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Encyclopedia of Algorithms pp 1–6Cite as

Count-Min Sketch

Years and Authors of Summarized Original Work

2004; Cormode, Muthukrishnan

Problem Definition

The problem of sketching a large mathematical object is to produce a compact data structure that approximately represents it. The Count-Min (CM) sketch is an example of a sketch that allows a number of related quantities to be estimated with accuracy guarantees, including point queries and dot product queries. Such queries are at the core of many computations, so the structure can be used in order to answer a variety of other queries, such as frequent items (heavy hitters), quantile finding, join size estimation, and more. Since the sketch can process updates in the form of additions or subtractions to dimensions of the vector (which may correspond to insertions or deletions or other transactions), it is capable of working over streams of updates, at high rates.

The data structure maintains the linear projection of the vector with a number of other random vectors. These vectors are defined...

Keywords

  • Streaming algorithms
  • Frequent items
  • Approximate counting
  • Sketch

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Recommended Reading

  1. Alon N, Matias Y, Szegedy M (1996) The space complexity of approximating the frequency moments. In: ACM symposium on theory of computing, Philadelphia, pp 20–29

    Google Scholar 

  2. Charikar M, Chen K, Farach-Colton M (2002) Finding frequent items in data streams. In: Procedings of the international colloquium on automata, languages and programming (ICALP), Málaga

    Google Scholar 

  3. Cormode G, Hadjieleftheriou M (2009) Finding the frequent items in streams of data. Commun ACM 52(10):97–105

    CrossRef  Google Scholar 

  4. Cormode G, Muthukrishnan S (2005) An improved data stream summary: the Count-Min sketch and its applications. J Algorithms 55(1):58–75

    CrossRef  MATH  MathSciNet  Google Scholar 

  5. Cormode G, Muthukrishnan S (2005) Summarizing and mining skewed data streams. In: SIAM conference on data mining, Newport Beach

    Google Scholar 

  6. Cormode G, Korn F, Muthukrishnan S, Johnson T, Spatscheck O, Srivastava D (2004) Holistic UDAFs at streaming speeds. In: ACM SIGMOD international conference on management of data, Paris, pp 35–46

    Google Scholar 

  7. Estan C, Varghese G (2002) New directions in traffic measurement and accounting. In: Proceedings of ACM SIGCOMM, computer communication review, vol 32, 4, Pittsburgh, PA, pp 323–338

    Google Scholar 

  8. Ganguly S, Lakshminath B (2006) Estimating entropy over data streams. In: European symposium on algorithms (ESA), Zurich

    Google Scholar 

  9. Indyk P (2003) Better algorithms for high-dimensional proximity problems via asymmetric embeddings. In: ACM-SIAM symposium on discrete algorithms, Baltimore

    Google Scholar 

  10. Lai YK, Byrd GT (2006) High-throughput sketch update on a low-power stream processor. In: Proceedings of the ACM/IEEE symposium on architecture for networking and communications systems, San Jose

    Google Scholar 

  11. Manerikar N, Palpanas T (2009) Frequent items in streaming data: an experimental evaluation of the state-of-the-art. Data Knowl Eng 68(4):415–430

    CrossRef  Google Scholar 

  12. Motwani R, Raghavan P (1995) Randomized algorithms. Cambridge University Press, Cambridge/New York

    CrossRef  MATH  Google Scholar 

  13. Roughan M, Zhang Y (2006) Secure distributed data mining and its application in large-scale network measurements. In: ACM SIGCOMM computer communication review (CCR), Pisa

    Google Scholar 

  14. Sarlós T, Benzúr A, Csalogány K, Fogaras D, Rácz B (2006) To randomize or not to randomize: space optimal summaries for hyperlink analysis. In: International conference on World Wide Web (WWW), Edinburgh

    Google Scholar 

  15. Spiegel J, Polyzotis N (2006) Graph-based synopses for relational selectivity estimation. In: ACM SIGMOD international conference on management of data, Chicago

    Google Scholar 

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Author information

Authors and Affiliations

  1. Computer Science, University of Warwick, Warwick, UK

    Graham Cormode Dr.

Authors
  1. Graham Cormode Dr.
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Corresponding author

Correspondence to Graham Cormode Dr. .

Editor information

Editors and Affiliations

  1. Dept. Electrical Engineering & Computer Science, Northwestern University, Evanston, Illinois, USA

    Ming-Yang Kao

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Cormode, G. (2014). Count-Min Sketch. In: Kao, MY. (eds) Encyclopedia of Algorithms. Springer, Boston, MA. https://doi.org/10.1007/978-3-642-27848-8_579-1

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  • DOI: https://doi.org/10.1007/978-3-642-27848-8_579-1

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