Solving implication problems in database applications
SIGMOD '89 Proceedings of the 1989 ACM SIGMOD international conference on Management of data
Rewriting aggregate queries using views
PODS '99 Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Semantic caching via query matching for web sources
Proceedings of the eighth international conference on Information and knowledge management
Answering complex SQL queries using automatic summary tables
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Semantic Caching and Query Processing
IEEE Transactions on Knowledge and Data Engineering
Aggregate-Query Processing in Data Warehousing Environments
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Semantic Data Caching and Replacement
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Answering Queries with Aggregation Using Views
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Answering Queries by Semantic Caches
DEXA '99 Proceedings of the 10th International Conference on Database and Expert Systems Applications
Associative caching in client-server databases
Associative caching in client-server databases
Graph based query trimming algorithm for relational data semantic cache
Proceedings of the International Conference on Management of Emergent Digital EcoSystems
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Aggregate 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.