A dynamic space-efficient filter with constant time operations

Ioana O. Bercea, Guy Even

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

A dynamic dictionary is a data structure that maintains sets of cardinality at most n from a given universe and supports insertions, deletions, and membership queries. A filter approximates membership queries with a one-sided error that occurs with probability at most ε. The goal is to obtain dynamic filters that are space-efficient (the space is 1 + o(1) times the information-theoretic lower bound) and support all operations in constant time with high probability. One approach to designing filters is to reduce to the retrieval problem. When the size of the universe is polynomial in n, this approach yields a space-efficient dynamic filter as long as the error parameter ε satisfies log(1/ε) = ω(log log n). For the case that log(1/ε) = O(log log n), we present the first space-efficient dynamic filter with constant time operations in the worst case (whp). In contrast, the space-efficient dynamic filter of Pagh et al. [29] supports insertions and deletions in amortized expected constant time. Our approach employs the classic reduction of Carter et al. [9] on a new type of dictionary construction that supports random multisets.

Original languageEnglish
Title of host publication17th Scandinavian Symposium and Workshops on Algorithm Theory, SWAT 2020
EditorsSusanne Albers
PublisherSchloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing
ISBN (Electronic)9783959771504
DOIs
StatePublished - 1 Jun 2020
Event17th Scandinavian Symposium and Workshops on Algorithm Theory, SWAT 2020 - Torshavn, Faroe Islands
Duration: 22 Jun 202024 Jun 2020

Publication series

NameLeibniz International Proceedings in Informatics, LIPIcs
Volume162
ISSN (Print)1868-8969

Conference

Conference17th Scandinavian Symposium and Workshops on Algorithm Theory, SWAT 2020
Country/TerritoryFaroe Islands
CityTorshavn
Period22/06/2024/06/20

Keywords

  • Data Structures

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