Skip to main content

Misra-Gries Summaries

  • Living reference work entry
  • First Online:
Encyclopedia of Algorithms

Years and Authors of Summarized Original Work

1982; Misra, Gries

Problem Definition

The frequent items problem is to process a stream of items and find all items occurring more than a given fraction of the time. It is one of the most heavily studied problems in data stream algorithms, dating back to the 1980s. Many applications rely directly or indirectly on finding the frequent items, and implementations are in use in large-scale industrial systems. Informally, given a sequence of items, the problem is simply to find those items which occur most frequently. Typically, this is formalized as finding all items whose frequency exceeds a specified fraction of the total number of items. Variations arise when the items have weights and further when these weights can also be negative.

Definition 1.

Given a stream \(\mathcal{S}\) of n items t 1 … t n , the frequency of an item i is \(f_{i} = \vert \{j\vert t_{j} = i\}\vert \). The exact ϕ-frequent items comprise the set {i | f i  > ϕ n}.

...

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

Recommended Reading

  1. Agarwal P, Cormode G, Huang Z, Phillips J, Wei Z, Yi K (2012) Mergeable summaries. In: ACM principles of database systems, Scottsdale

    Google Scholar 

  2. Berinde R, Cormode G, Indyk P, Strauss M (2009) Space-optimal heavy hitters with strong error bounds. In: ACM principles of database systems, Providence

    Google Scholar 

  3. Bose P, Kranakis E, Morin P, Tang Y (2003) Bounds for frequency estimation of packet streams. In: SIROCCO, Umeå

    Google Scholar 

  4. Boyer B, Moore J (1981) A fast majority vote algorithm. Technical report ICSCA-CMP-32, Institute for Computer Science, University of Texas

    Google Scholar 

  5. Boyer RS, Moore JS (1991) MJRTY – a fast majority vote algorithm. In: Bledsoe WW, Boyer RS (eds) Automated reasoning: essays in honor of Woody Bledsoe. Automated reasoning series. Kluwer Academic, Dordrecht/Boston, pp 105–117

    Chapter  Google Scholar 

  6. Chakrabarti A, Cormode G, McGregor A (2007) A near-optimal algorithm for computing the entropy of a stream. In: ACM-SIAM symposium on discrete algorithms, New Orleans

    Google Scholar 

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

    Article  Google Scholar 

  8. Demaine E, López-Ortiz A, Munro JI (2002) Frequency estimation of internet packet streams with limited space. In: European symposium on algorithms (ESA), Rome

    Google Scholar 

  9. Fischer M, Salzburg S (1982) Finding a majority among n votes: solution to problem 81-5. J Algorithms 3(4):376–379

    Google Scholar 

  10. Ghashami M, Phillips JM (2014) Relative errors for deterministic low-rank matrix approximations. In: ACM-SIAM symposium on discrete algorithms, Portland, pp 707–717

    Google Scholar 

  11. Karp R, Papadimitriou C, Shenker S (2003) A simple algorithm for finding frequent elements in sets and bags. ACM Trans Database Syst 28:51–55

    Article  Google Scholar 

  12. Liberty E (2013) Simple and deterministic matrix sketching. In: ACM SIGKDD, Chicago, pp 581–588

    Google Scholar 

  13. 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

    Article  Google Scholar 

  14. Manku G, Motwani R (2002) Approximate frequency counts over data streams. In: International conference on very large data bases, Hong Kong, pp 346–357

    Google Scholar 

  15. Metwally A, Agrawal D, Abbadi AE (2005) Efficient computation of frequent and top-k elements in data streams. In: International conference on database theory, Edinburgh

    Google Scholar 

  16. Misra J, Gries D (1982) Finding repeated elements. Sci Comput Program 2:143–152

    Article  MATH  MathSciNet  Google Scholar 

  17. Moore J (1981) Problem 81-5. J Algorithms 2:208–209

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Graham Cormode Dr. .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media New York

About this entry

Cite this entry

Cormode, G. (2014). Misra-Gries Summaries. In: Kao, MY. (eds) Encyclopedia of Algorithms. Springer, Boston, MA. https://doi.org/10.1007/978-3-642-27848-8_572-1

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-27848-8_572-1

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer, Boston, MA

  • Online ISBN: 978-3-642-27848-8

  • eBook Packages: Springer Reference Computer SciencesReference Module Computer Science and Engineering

Publish with us

Policies and ethics