Efficiently Supporting Temporal Granularities

Granularity is an integral feature of temporal data. For instance, a person’s age is commo nly given to the granularity ofyearsand 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 tem poral indeterminacy, or “don’t know when” information. We also minimally extend the syntax and semant ics of SQL-92 to support mixed granularities. This support rests on two operations, scaleand cast, that move times between granularities, e.g., from days to months. We demonstrate that our sol tion is practical by showing how granularities can be specified in a modular fashion, and by outlining a timeand space-efficient implementation. The implementation uses several optimization strategi es to mitigate the expense of accommodating multiple granularities. [