cient Computation of Temporal Aggregates with Range Predicates

Any software made available via TimeCenter is provided \as is" and without any express or implied warranties, including, without limitation, the implied warranty of merchantability and tness for a particular purpose. The TimeCenter icon on the cover combines two \arrows." These \arrows" are letters in the so-called Rune alphabet used one millennium ago by the Vikings, as well as by their precedessors and successors. The Rune alphabet (second phase) has 16 letters, all of which h a ve angular shapes and lack horizontal lines because the primary storage medium was wood. Runes may also befound on jewelry, tools, and weapons and were perceived by m a n y a s h a ving magic, hidden powers. The two Rune arrows in the icon denote \T" and \C," respectively. Extended Abstract A temporal aggregation query is an important but costly operation for applications that maintain time-evolving data (data warehouses, temporal databases, etc.). Due to the large volume of such data, performance improvements for temporal aggregation queries are critical. In this paper we examine techniques to compute temporal aggregates that include key-range predicates (range temporal aggregates). In particular we concentrate on SUM, COUNT and AVG aggregates. This problem is novell to handle arbitrary key ranges, previous methods would need to keep a separate index for every possible key range. We propose an approach based on a new index structure called the Multiversion SB-Tree, which incorporates features from both the SB-Tree and the Multiversion B-Tree, to handle arbitrary key-range temporal SUM, COUNT and AVG queries. We analyze the performance of our approach and present experimental results that show its eeciency.