Parallel algorithms for computing temporal aggregates

The ability to model the temporal dimension is essential to many applications. Furthermore, the rate of increase in database size and response time requirements has out-paced advancements in processor and mass storage technology, leading to the need for parallel temporal database management systems. In this paper, we introduce a variety of parallel temporal aggregation algorithms for a shared-nothing architecture based on the sequential "aggregation tree algorithm". Via an empirical study, we found that the number of processing nodes, the partitioning of the data, the placement of results and the degree of data reduction effected by the aggregation impacted on the performance of the algorithms. For distributed results placement, we discovered that time-division merging was the obvious choice. For centralized results and high data reduction, pairwise merging was preferred, regardless of the number of processing nodes, but for low data reduction, it only performed well up to 32 nodes. This led us to a centralized variant of time-division merging which was best for larger configurations having low data reduction.