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Data sketching

Published:23 August 2017Publication History
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Abstract

The approximate approach is often faster and more efficient.

References

  1. Ahn, K.J., Guha, S., McGregor, A. Analyzing graph structure via linear measurements. In Proceedings of the ACM-SIAM Symposium on Discrete Algorithms, (2012). Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Bhagat, S., Burke, M., Diuk, C., Filiz, I.O., Edunov, S. Three-and-a-half degrees of separation. Facebook Research, 2016; https://research.fb.com/three-and-a-half-degrees-of-separation/.Google ScholarGoogle Scholar
  3. Bloom, B. Space/time trade-offs in hash coding with allowable errors. Commun. ACM 13, 7 (July 1970), 422--426. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Broder, M., Mitzenmacher, A. Network applications of Bloom filters: a survey. Internet Mathematics 1, 4 (2005), 485--509.Google ScholarGoogle Scholar
  5. Clarkson, K.L., Woodruff, D.P. Low rank approximation and regression in input sparsity time. In Proceedings of the ACM Symposium on Theory of Computing, (2013), 81--90. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Cormode, G., Korn, F., Muthukrishnan, S., Johnson, T., Spatscheck, O., Srivastava, D. 2004. Holistic UDAFs at streaming speeds. In Proceedings of the ACM SIGMOD International Conference on Management of Data, (2004), 35--46. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Cormode, G., Muthukrishnan, S. An improved data stream summary: the Count-Min sketch and its applications. J. Algorithms 55, 1 (2005), 58--75. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Flajolet, P., Martin, G.N. 1985. Probabilistic counting. In Proceedings of the IEEE Conference on Foundations of Computer Science, 1985, 76--82. Also in J. Computer and System Sciences 31, 182--209. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Guha, S., Mishra, N., Motwani, R., O'Callaghan, L. Clustering data streams. In Proceedings of the IEEE Conference on Foundations of Computer Science, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Heule, S., Nunkesser, M., Hall, A. HyperLogLog in practice: Algorithmic engineering of a state of the art cardinality estimation algorithm. In Proceedings of the International Conference on Extending Database Technology, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Jermaine, C. Sampling techniques for massive data. Synopses for massive data: samples, histograms, wavelets and sketches. Foundations and Trends in Databases 4, 1--3 (2012). G. Cormode, M. Garofalakis, P. Haas, and C. Jermaine, Eds. NOW Publishers. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Morris, R. Counting large numbers of events in small registers. Commun. ACM 21, 10 (Oct. 1977), 840--842. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Pike, R., Dorward, S., Griesemer, R., Quinlan, S. Interpreting the data: Parallel analysis with Sawzall. Dynamic Grids and Worldwide Computing 13, 4 (2005), 277--298. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Weinberger, K.Q., Dasgupta, A., Langford, J., Smola, A.J., Attenberg, J. Feature hashing for large-scale multitask learning. In Proceedings of the International Conference on Machine Learning, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Whang, K.Y., Vander-Zanden, B.T., Taylor, H.M. A linear-time probabilistic counting algorithm for database applications. ACM Trans. Database Systems 15, 2 (1990, 208. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Woodruff, D. Sketching as a tool for numerical linear algebra. Foundations and Trends in Theoretical Computer Science 10, 1--2 (2014), 1--157. Google ScholarGoogle ScholarDigital LibraryDigital Library

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  1. Data sketching

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      cover image Communications of the ACM
      Communications of the ACM  Volume 60, Issue 9
      September 2017
      94 pages
      ISSN:0001-0782
      EISSN:1557-7317
      DOI:10.1145/3134526
      Issue’s Table of Contents

      Copyright © 2017 ACM

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      Publication History

      • Published: 23 August 2017

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