Answering linear optimization queries with an approximate stream index |
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Authors: | Gang Luo Kun-Lung Wu Philip S Yu |
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Affiliation: | (1) IBM T.J. Watson Research Center, 19 Skyline Drive, Hawthorne, NY 10532, USA |
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Abstract: | We propose a SAO index to approximately answer arbitrary linear optimization queries in a sliding window of a data stream.
It uses limited memory to maintain the most “important” tuples. At any time, for any linear optimization query, we can retrieve
the approximate top-K tuples in the sliding window almost instantly. The larger the amount of available memory, the better the quality of the answers
is. More importantly, for a given amount of memory, the quality of the answers can be further improved by dynamically allocating
a larger portion of the memory to the outer layers of the SAO index.
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Keywords: | Indexing method Query processing Relational database Stream processing Linear optimization query |
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