Skip to main content
SpringerLink
Log in

Large-Scale Learning from Data Streams with Apache SAMOA

  • Chapter
  • First Online:
Learning from Data Streams in Evolving Environments

Part of the book series: Studies in Big Data ((SBD,volume 41))

Abstract

Apache SAMOA (Scalable Advanced Massive Online Analysis) is an open-source platform for mining big data streams. Big data is defined as datasets whose size is beyond the ability of typical software tools to capture, store, manage, and analyze, due to the time and memory complexity. Apache SAMOA provides a collection of distributed streaming algorithms for the most common data mining and machine learning tasks such as classification, clustering, and regression, as well as programming abstractions to develop new algorithms. It features a pluggable architecture that allows it to run on several distributed stream processing engines such as Apache Flink, Apache Storm, and Apache Samza. Apache SAMOA is written in Java and is available at https://samoa.incubator.apache.org under the Apache Software License version 2.0.

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

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
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

Notes

  1. 1.

    http://hadoop.apache.org.

  2. 2.

    http://mahout.apache.org.

  3. 3.

    http://storm.apache.org.

  4. 4.

    http://flink.apache.org.

  5. 5.

    http://samza.apache.org.

  6. 6.

    https://apex.apache.org.

  7. 7.

    http://jubat.us/en.

  8. 8.

    http://github.com/vpa1977/stormmoa.

  9. 9.

    https://github.com/samoa-moa/samoa-moa.

  10. 10.

    http://moa.cms.waikato.ac.nz/datasets/,http://osmot.cs.cornell.edu/kddcup/datasets.html.

  11. 11.

    http://moa.cms.waikato.ac.nz.

  12. 12.

    http://kt.ijs.si/elena_ikonomovska/data.html.

  13. 13.

    http://www.openstack.org.

  14. 14.

    http://samza.incubator.apache.org.

  15. 15.

    http://hadoop.apache.org.

  16. 16.

    http://kafka.apache.org.

  17. 17.

    http://zookeeper.apache.org.

References

  1. Aggarwal, C.C.: Data Streams: Models and Algorithms. Springer, Berlin (2007)

    Book  Google Scholar 

  2. Bache, K., Lichman, M.: UCI machine learning repository (2013). http://archive.ics.uci.edu/ml

    Google Scholar 

  3. Ben-Haim, Y., Tom-Tov, E.: A streaming parallel decision tree algorithm. J. Mach. Learn. Res. 11, 849–872 (2010). ISSN 1532–4435. http://dl.acm.org/citation.cfm?id=1756006.1756034

  4. Bifet, A., Holmes, G., Kirkby, R., Pfahringer, B.: MOA: Massive online analysis. J. Mach. Learn. Res. 11, 1601–1604 (2010)

    Google Scholar 

  5. Bordino, I., Kourtellis, N., Laptev, N., Billawala, Y.: Stock trade volume prediction with Yahoo Finance user browsing behavior. In: 30th International Conference on Data Engineering (ICDE), pp. 1168–1173. IEEE, New York (2014)

    Google Scholar 

  6. Chatzakou, D., Kourtellis, N., Blackburn, J., De Cristofaro, E., Stringhini, G., Vakali, A.: Mean birds: detecting aggression and bullying on Twitter. In: 9th International Conference on Web Science (WebSci). ACM, New York (2017)

    Google Scholar 

  7. Chen, C., Zhang, J., Chen, X., Xiang, Y., Zhou, W.: 6 million spam tweets: a large ground truth for timely Twitter spam detection. In: International Conference on Communications (ICC). IEEE, New York (2015)

    Google Scholar 

  8. De Francisci Morales, G.: SAMOA: a platform for mining big data streams. In: RAMSS: 2nd International Workshop on Real-Time Analysis and Mining of Social Streams @WWW (2013)

    Google Scholar 

  9. De Francisci Morales, G., Bifet, A.: SAMOA: scalable advanced massive online analysis. J. Mach. Learn. Res. 16, 149–153 (2015)

    Google Scholar 

  10. De Francisci Morales, G., Gionis, A., Lucchese, C.: From chatter to headlines: harnessing the real-time web for personalized news recommendation. In: 5th ACM International Conference on Web Search and Data Mining (WSDM), pp. 153–162. ACM, New York (2012)

    Google Scholar 

  11. Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. In: 6th Symposium on Operating Systems Design and Implementation (OSDI), pp. 137–150. USENIX Association, Berkeley (2004)

    Google Scholar 

  12. Devooght, R., Kourtellis, N., Mantrach, A.: Dynamic matrix factorization with priors on unknown values. In: 21st International Conference on Knowledge Discovery and Data Mining (SIGKDD), pp. 189–198. ACM, New York (2015)

    Google Scholar 

  13. Domingos, P., Hulten, G.: Mining high-speed data streams. In: 6th International Conference on Knowledge Discovery and Data Mining (KDD), pp. 71–80 (2000)

    Google Scholar 

  14. Gama, J., Sebastião, R., Rodrigues, P.P.: On evaluating stream learning algorithms. Mach. Learn. 90(3), 317–346 (2013)

    Article  MathSciNet  Google Scholar 

  15. Gama, J., Zliobaite, I., Bifet, A., Pechenizkiy, M., Bouchachia, A.: A survey on concept drift adaptation. ACM Comput. Surv. 46(4), 13–30 (2014)

    Article  Google Scholar 

  16. Hoeffding, W.: Probability inequalities for sums of bounded random variables. J. Am. Stat. Assoc. 58(301), 13–30 (1963). http://amstat.tandfonline.com/doi/abs/10.1080/01621459.1963.10500830

    Article  MathSciNet  Google Scholar 

  17. Ikonomovska, E., Gama, J., Džeroski, S.: Learning model trees from evolving data streams. Data Min. Knowl. Disc. 23(1), 128–168 (2011)

    Article  MathSciNet  Google Scholar 

  18. Jacobs, A.: The pathologies of big data. Commun. ACM 52(8), 36–44 (2009)

    Article  Google Scholar 

  19. Kourtellis, N., Bonchi, F., De Francisci Morales, G.: Scalable online betweenness centrality in evolving graphs. IEEE Trans. Knowl. Data Eng. 27(9), 2494–2506 (2015)

    Article  Google Scholar 

  20. Kourtellis, N., De Francisci Morales, G., Bifet, A.: VHT: vertical hoeffding tree. In: 4th IEEE International Conference on Big Data (BigData) (2016)

    Google Scholar 

  21. Oza, N.C., Russell, S.: Online bagging and boosting. In: Artificial Intelligence and Statistics, pp. 105–112. Morgan Kaufmann, Los Altos (2001)

    Google Scholar 

  22. Page, E.: Continuous inspection schemes. Biometrika 41(1–2), 100–115 (1954)

    Article  MathSciNet  Google Scholar 

  23. Thu Vu, A., De Francisci Morales, G., Gama, J., Bifet, A.: Distributed adaptive model rules for mining big data streams. In: 2nd IEEE International Conference on Big Data (BigData) (2014)

    Google Scholar 

  24. Uddin Nasir, M.A., De Francisci Morales, G., Garcia-Soriano, D., Kourtellis, N., Serafini, M.: The power of both choices: practical load balancing for distributed stream processing engines. In: 31st International Conference on Data Engineering (ICDE) (2015)

    Google Scholar 

  25. Uddin Nasir, M.A., De Francisci Morales, G., Kourtellis, N., Serafini, M.: When two choices are not enough: balancing at scale in distributed stream processing. In: 32nd International Conference on Data Engineering (ICDE) (2016)

    Google Scholar 

  26. Vasiloudis, T., Beligianni, F., De Francisci Morales, G.: BoostVHT: boosting distributed streaming decision trees. In: 26th ACM International Conference on Information and Knowledge Management (CIKM) (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Telefonica Research, Barcelona, Spain

    Nicolas Kourtellis

  2. Qatar Computing Research Institute, Doha, Qatar

    Gianmarco De Francisci Morales

  3. LTCI, Télécom ParisTech, Paris, France

    Albert Bifet

Authors
  1. Nicolas Kourtellis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gianmarco De Francisci Morales
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Albert Bifet
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nicolas Kourtellis .

Editor information

Editors and Affiliations

  1. Institute Mines-Telecom Lille Douai, Douai, France

    Moamar Sayed-Mouchaweh

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer International Publishing AG, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Kourtellis, N., De Francisci Morales, G., Bifet, A. (2019). Large-Scale Learning from Data Streams with Apache SAMOA. In: Sayed-Mouchaweh, M. (eds) Learning from Data Streams in Evolving Environments. Studies in Big Data, vol 41. Springer, Cham. https://doi.org/10.1007/978-3-319-89803-2_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-89803-2_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-89802-5

  • Online ISBN: 978-3-319-89803-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation