Parallel processing of multiple aggregate queries on shared-nothing multiprocessors

Abstract

Decision support systems that include on-line analytical processing and data mining have recently attracted research attention. Such applications treat data in very large databases as multidimensional data cubes. Each cell of a data cube typically is some aggregation, such as total sales volume, that is of interest to analysts. Since it may be necessary to compute many cells, and the performance is critical, we propose parallel algorithms that compute multiple aggregate queries in data cubes on a shared-nothing multiprocessor with high-bandwidth communication facilities. We evaluate the algorithms on the basis of analytical modeling and an implementation on an IBM SP2 system.KeywordsAssociation RuleMain MemoryData CubeSource RelationMultiple QueryThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.