Relative error streaming quantiles

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Abstract

Estimating ranks, quantiles, and distributions over streaming data is a central task in data analysis and monitoring. Given a stream of n items from a data universe equipped with a total order, the task is to compute a sketch (data structure) of size polylogarithmic in n. Given the sketch and a query item y, one should be able to approximate its rank in the stream, i.e., the number of stream elements smaller than or equal to y.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Divisions: Faculty of Science, Engineering and Medicine > Science > Computer Science
Library of Congress Subject Headings (LCSH): Data structures (Computer science) , Database management , Algorithms , Computer networks -- Management -- Algorithms, Quantile regression
Journal or Publication Title: Journal of the ACM
Publisher: Association for Computing Machinery, Inc.
ISSN: 0004-5411
Official Date: 16 October 2023
Dates:
Date
Event
16 October 2023
Published
26 August 2023
Accepted
Volume: 70
Number: 5
Page Range: pp. 1-48
DOI: 10.1145/3617891
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access (Creative Commons open licence)
Date of first compliant deposit: 6 November 2023
Date of first compliant Open Access: 8 November 2023
RIOXX Funder/Project Grant:
Project/Grant ID
RIOXX Funder Name
Funder ID
ERC-2014-CoG 647557
European Research Council
UNCE/SCI/004
Univerzita Karlova v Praze
CCF-1918989
[NSF] National Science Foundation (US)
CCF-1845125
[NSF] National Science Foundation (US)
19-27871X
Grantová Agentura České Republiky
UNSPECIFIED
Persistent URL: https://wrap.warwick.ac.uk/180761/

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