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<p><b>Abstract</b>—<it>Granularity</it> is an integral feature of temporal data. For instance, a person's age is commonly given to the granularity of <it>years</it> and the time of their next airline flight to the granularity of <it>minutes</it>. A granularity creates a discrete image, in terms of <it>granules</it>, 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, <it>scale</it> and <it>cast</it>, 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.</p>
Calendar, granularity, indeterminacy, SQL-92, temporal database, TSQL2.
William S. Evans, Richard T. Snodgrass, Hong Lin, Curtis E. Dyreson, "Efficiently Supporting Temporal Granularities", IEEE Transactions on Knowledge & Data Engineering, vol. 12, no. , pp. 568-587, July/August 2000, doi:10.1109/69.868908
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