@inproceedings{86b90eaf0ff8408ea8123955e5e85c18,
title = "Quancurrent: A Concurrent Quantiles Sketch",
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 12x and a query speedup of 30x 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.",
keywords = "big data, concurrency, quantiles, real-time analysis, sketches, streaming algorithms",
author = "\{Elias Zada\}, Shaked and Arik Rinberg and Idit Keidar",
note = "Publisher Copyright: {\textcopyright} 2023 ACM.; 35th ACM Symposium on Parallelism in Algorithms and Architectures, SPAA 2023 ; Conference date: 17-06-2023 Through 19-06-2023",
year = "2023",
month = jun,
day = "17",
doi = "10.1145/3558481.3591074",
language = "الإنجليزيّة",
series = "Annual ACM Symposium on Parallelism in Algorithms and Architectures",
pages = "15--25",
booktitle = "SPAA 2023 - Proceedings of the 35th ACM Symposium on Parallelism in Algorithms and Architectures",
}