Approximating Data with the Count-Min Sketch

Authors:
Graham Cormode;Muthu Muthukrishnan
Affiliations:
AT&TLabs-Research;Rutgers University
Venue:
IEEE Software
Year:
2012

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Abstract

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.