Abstract
AbstractAggregate queries are frequent in massive database applications. Their execution tends to be time consuming and costly. Therefore efficiently executing aggregate queries is very important. Semantic cache is a novel method for aiding query evaluation that reuses results of previously answered queries. But little work has been done on semantic cache involving aggregate queries. This is a limiting factor in its applicability. To use semantic cache in massive database applications, it is necessary to extend semantic cache to process aggregate query. In this paper, query matching is identified as a foundation for answering aggregate query by semantic caches. Firstly a formal semantic cache model for aggregate query is proposed. Based on this model, we discuss aggregate query matching. Two algorithms are presented for aggregate query matching. These two algorithms have been implemented in a massive database application project. The practice shows the algorithms are efficient.KeywordsAggregation FunctionLarge Data BaseAggregate QueryMatch TypeQuery MatchThese 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.
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