SpringerLink
Log in

A Recent Study of Emerging Tools and Technologies Boosting Big Data Analytics

  • Conference paper
  • First Online:
Innovations in Computer Science and Engineering

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 413))

  • 955 Accesses

Abstract

Traditional technologies and data processing applications are inadequate for big data processing. Big Data concern very large-volume, complex formats, growing data sets with multiple, heterogeneous sources, and formats. With the reckless expansion in networking, communication, storage, and data collection capability, the big data science is rapidly growing in every engineering and science domain. Challenges in front of data scientists include different tasks, such as data capture, classification, storage, sharing, transfer, analysis, search, visualization, and decision making. This paper is aimed to discuss the need of big data analytics, journey of raw data to meaningful decision, and the different tools and technologies emerged to process the big data at different levels, to derive meaningful decisions out of it.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
EUR 29.95
Price includes VAT (Germany)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
EUR 213.99
Price includes VAT (Germany)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
EUR 267.49
Price includes VAT (Germany)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. X. Wu, X. Zhu, G.-Q. Wu, and W. Ding, “Data mining with big data,” IEEE Trans. Knowl. Data Eng., vol. 26, no. 1, pp. 97–107, 2014.

    Google Scholar 

  2. L. Wang, K. Lu, P. Liu, R. Ranjan, and L. Chen, “IK-SVD: Dictionary Learning for Spatial Big Data via Incremental Atom Update,” vol. XX, no. Xx, pp. 1–12, 2014.

    Google Scholar 

  3. M. Augier, “Sublime Simon: The consistent vision of economic psychology’s Nobel laureate,” J. Econ. Psychol., vol. 22, no. 3, pp. 307–334, 2001.

    Google Scholar 

  4. Y. Liu, B. Wu, H. Wang, and P. Ma, “BPGM : A Big Graph Mining Tool,” vol. 19, no. 1, 2014.

    Google Scholar 

  5. S. Meng, W. Dou, X. Zhang, J. Chen, and S. Member, “KASR : A Keyword-Aware Service Recommendation Method on MapReduce for Big Data Applications,” vol. 25, no. 12, pp. 1–11, 2013.

    Google Scholar 

  6. D. Takaishi, S. Member, H. Nishiyama, and S. Member, “in Densely Distributed Sensor Networks,” vol. 2, no. 3, 2014.

    Google Scholar 

  7. P. Shen and C. Li, “Distributed Information Theoretic Clustering,” vol. 62, no. 13, pp. 3442–3453, 2014.

    Google Scholar 

  8. Y. Wang, L. Chen, S. Member, and J. Mei, “Incremental Fuzzy Clustering With Multiple Medoids for Large Data,” vol. 22, no. 6, pp. 1557–1568, 2014.

    Google Scholar 

  9. M. Muja, “Scalable Nearest Neighbour Methods for High Dimensional Data,” vol. 36, no. April, pp. 2227–2240, 2013.

    Google Scholar 

  10. (2012), sqoop [online]. Available: https://sqoop.apache.org/docs/1.4.2/SqoopUserGuide.htm.

  11. W. Dou, X. Zhang, J. Liu, J. Chen, and S. Member, “HireSome -II : Towards Privacy-Aware Cross- Cloud Service Composition for Big Data Applications,” vol. 26, no. 2, pp. 1–11, 2013.

    Google Scholar 

  12. (2013), Flume [online]. Available: https://flume.apache.org/FlumeUserGuide.html.

  13. (2014), Zookeeper [online]. Available: https://zookeeper.apache.org/releases.html.

  14. (2013). HBase [Online]. Available: http://hbase.apache.org/.

  15. R. Cattell, “Scalable SQL and NoSQL data stores,’’ SIGMOD Rec., vol. 39, no. 4, pp. 12_27, 2011.

    Google Scholar 

  16. (2014), Gluster [online]. Available: http://www.gluster.org/.

  17. (2013). Hadoop Distributed File System [Online]. Available: http://hadoop.apache.org/docs/r1.0.4/hdfsdesign.html.

  18. S. Ghemawat, H. Gobioff, and S.-T. Leung, “The Google file system,’’ in Proc. 19th ACM Symp. Operating Syst. Principles, 2003, pp.29_43.

    Google Scholar 

  19. (2015), Infinispan [online]. Available: http://infinispan.org/documentation/.

  20. A. Thusoo et al., “Hive: A warehousing solution over a Map-Reduceframework,’’ Proc. VLDB Endowment, vol. 2, no. 2, pp. 1626_1629, 2009.

    Google Scholar 

  21. (2014), Lucene [online]. Available: https://lucene.apache.org/.

  22. (2013). Solr [Online]. Available: http://lucene.apache.org/solr/.

  23. (2013). Rapidminer [Online]. Available: https://rapidminer.com.

  24. (2015). Talend [Online]. Available: https://www.talend.com/.

  25. (2015). SpagoBI [Online]. Available: http://www.spagobi.org/.

  26. D. Breuker, “Towards Model-Driven Engineering for Big Data Analytics -- An Exploratory Analysis of Domain-Specific Languages for Machine Learning,” 2014 47th Hawaii Int. Conf. Syst. Sci., pp. 758–767, 2014.

    Google Scholar 

  27. S. J. Rysavy, D. Bromley, and V. Daggett, “DIVE: A graph-based visual-analytics framework for big data,” IEEE Comput. Graph. Appl., vol. 34, no. 2, pp. 26–37, 2014.

    Google Scholar 

  28. (2015). Orange [Online]. Available: http://orange.biolab.si/.

  29. P. Louridas and C. Ebert, “Embedded analytics and statistics for big data,” IEEE Softw., vol. 30, no. 6, pp. 33–39, 2013.

    Google Scholar 

  30. (2015). Storm [Online]. Available: http://storm-project.net/.

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, K L University, Guntur, A.P., India

    Govind Pole & Pradeepini Gera

Authors
  1. Govind Pole
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pradeepini Gera
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Govind Pole .

Editor information

Editors and Affiliations

  1. Guru Nanak Institutions, Professor & Managing Director, Ibrahimpatnam, Andhra Pradesh, India

    H. S. Saini

  2. Guru Nanak Institutions, Professor & Associate Director, Ibrahimpatnam, Andhra Pradesh, India

    Rishi Sayal

  3. Guru Nanak Institutions, Professor and Head – CSE and IT, Ibrahimpatnam, India

    Sandeep Singh Rawat

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer Science+Business Media Singapore

About this paper

Cite this paper

Pole, G., Gera, P. (2016). A Recent Study of Emerging Tools and Technologies Boosting Big Data Analytics. In: Saini, H., Sayal, R., Rawat, S. (eds) Innovations in Computer Science and Engineering. Advances in Intelligent Systems and Computing, vol 413. Springer, Singapore. https://doi.org/10.1007/978-981-10-0419-3_4

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-0419-3_4

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-0417-9

  • Online ISBN: 978-981-10-0419-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation