Counting with TinyTable: Every bit counts!

Gil Einziger; Roy Friedman

doi:10.1109/INFCOMW.2015.7179351

Counting with TinyTable: Every bit counts!

Gil Einziger, Roy Friedman

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

8 Scopus citations

Abstract

Counting Bloom filters (CBF) and their variants are data structures that support membership or multiplicity queries with a low probabilistic error. Yet, they incur a significant memory space overhead when compared to lower bounds as well as to (plain) Bloom filters, which can only represent set membership without removals. This work presents TinyTable, an efficient hash table based construction that supports membership queries, multiplicity queries (statistics) and removals. TinyTable is more space efficient than existing alternatives, both those derived from Bloom filters and other hash table based schemes. In fact, when the required false positive rate is smaller than 1%, it is even more space efficient than (plain) Bloom filters.

Original language	English
Title of host publication	2015 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2015
Publisher	Institute of Electrical and Electronics Engineers
Pages	77-78
Number of pages	2
ISBN (Electronic)	9781467371315
DOIs	https://doi.org/10.1109/INFCOMW.2015.7179351
State	Published - 4 Aug 2015
Externally published	Yes
Event	IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2015 - Hong Kong, Hong Kong Duration: 26 Apr 2015 → 1 May 2015

Publication series

Name	Proceedings - IEEE INFOCOM
Volume	2015-August
ISSN (Print)	0743-166X

Conference

Conference	IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2015
Country/Territory	Hong Kong
City	Hong Kong
Period	26/04/15 → 1/05/15

ASJC Scopus subject areas

General Computer Science
Electrical and Electronic Engineering

Access to Document

10.1109/INFCOMW.2015.7179351

Cite this

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title = "Counting with TinyTable: Every bit counts!",

abstract = "Counting Bloom filters (CBF) and their variants are data structures that support membership or multiplicity queries with a low probabilistic error. Yet, they incur a significant memory space overhead when compared to lower bounds as well as to (plain) Bloom filters, which can only represent set membership without removals. This work presents TinyTable, an efficient hash table based construction that supports membership queries, multiplicity queries (statistics) and removals. TinyTable is more space efficient than existing alternatives, both those derived from Bloom filters and other hash table based schemes. In fact, when the required false positive rate is smaller than 1%, it is even more space efficient than (plain) Bloom filters.",

author = "Gil Einziger and Roy Friedman",

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Einziger, G & Friedman, R 2015, Counting with TinyTable: Every bit counts! in 2015 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2015., 7179351, Proceedings - IEEE INFOCOM, vol. 2015-August, Institute of Electrical and Electronics Engineers, pp. 77-78, IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2015, Hong Kong, Hong Kong, 26/04/15. https://doi.org/10.1109/INFCOMW.2015.7179351

Counting with TinyTable: Every bit counts! / Einziger, Gil; Friedman, Roy.
2015 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2015. Institute of Electrical and Electronics Engineers, 2015. p. 77-78 7179351 (Proceedings - IEEE INFOCOM; Vol. 2015-August).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - Counting Bloom filters (CBF) and their variants are data structures that support membership or multiplicity queries with a low probabilistic error. Yet, they incur a significant memory space overhead when compared to lower bounds as well as to (plain) Bloom filters, which can only represent set membership without removals. This work presents TinyTable, an efficient hash table based construction that supports membership queries, multiplicity queries (statistics) and removals. TinyTable is more space efficient than existing alternatives, both those derived from Bloom filters and other hash table based schemes. In fact, when the required false positive rate is smaller than 1%, it is even more space efficient than (plain) Bloom filters.

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ER -

Counting with TinyTable: Every bit counts!

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