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NiagaraCQ: a scalable continuous query system for Internet databases
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Testing and spot-checking of data streams (extended abstract)
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SSDBM '03 Proceedings of the 15th International Conference on Scientific and Statistical Database Management
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VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
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VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
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VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
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VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
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VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
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VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
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The VLDB Journal — The International Journal on Very Large Data Bases
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IEEE Transactions on Knowledge and Data Engineering
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SSPS '08 Proceedings of the 2nd international workshop on Scalable stream processing system
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DASFAA '09 Proceedings of the 14th International Conference on Database Systems for Advanced Applications
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Continuous queries often require significant run-time state over arbitrary data streams. However, streams may exhibit certain data or arrival patterns, or constraints, that can be detected and exploited to reduce state considerably without compromising correctness. Rather than requiring constraints to be satisfied precisely, which can be unrealistic in a data streams environment, we introduce k-constraints, where k is an adherence parameter specifying how closely a stream adheres to the constraint. (Smaller k's are closer to strict adherence and offer better memory reduction.) We present a query processing architecture, called k-Mon, that detects useful k-constraints automatically and exploits the constraints to reduce run-time state for a wide range of continuous queries. Experimental results showed dramatic state reduction, while only modest computational overhead was incurred for our constraint monitoring and query execution algorithms.