Skip to main content
SpringerLink
Log in
Book cover

International Conference on Artificial Intelligence and Symbolic Computation

AI2SD 2019: Advanced Intelligent Systems for Sustainable Development (AI2SD’2019) pp 68–80Cite as

Current State and Challenges of Big Data

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 92))

Abstract

Big Data is the complex, bulky, growing set of data coming from independent sources. In today’s modern age Big data has an essential part in nearly every field of human life including science, engineering, social, biological and biomedical departments. In the following paper importance of big data, stream learning, deep learning, Hadoop and its application are discussed.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
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

Learn about institutional subscriptions

Notes

  1. 1.

    https://www.apple.com/siri/.

  2. 2.

    https://www.microsoft.com/en-us/cortana.

  3. 3.

    https://assistant.google.com/.

References

  1. Labrinidis, A., Jagadish, H.: Challenges and opportunities with big data. Proc. VLDB Endowment 5, 2032–2033 (2012). https://doi.org/10.14778/2367502.2367572

    Article  Google Scholar 

  2. Stouky, A., Jaoujane, B., Daoudi, R., Chaoui, H.: Improving Software Automation Testing Using Jenkins, and Machine Learning Under Big Data (2019). Accessed 24 Apr 2019

    Google Scholar 

  3. Saraladevi, B., Pazhaniraja, N., Paul, P., et al.: Big data and hadoop-a study in security perspective. Procedia Comput. Sci. 50, 596–601 (2015). https://doi.org/10.1016/j.procs.2015.04.091

    Article  Google Scholar 

  4. Ji, C.: Big data processing in cloud computing environments. In: International Symposium on Pervasive Systems, Algorithms, and Networks

    Google Scholar 

  5. Song, Y.-S.: Storing Big Data- The rise of the Storage Cloud (2012)

    Google Scholar 

  6. Philip, R.: Managing Big Data. TDWI research (2013)

    Google Scholar 

  7. Hortonworks: What is Apache Spark. In: Hortonworks (2019). https://hortonworks.com/apache/spark/. Accessed 24 Apr 2019

  8. Shay, C.: Application Denial of Service. Hack tics Ltd (2007)

    Google Scholar 

  9. Daniel, J.: Understanding brute force. National Science Foundation, Chicago (2019)

    Google Scholar 

  10. Bifet, A., De Francisci, M.: Big data stream learning with SAMOA. In: IEEE International Conference on Data Mining Workshops, ICDMW 2015, pp. 1199–1202 (2015). https://doi.org/10.1109/ICDMW.2014.24

  11. Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)

    Article  MathSciNet  Google Scholar 

  12. Bengio, Y.: Learning deep architectures for AI. Found. Trends® Mach. Learn. 2(1), 1–127 (2009)

    Google Scholar 

  13. Bengio, Y., LeCun, Y.: Scaling learning algorithms towards AI. Large-Scale Kernel Mach. 34(5), 1–41 (2007)

    Google Scholar 

  14. Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013)

    Article  Google Scholar 

  15. Arel, I., Rose, D.C., Karnowski, T.P.: Deep machine learning-a new frontier in artificial intelligence research [research frontier]. IEEE Comput. Intell. Mag. 5(4), 13–18 (2010)

    Article  Google Scholar 

  16. Bengio, Y., Lamblin, P., Popovici, D., Larochelle, H.: Greedy layer-wise training of deep networks. Adv. Neural. Inf. Process. Syst. 19, 153 (2007)

    Google Scholar 

  17. Hordri, N.F., Yuhaniz, S.S., Shamsuddin, S.M.: Deep Learning and Its Applications: A Review (2016)

    Google Scholar 

  18. Jan, B., Farman, H., Khan, M., Imran, M., Islam, I., Ahmad, A., Ali, S., Jeon, G.: Deep learning in big data analytics: a comparative study. Comput. Electr. Eng. (2017). https://doi.org/10.1016/j.compeleceng.2017.12.009

    Article  Google Scholar 

  19. Demchenko, Y., Grosso, P., de Laat, C., Membrey, P.: Addressing big data issues in scientific data infrastructure. In: 2013 International Conference on Collaboration Technologies and Systems (CTS), San Diego, CA, pp. 48–55 (2015). https://doi.org/10.1109/cts.2013.6567203

  20. Louis Columbus Contributor @Forbes, 23 May 2018. https://www.forbes.com/sites/louiscolumbus/2018/05/23/10-charts-that-will-change-your-perspective-of-big-datas-growth/#2c9ba09b2926

  21. El-Gayar, O., Timsina, P.: Opportunities for business intelligence and big data analytics in evidence based medicine. In: 2014 47th Hawaii International Conference on System Sciences, Waikoloa, HI, pp. 749–757 (2014). https://doi.org/10.1109/hicss.2014.100

  22. Wu, J., Ota, K., Dong, M., Li, J., Wang, H.: Big data analysis-based security situational awareness for smart grid. IEEE Trans. Big Data 4(3), 408–417 (2018). https://doi.org/10.1109/tbdata.2016.2616146

  23. Alfred, R.: The rise of machine learning for big data analytics. In: 2016 2nd International Conference on Science in Information Technology (ICSITech), Balikpapan, p. 1 (2016). https://doi.org/10.1109/icsitech.2016.7852593

  24. Mazumder, S., Dhar, S.: Hadoop ecosystem as enterprise big data platform: perspectives and practices. Int. J. Inf. Technol. Manage. 17(4), 334–348 (2018). Inderscience Enterprises Ltd

    Google Scholar 

  25. Vora, M.N.: Hadoop-HBase for large-scale data. In: Proceedings of 2011 International Conference on Computer Science and Network Technology, Harbin, pp. 601–605 (2011). https://doi.org/10.1109/iccsnt.2011.6182030

  26. Mondal, K., Dutta, P.: Big data parallelism: challenges in different computational paradigms. In: Proceedings of the 2015 Third International Conference on Computer, Communication, Control and Information Technology (C3IT), Hooghly, pp. 1–5 (2015). https://doi.org/10.1109/c3it.2015.7060186

  27. Kasu, P., Kim, Y., Park, S., Atchley, S., Vallée, G.R.: Design and analysis of fault tolerance mechanisms for big data transfers. In: 2016 IEEE International Conference on Cluster Computing (CLUSTER), Taipei, pp. 138–139 (2016). https://doi.org/10.1109/cluster.2016.74

  28. Chawda, R.K., Thakur, G.: Big data and advanced analytics tools. In: 2016 Symposium on Colossal Data Analysis and Networking (CDAN), Indore, pp. 1–8 (2016). https://doi.org/10.1109/CDAN.2016.7570890

Download references

Author information

Authors and Affiliations

  1. Higher School of Technology, ISIC ESTM, LMMI Laboratory, ENSAM Moulay-Ismail University, Meknes, Morocco

    Yassine Benlachmi & Moulay Lahcen Hsnaoui

Authors
  1. Yassine Benlachmi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Moulay Lahcen Hsnaoui
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Yassine Benlachmi or Moulay Lahcen Hsnaoui .

Editor information

Editors and Affiliations

  1. Faculty of Sciences and Techniques of Tangier, Abdelmalek Essaâdi University, Tangier, Morocco

    Mostafa Ezziyyani

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Benlachmi, Y., Hsnaoui, M.L. (2020). Current State and Challenges of Big Data. In: Ezziyyani, M. (eds) Advanced Intelligent Systems for Sustainable Development (AI2SD’2019). AI2SD 2019. Lecture Notes in Networks and Systems, vol 92. Springer, Cham. https://doi.org/10.1007/978-3-030-33103-0_8

Download citation

Publish with us

Policies and ethics

Search

Navigation