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Approximating Data with the Count-Min Sketch
January/February 2012 (vol. 29 no. 1)
pp. 64-69
ASCII Text
x 
 
Graham Cormode, Muthu Muthukrishnan, "Approximating Data with the Count-Min Sketch," IEEE Software, vol. 29, no. 1, pp. 64-69, January/February, 2012.
 
 
BibTex
x
 
@article{ 10.1109/MS.2011.127,
author = {Graham Cormode and Muthu Muthukrishnan},
title = {Approximating Data with the Count-Min Sketch},
journal ={IEEE Software},
volume = {29},
number = {1},
issn = {0740-7459},
year = {2012},
pages = {64-69},
doi = {http://doi.ieeecomputersociety.org/10.1109/MS.2011.127},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - MGZN
JO - IEEE Software
TI - Approximating Data with the Count-Min Sketch
IS - 1
SN - 0740-7459
SP64
EP69
EPD - 64-69
A1 - Graham Cormode,
A1 - Muthu Muthukrishnan,
PY - 2012
KW - Count-Min sketch
KW - massive data
KW - streaming algorithms
KW - software engineering
VL - 29
JA - IEEE Software
ER -
 
Graham Cormode, AT&T Labs-Research
Muthu Muthukrishnan, Rutgers University
Faced with handling multiple large data sets in modern data-processing settings, researchers have proposed sketch data structures that capture salient properties while occupying little memory and that update or probe quickly. In particular, the Count-Min sketch has proven effective for a variety of applications. It concurrently tracks many item counts with surprisingly strong accuracy.

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Index Terms:
Count-Min sketch, massive data, streaming algorithms, software engineering
Citation:
Graham Cormode, Muthu Muthukrishnan, "Approximating Data with the Count-Min Sketch," IEEE Software, vol. 29, no. 1, pp. 64-69, Jan.-Feb. 2012, doi:10.1109/MS.2011.127
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