Efficient Computation of Temporal Aggregates with Range Predicates Donghui Zhang, Alexander Markowetz, Vassilis J. Tsotras, Dimitrios Gunopulos, and Bernhard Seeger   View Paper (PDF)   Return to Aggregates 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 key­range predicates (range temporal aggregates). In particular we concentrate on SUM, COUNT and AVG aggregates. This problem is novel; 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 SB­Tree, which incorporates features from both the SB­Tree and the Multiversion B­Tree, to handle arbitrary key­range temporal SUM, COUNT and AVG queries. We analyze the performance of our approach and present experimental results that show its efficiency. DiSC'02 © 2003 Association for Computing Machinery