Efficiently supporting temporal granularities

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

Research output: Contribution to journalArticlepeer-review

42 Scopus citations


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
Issue number4
StatePublished - 2000
Externally publishedYes


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

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics


Dive into the research topics of 'Efficiently supporting temporal granularities'. Together they form a unique fingerprint.

Cite this