Abstract-The ability to model time-varying natures is essential to many database applications such as data warehousing and mining. However, the temporal aspects provide many unique characteristics and challenges for query processing and optimization. Among the challenges is computing temporal aggregates, which is complicated by having to compute temporal grouping. In this paper, we introduce a variety of temporal aggregation algorithms that overcome major drawbacks of previous work. First, for small-scale aggregations, both the worst-case and average-case processing time have been improved significantly. Second, for large-scale aggregations, the proposed algorithms can deal with a database that is substantially larger than the size of available memory. Third, the parallel algorithm designed on a shared-nothing architecture achieves scalable performance by delivering nearly linear scale-up and speed-up, even at the presence of data skew. The contributions made in this paper are particularly important because the rate of increase in database size and response time requirements has out-paced advancements in processor and mass storage technology. Index Terms-Temporal databases, temporal aggregation, scalable query processing, data partitioning, balanced tree algorithm, merge-sort algorithm, temporal query processing, aggregate queries.
Besides, Moon et al. REF also proposed a bucket algorithm and parallelized it on a shared-nothing architecture.
Efficient Algorithms for Large-Scale Temporal Aggregation
IEEE TRANS. ON KNOWLEDGE AND DATA ENGINEERING
Computer Science