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- W4292761019 abstract "Sketches are a family of streaming algorithms widely used in the world of big data to perform fast, real-time analytics. A popular sketch type is Quantiles, which estimates the data distribution of a large input stream. We present Quancurrent, a highly scalable concurrent Quantiles sketch. Quancurrent's throughput increases linearly with the number of available threads, and with $32$ threads, it reaches an update speedup of $12$x and a query speedup of $30$x over a sequential sketch. Quancurrent allows queries to occur concurrently with updates and achieves an order of magnitude better query freshness than existing scalable solutions." @default.
- W4292761019 created "2022-08-23" @default.
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- W4292761019 date "2022-08-19" @default.
- W4292761019 modified "2023-09-29" @default.
- W4292761019 title "Quancurrent: A Concurrent Quantiles Sketch" @default.
- W4292761019 doi "https://doi.org/10.48550/arxiv.2208.09265" @default.
- W4292761019 hasPublicationYear "2022" @default.
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