Skip to main content

Scalable Analytics – Algorithms and Systems

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNISA,volume 7678)

Abstract

The amount of data collected is increasing and the time window to leverage this has been decreasing. To satisfy the twin requirements, both algorithms and systems have to keep pace. The goal of this tutorial is to provide an overview of the common problems, algorithms, and systems for handling large-scale analytics tasks.

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (Canada)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   54.99
Price excludes VAT (Canada)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   72.00
Price excludes VAT (Canada)
  • 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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Agarwal, A., Chapelle, O., Dudik, M., Langford, J.: A reliable effective terascale linear learning system (2012), http://arxiv.org/abs/1110.4198

  2. Ahmed, A., Low, Y., Aly, M., Josifovski, V., Smola, A.J.: Scalable distributed inference of dynamic user interests for behavioral targeting. In: KDD (2011)

    Google Scholar 

  3. Bottou, L., Bousquet, O.: The tradeoffs of large scale learning. In: Advances in Neural Information Processing Systems (2008)

    Google Scholar 

  4. Chu, C.-T., Kim, S.K., Lin, Y.-A., Yu, Y., Bradski, G.R., Ng, A.Y., Olukotun, K.: Map-Reduce for machine learning on multicore. In: NIPS (2006)

    Google Scholar 

  5. Kant, R., Sengamedu, S.H., Kumar, K.: Comment spam detection by sequence mining. In: WSDM (2011)

    Google Scholar 

  6. Kearns, M.: Efficient noise-tolerant learning from statistical queries. JACM (1998)

    Google Scholar 

  7. Lin, J.: MapReduce is good enough? if all you have is a hammer, throw away everything that’s not a nail! (2012), http://arxiv.org/abs/1209.2191

  8. Lin, J., Kolcz, A.: Large-scale machine learning at Twitter. In: SIGMOD (2012)

    Google Scholar 

  9. Low, Y., Gonzalez, J., Kyrola, A., Bickson, D., Guestrin, C., Hellerstein, J.: GraphLab: a new framework for parallel machine learning. In: UAI (2010)

    Google Scholar 

  10. Nair, V., Mahajan, D., Sellamanickam, S.: A unified approach to learning task-specific bit vector representations for fast nearest neighbor search. In: WWW (2012)

    Google Scholar 

  11. Niu, F., Recht, B., Re, C., Wright, S.J.: HOGWILD!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent. In: NIPS (2011)

    Google Scholar 

  12. Smola, A., Narayanamurthy, S.: An architecture for parallel topic models. In: VLDB (2010)

    Google Scholar 

  13. Weinberger, K., Dasgupta, A., Langford, J., Smola, A., Attenberg, J.: Feature hashing for large scale multitask learning. In: ICML (2009)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Sengamedu, S.H. (2012). Scalable Analytics – Algorithms and Systems. In: Srinivasa, S., Bhatnagar, V. (eds) Big Data Analytics. BDA 2012. Lecture Notes in Computer Science, vol 7678. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35542-4_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-35542-4_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35541-7

  • Online ISBN: 978-3-642-35542-4

  • eBook Packages: Computer ScienceComputer Science (R0)