syntax and semantics of SQL-92 to support mixed granularities. This support rests on two operations, scale and cast, that move times between granularities, e.g., from days to months. We demonstrate that our solution is practical by showing how granularities can be specified in a modular fashion, and by outlining a time- and space-efficient implementation. The implementation uses several optimization strategies to mitigate the expense of accommodating multiple granularities.

Original languageEnglish (US)
Pages (from-to)568-587
Number of pages20
JournalIEEE Transactions on Knowledge and Data Engineering
Volume12
Issue number4
StatePublished - 2000

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Semantics

Keywords

  • Calendar
  • Granularity
  • Indeterminacy
  • SQL-92
  • Temporal database
  • TSQL2

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Artificial Intelligence
  • Information Systems

Cite this

Efficiently supporting temporal granularities. / Dyreson, Curtis E.; Evans, William S.; Lin, Hong; Snodgrass, Richard Thomas.

In: IEEE Transactions on Knowledge and Data Engineering, Vol. 12, No. 4, 2000, p. 568-587.

Research output: Contribution to journalArticle

Dyreson, Curtis E. ; Evans, William S. ; Lin, Hong ; Snodgrass, Richard Thomas. / Efficiently supporting temporal granularities. In: IEEE Transactions on Knowledge and Data Engineering. 2000 ; Vol. 12, No. 4. pp. 568-587.
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