Skip to main content

Generalizing the Layering Method of Indyk and Woodruff: Recursive Sketches for Frequency-Based Vectors on Streams

  • Conference paper
Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX 2013, RANDOM 2013)

Abstract

In their ground-breaking paper, Indyk and Woodruff (STOC 05) showed how to compute the k-th frequency moment F k (for k > 2) in space O(poly − log(n,m) · n\(^{1-{2} \over{k}})\), giving the first optimal result up to poly-logarithmic factors in n and m (here m is the length of the stream and n is the size of the domain.) The method of Indyk and Woodruff reduces the problem of F k to the problem of computing heavy hitters in the streaming manner. Their reduction only requires polylogarithmic overhead in term of the space complexity and is based on the fundamental idea of “layering.” Since 2005 the method of Indyk and Woodruff has been used in numerous applications and has become a standard tool for streaming computations.

We propose a new recursive sketch that generalizes and improves the reduction of Indyk and Woodruff. Our method works for any non-negative frequency-based function in several models, including the insertion-only model, the turnstile model and the sliding window model. For frequency-based functions with sublinear polynomial space complexity our reduction only requires log(c)(n) overhead, where log(c)(n) is the iterative log function. Thus, we improve the reduction of Indyk and Woodruff by polylogarithmic factor. We illustrate the generality of our method by several applications: frequency moments, frequency based functions, spatial data streams and measuring independence of data sets.

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 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Alon, N., Matias, Y., Szegedy, M.: The space complexity of approximating the frequency moments. J. Comput. Syst. Sci. 58(1), 137–147 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  2. Andoni, A., Krauthgamer, R., Onak, K.: Streaming algorithms via precision sampling. In: FOCS, pp. 363–372 (2011)

    Google Scholar 

  3. Bar-Yossef, Z., Jayram, T.S., Kumar, R., Sivakumar, D.: An information statistics approach to data stream and communication complexity. J. Comput. Syst. Sci. 68(4), 702–732 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  4. Bar-Yossef, Z., Jayram, T.S., Kumar, R., Sivakumar, D., Trevisan, L.: Counting distinct elements in a data stream. In: Rolim, J.D.P., Vadhan, S.P. (eds.) RANDOM 2002. LNCS, vol. 2483, pp. 1–10. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  5. Beame, P., Jayram, T.S., Rudra, A.: Lower bounds for randomized read/write stream algorithms. In: STOC 2007: Proceedings of the Thirty-ninth Annual ACM Symposium on Theory of Computing, pp. 689–698. ACM, New York (2007)

    Chapter  Google Scholar 

  6. Bhuvanagiri, L., Ganguly, S., Kesh, D., Saha, C.: Simpler algorithm for estimating frequency moments of data streams. In: SODA, pp. 708–713 (2006)

    Google Scholar 

  7. Braverman, V., Gelles, R., Ostrovsky, R.: How to catch l 2-heavy-hitters on sliding windows. In: Du, D.-Z., Zhang, G. (eds.) COCOON 2013. LNCS, vol. 7936, pp. 638–650. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  8. Braverman, V., Ostrovsky, R.: Generalizing the layering method of indyk and woodruff: Recursive sketches for frequency-based vectors on streams. In: Raghavendra, P., Raskhodnikova, S., Jansen, K., Rolim, J.D.P. (eds.) APPROX/RANDOM 2013. LNCS, vol. 8096, pp. 58–70. Springer, Heidelberg (2013)

    Google Scholar 

  9. Braverman, V., Ostrovsky, R.: Smooth histograms for sliding windows. In: Proceedings of the 48th Annual IEEE Symposium on Foundations of Computer Science, FOCS 2007, pp. 283–293. IEEE Computer Society, Washington, DC (2007)

    Google Scholar 

  10. Braverman, V., Ostrovsky, R.: Measuring independence of datasets. In: Proceedings of the 42nd ACM Symposium on Theory of Computing, STOC 2010, pp. 271–280. ACM, New York (2010)

    Chapter  Google Scholar 

  11. Braverman, V., Ostrovsky, R.: Recursive sketching for frequency moments. CoRR, abs/1011.2571 (2010)

    Google Scholar 

  12. Braverman, V., Ostrovsky, R.: Zero-one frequency laws. In: Proceedings of the 42nd ACM Symposium on Theory of Computing, STOC 2010, pp. 281–290. ACM, New York (2010)

    Chapter  Google Scholar 

  13. Chakrabarti, A., Cormode, G., McGregor, A.: Robust lower bounds for communication and stream computation. In: STOC 2008: Proceedings of the 40th Annual ACM Symposium on Theory of Computing, pp. 641–650. ACM, New York (2008)

    Google Scholar 

  14. Chakrabarti, A., Khot, S., Sun, X.: Near-optimal lower bounds on the multi-party communication complexity of set disjointness. In: IEEE Conference on Computational Complexity, pp. 107–117 (2003)

    Google Scholar 

  15. 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 

  16. Coppersmith, D., Kumar, R.: An improved data stream algorithm for frequency moments. In: SODA, pp. 151–156 (2004)

    Google Scholar 

  17. Cormode, G., Datar, M., Indyk, P., Muthukrishnan, S.: Comparing data streams using hamming norms (how to zero in). IEEE Trans. on Knowl. and Data Eng. 15(3), 529–540 (2003)

    Article  Google Scholar 

  18. Feigenbaum, J., Kannan, S., Strauss, M., Viswanathan, M.: An approximate l1-difference algorithm for massive data streams. In: FOCS 1999: Proceedings of the 40th Annual Symposium on Foundations of Computer Science, p. 501. IEEE Computer Society, Washington, DC (1999)

    Google Scholar 

  19. Flajolet, P., Nigel Martin, G.: Probabilistic counting algorithms for data base applications. J. Comput. Syst. Sci. 31(2), 182–209 (1985)

    Article  MATH  Google Scholar 

  20. Ganguly, S.: Estimating frequency moments of data streams using random linear combinations. In: Jansen, K., Khanna, S., Rolim, J.D.P., Ron, D. (eds.) APPROX and RANDOM 2004. LNCS, vol. 3122, pp. 369–380. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  21. Ganguly, S., Cormode, G.: On estimating frequency moments of data streams. In: Charikar, M., Jansen, K., Reingold, O., Rolim, J.D.P. (eds.) APPROX and RANDOM 2007. LNCS, vol. 4627, pp. 479–493. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  22. Indyk, P.: Stable distributions, pseudorandom generators, embeddings, and data stream computation. J. ACM 53(3), 307–323 (2006)

    Article  MathSciNet  Google Scholar 

  23. Indyk, P., Woodruff, D.L.P.: Optimal approximations of the frequency moments of data streams. In: STOC 2005: Proceedings of the Thirty-seventh Annual ACM Symposium on Theory of Computing, pp. 202–208. ACM, New York (2005)

    Chapter  Google Scholar 

  24. Jayram, T.S., McGregor, A., Muthukrishnan, S., Vee, E.: Estimating statistical aggregates on probabilistic data streams. In: PODS 2007: Proceedings of the Twenty-sixth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, pp. 243–252. ACM, New York (2007)

    Chapter  Google Scholar 

  25. Kane, D.M., Nelson, J., Woodruff, D.P.: On the exact space complexity of sketching and streaming small norms. In: Proceedings of the 21st Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2010 (2010)

    Google Scholar 

  26. Kane, D.M., Nelson, J., Woodruff, D.P.: An optimal algorithm for the distinct elements problem. In: PODS 2010: Proceedings of the Twenty-ninth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems of Data, pp. 41–52. ACM, New York (2010)

    Chapter  Google Scholar 

  27. Li, P.: Compressed counting. In: SODA 2009: Proceedings of the Nineteenth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 412–421. Society for Industrial and Applied Mathematics, Philadelphia (2009)

    Google Scholar 

  28. Nelson, J., Woodruff, D.P.: Fast manhattan sketches in data streams. In: PODS 2010: Proceedings of the Twenty-ninth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems of Data, pp. 99–110. ACM, New York (2010)

    Chapter  Google Scholar 

  29. Tirthapura, S., Woodruff, D.P.: Rectangle-efficient aggregation in spatial data streams. In: Proceedings of the 31st Symposium on Principles of Database Systems, PODS 2012, pp. 283–294. ACM, New York (2012)

    Chapter  Google Scholar 

  30. Woodruff, D.P.: Optimal space lower bounds for all frequency moments. In: SODA 2004: Proceedings of the Fifteenth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 167–175 (2004)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Braverman, V., Ostrovsky, R. (2013). Generalizing the Layering Method of Indyk and Woodruff: Recursive Sketches for Frequency-Based Vectors on Streams. In: Raghavendra, P., Raskhodnikova, S., Jansen, K., Rolim, J.D.P. (eds) Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques. APPROX RANDOM 2013 2013. Lecture Notes in Computer Science, vol 8096. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40328-6_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-40328-6_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40327-9

  • Online ISBN: 978-3-642-40328-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics