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• W2439904216 abstract "Approximated algorithms are often used to estimate the frequency of items on high volume, fast data streams. The most common ones are variations of Count-Min sketch, which use sub-linear space for the count, but can produce errors in the counts of the most frequent items and can misclassify low-frequency items. In this paper, we improve the accuracy of sketch-based algorithms by increasing the frequency estimation accuracy of the most frequent items and reducing the possible misclassification of low-frequency items, while also improving the overall throughput. Our solution, called Augmented Sketch (ASketch), is based on a pre-filtering stage that dynamically identifies and aggregates the most frequent items. Items overflowing the pre-filtering stage are processed using a conventional sketch algorithm, thereby making the solution general and applicable in a wide range of contexts. The pre-filtering stage can be efficiently implemented with SIMD instructions on multi-core machines and can be further parallelized through pipeline parallelism where the filtering stage runs in one core and the sketch algorithm runs in another core." @default.
• W2439904216 created "2016-06-24" @default.
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• W2439904216 date "2016-06-14" @default.
• W2439904216 modified "2023-10-16" @default.
• W2439904216 title "Augmented Sketch" @default.
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• W2439904216 doi "https://doi.org/10.1145/2882903.2882948" @default.
• W2439904216 hasPublicationYear "2016" @default.
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