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Separator: Sifting Hierarchical Heavy Hitters Accurately from Data Streams

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Advanced Data Mining and Applications (ADMA 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4632))

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Abstract

In this paper, we present a new algorithm, Separator, for accurate and efficient Hierarchical Heavy Hitter (HHH) detection, an emerging research area of data stream mining. Existing algorithms exploit either bottom-up or top-down processing strategy to solve this problem, whereas we propose a novel combination of these two strategies. Based on this strategy and a devised compact data structure, we implement our algorithm. It is theoretically proved to have tight error bound and small space usage. Comprehensive experiments conducted also verify its accuracy and efficiency.

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References

  1. Gaber, M.M., Zaslavsky, A., Krishnaswamy, S.: Mining Data Streams: A Review. SIGMOD Record 2, 18–26 (2005)

    Article  Google Scholar 

  2. Cormode, G., Korn, F., Muthukrishnan, S., Srivastava, D.: Finding Hierarchical Heavy Hitters in Data Streams. In: Proc. 29th ACM VLDB, pp. 464–475 (2003)

    Google Scholar 

  3. Estan, C., Varghese, G.: New Directions in Traffic Measurement and Accounting. In: Proc. 1st ACM SIGCOMM Workshop on Internet Measurement, pp. 75–80 (2001)

    Google Scholar 

  4. Metwally, A., Agrawal, D., Abbadi, A.E.: Efficient Computation of Frequent and Top-k Elements in Data Streams. In: Eiter, T., Libkin, L. (eds.) ICDT 2005. LNCS, vol. 3363, pp. 398–412. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  5. Charikar, M., Chen, K., Farach-Colton, M.: Finding Frequent Items in Data Streams. In: Widmayer, P., Triguero, F., Morales, R., Hennessy, M., Eidenbenz, S., Conejo, R. (eds.) ICALP 2002. LNCS, vol. 2380, pp. 693–703. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  6. Manku, G., Motwani, R.: Approximate Frequency Counts over Data Streams. In: Proc. 28th ACM VLDB, pp. 346–357 (2002)

    Google Scholar 

  7. Demaine, E.D., López-Ortiz, A., Munro, J.I.: Frequency Estimation of Internet Packet Streams with Limited Space. In: Möhring, R.H., Raman, R. (eds.) ESA 2002. LNCS, vol. 2461, pp. 348–360. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  8. Cormode, G., Muthukrishnan, S.: Whats Hot and What’s Not: Tracking Most Frequent Items Dynamically. In: Proc. 22nd ACM PODS, pp. 296–306 (2003)

    Google Scholar 

  9. Cormode, G., Muthukrishnan, S.: An Improved Data Stream Summary: The Count-Min Sketch and Its Applications. In: Farach-Colton, M. (ed.) LATIN 2004. LNCS, vol. 2976, pp. 29–38. Springer, Heidelberg (2004)

    Google Scholar 

  10. Estan, C., Savage, S., Varghese, G.: Automatically Inferring Patterns of Resource Consumption in Network Traffic. Computer Communication Review. 4, 137–150 (2003)

    Google Scholar 

  11. Gibbons, P.B., Matias, Y.: Synopsis Data Structures for Massive Data Set. DIMACS Series in Discrete Mathematics and Theoretical Computer Science, 39–70 (1999)

    Google Scholar 

  12. Muthukrishnan, S.: Data Streams: Algorithms and Applications. In: ACM-SIAM Symp. Discrete Algorithms (2003), Available http://athos.rutgers.edu/m~uthu/stream-1-1.ps

  13. Zhang, Y., Singh, S., Sen, S., Duffield, N., Lund, C.: Online Identification of Hierarchical Heavy Hitters: Algorithms, Evaluation, and Applications. In: Proc. IMC, pp. 101–114 (2004)

    Google Scholar 

  14. Hershberger, J., Shrivastava, N., Suri, S., Toth, C.D.: Space Complexity of Hierarchical Heavy Hitters in Multi-Dimensional Data Streams. In: Proc. 24th PODS, pp. 338–347 (2005)

    Google Scholar 

  15. Cormode, G., Muthukrishnan, S.: Summarizing and Mining Skewed Data Streams. In: Proc. 5th SDM (2005)

    Google Scholar 

  16. Knuth, D.E.: The Art of Programming. Addison-Wesley, London (1973)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Dept. of Management Science and Engineering, Tsinghua University, Beijing 100084, China

    Yuan Lin & Hongyan Liu

Authors
  1. Yuan Lin
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  2. Hongyan Liu
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Editor information

Editors and Affiliations

  1. Computer Science Department, University of Calgary , Calgary, AB, Canada

    Reda Alhajj

  2. School of Computer Science and Technology , Harbin Institute of Technology, Harbin, China

    Hong Gao

  3. School of Computer Science and Technology , Harbin Institute of Technology , Harbin, China

    Jianzhong Li

  4. School of Information Technology and Electronic Engineering , The University of Queensland , Queensland, Australia

    Xue Li

  5. Department of Computing Science , University of Alberta, Edmonton, AB, Canada

    Osmar R. Zaïane

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© 2007 Springer Berlin Heidelberg

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Lin, Y., Liu, H. (2007). Separator: Sifting Hierarchical Heavy Hitters Accurately from Data Streams. In: Alhajj, R., Gao, H., Li, J., Li, X., Zaïane, O.R. (eds) Advanced Data Mining and Applications. ADMA 2007. Lecture Notes in Computer Science(), vol 4632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73871-8_17

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  • DOI: https://doi.org/10.1007/978-3-540-73871-8_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73870-1

  • Online ISBN: 978-3-540-73871-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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