Computing temporal aggregates

Aggregate computation, such as selecting the minimum attribute value of a relation, is expensive, especially in a temporal database. We describe the basic techniques behind computing aggregates in conventional databases and show that these techniques are not efficient when applied to temporal databases. We examine the problem of computing constant intervals (intervals of time for which the aggregate value is constant) used for temporal grouping. We introduce two new algorithms for computing temporal aggregates: the aggregation tree and the k-ordered aggregation tree. An empirical comparison demonstrates that the choice of algorithm depends in part on the amount of memory available, the number of tuples in the underlying relation, and the degree to which the tuples are ordered. This study shows that the simplest strategy is to first sort the underlying relation, then apply the k-ordered aggregation tree algorithm with k=1.<>