An Efficient Method for Indexing Now-relative Bitemporal Data
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Most modern database applications contain a significant amount of time dependent data and a substantial proportion of this data is now-relative, i.e. current now. While much research has focussed on indexing temporal data in general, little work has addressed the indexing of now-relative data, which is a natural and meaningful part of every temporal database as well as being the focus of most queries. This paper proposes a logical query transformation that relies on the POINT representation of current time and the geometrical features of spatial access methods. Logical query transformation enables off-the-shelf spatial indexes to be used. We empirically demonstrate that this method is efficient on now-relative bitemporal data, outperforming a straightforward maximum-timestamp approach by a factor of more than 20, both in number of disk accesses and CPU usage.
Database Technologies 2004
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