Skip to main content

Frequent Items on Streams

  • Reference work entry
  • First Online:
Encyclopedia of Database Systems
  • 31 Accesses

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 4,499.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 6,499.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Recommended Reading

  1. Agarwal P, Cormode G, Zengfeng H, Phillips J, Wei Z, Yi K. Mergeable summaries. In: Proceedings of the 31st ACM PODS Symposium on Principles of Database Systems; 2012. p. 23–34. An extended version appeared in ACM Trans Database Syst. 2013;38(4):26:1–28.

    Article  MathSciNet  MATH  Google Scholar 

  2. Arasu A, Manku G. Approximate counts and quantiles over sliding windows. In: Proceedings of the 23rd ACM PODS Symposium on Principles of Database Systems; 2004. p. 286–96.

    Google Scholar 

  3. Bandi N, Metwally A, Agrawal D, Abbadi AE. Fast data stream algorithms using associative memories. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 2007. p. 247–56.

    Google Scholar 

  4. Boyer R, Moore J. A fast majority vote algorithm. Technical report 1981–32. Austin: Institute for Computing Science, University of Texas; 1981.

    Google Scholar 

  5. Chakrabarti Al, Cormode G, McGregor A. A near-optimal algorithm for computing the entropy of a stream. In: Proceedings of the 18th ACM-SIAM Symposium on Discrete Algorithms; 2007. p. 328–35.

    Google Scholar 

  6. Cormode G, Hadjieleftherion M. Finding frequent items in data streams. Proc VLDB Endowment. 2008;1(2):1530–41.

    Article  Google Scholar 

  7. Cormode G, Hadjieleftheriou M. Methods for finding frequent items in data streams. VLDB J. 2010;19: 3–20.

    Article  Google Scholar 

  8. Cormode G, Korn F, Muthukrishnan S, Srivastava D. Diamond in the rough: finding hierarchical heavy hitters in multi-dimensional data. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 2004. p. 155–66. An extended version appeared in ACM Trans Knowl Discov Data. 2008;1(4):1–48.

    Article  Google Scholar 

  9. Cormode G, Korn F, Tirthapura S. Exponentially decayed aggregates on data streams. In: Proceedings of the IEEE 24th ICDE International Conference on Data Engineering; 2008. p. 1379–81.

    Google Scholar 

  10. Cormode G, Muthukrishnan S. What’s hot and what’s not: tracking most frequent items dynamically. In: Proceedings of the 22nd ACM PODS Symposium on Principles of Database Systems; 2003. p. 296–306. An extended version appeared in ACM Trans Comput Syst. 2005;30(1):249–78.

    Google Scholar 

  11. Demaine E, López-Ortiz A, Munro J. Frequency estimation of internet packet streams with limited space. In: Proceedings of the 10th Annual European Symposium on Algorithms; 2002. p. 348–60.

    Chapter  Google Scholar 

  12. Estan C, Varghese G. New directions in traffic measurement and accounting: focusing on the elephants, ignoring the mice. ACM Trans Comput Syst. 2003;21(3):270–313.

    Article  Google Scholar 

  13. Fischer M, Salzberg S. Finding a majority among N votes: solution to problem 81–5. J Algorithms. 1982;3(4):376–9.

    Google Scholar 

  14. Jin C, Qian W, Sha C, Yu J, Zhou A. Dynamically maintaining frequent items over a data stream. In: Proceedings of the 12th International Conference on Information and Knowledge Management; 2003. p. 287–94.

    Google Scholar 

  15. Karp R, Shenker S, Papadimitriou C. A simple algorithm for finding frequent elements in streams and bags. ACM Trans Database Syst. 2003;28(1):51–5.

    Article  Google Scholar 

  16. Lee L, Ting H. A simpler and more efficient deterministic scheme for finding frequent items over sliding windows. In: Proceedings of the 25th ACM PODS Symposium on Principles of Database Systems; 2006. p. 290–7.

    Google Scholar 

  17. Liu H, Lin Y, Han J. Methods for mining frequent items in data streams: an overview. Knowl Inf Syst. 2011;26:1–30.

    Article  Google Scholar 

  18. Manku G, Motwani R. Approximate frequency counts over data streams. In: Proceedings of the 28th International Conference on Very Large Data Bases; 2002. p. 346–57.

    Chapter  Google Scholar 

  19. Metwally A, Agrawal D, El Abbadi A. Efficient computation of frequent and top-k elements in data streams. In: Proceedings of the 10th International Conference on Database Theory; 2005. p. 398–412. An extended version appeared in ACM Trans Database Syst. 2006;31(3):1095–133.

    Article  Google Scholar 

  20. Misra J, Gries D. Finding repeated elements. Sci Comput Program. 1982;2:143–52.

    Article  MathSciNet  MATH  Google Scholar 

  21. Zhang L, Guan Y. Frequency estimation over sliding windows. In: Proceedings of the 24th International Conference on Data Engineering; 2008. p. 1385–7.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ahmed Metwally .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Science+Business Media, LLC, part of Springer Nature

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Metwally, A. (2018). Frequent Items on Streams. In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_169

Download citation

Publish with us

Policies and ethics