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Data streaming systems are becoming essential for monitoring applications such as financial analysis and network intrusion detection. These systems often have to process many similar but different queries over common data. Since executing each query separately can lead to significant scalability and performance problems, it is vital to share resources by exploiting similarities in the queries. In this paper we present ways to efficiently share streaming aggregate queries with differing periodicdoi:10.1145/1142473.1142543 dblp:conf/sigmod/KrishnamurthyWF06 fatcat:ro4qgywofvfrpmwldnszu744se