Temporal Aggregation with Range Predicates

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. Previous approaches have aggregate predicates that involve only the time dimension. In this paper we examine techniques to compute temporal aggregates that include key-range predicates as well (range-temporal aggregates). In particular we concentrate on the SUM aggregate, while COUNT is a special case. 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 SB-Tree, which incorporates features from both the SB-Tree and the Multiversion B+-tree, to handle arbitrary key-range temporal aggregation queries. We analyze the performance of our approach and present experimental results that show its efficiency. Furthermore, we address a novel and practical variation called functional rangetemporal aggregation. Here, the value of any record can be a general function over time. The meaning of aggregation is altered such that the contribution of a record to the aggregation result is proportional to the size of the intersection between the record’s time interval and the query time interval. Both analytical and experimental results show the efficiency of our result.