Skip to main content
SpringerLink
Log in
Book cover

Industrial Conference on Data Mining

ICDM 2014: Advances in Data Mining. Applications and Theoretical Aspects pp 214–227Cite as

Big Data Analytics: A Literature Review Paper

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8557))

Abstract

In the information era, enormous amounts of data have become available on hand to decision makers. Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be studied and provided in order to handle and extract value and knowledge from these datasets. Furthermore, decision makers need to be able to gain valuable insights from such varied and rapidly changing data, ranging from daily transactions to customer interactions and social network data. Such value can be provided using big data analytics, which is the application of advanced analytics techniques on big data. This paper aims to analyze some of the different analytics methods and tools which can be applied to big data, as well as the opportunities provided by the application of big data analytics in various decision domains.

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   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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Adams, M.N.: Perspectives on Data Mining. International Journal of Market Research 52(1), 11–19 (2010)

    Article  Google Scholar 

  2. Asur, S., Huberman, B.A.: Predicting the Future with Social Media. In: ACM International Conference on Web Intelligence and Intelligent Agent Technology, vol. 1, pp. 492–499 (2010)

    Google Scholar 

  3. Bakshi, K.: Considerations for Big Data: Architecture and Approaches. In: Proceedings of the IEEE Aerospace Conference, pp. 1–7 (2012)

    Google Scholar 

  4. Cebr: Data equity, Unlocking the value of big data. in: SAS Reports, pp. 1–44 (2012)

    Google Scholar 

  5. Cohen, J., Dolan, B., Dunlap, M., Hellerstein, J.M., Welton, C.: MAD Skills: New Analysis Practices for Big Data. Proceedings of the ACM VLDB Endowment 2(2), 1481–1492 (2009)

    Article  Google Scholar 

  6. Cuzzocrea, A., Song, I., Davis, K.C.: Analytics over Large-Scale Multidimensional Data: The Big Data Revolution! In: Proceedings of the ACM International Workshop on Data Warehousing and OLAP, pp. 101–104 (2011)

    Google Scholar 

  7. Economist Intelligence Unit: The Deciding Factor: Big Data & Decision Making. In: Capgemini Reports, pp. 1–24 (2012)

    Google Scholar 

  8. Elgendy, N.: Big Data Analytics in Support of the Decision Making Process. MSc Thesis, German University in Cairo, p. 164 (2013)

    Google Scholar 

  9. EMC: Data Science and Big Data Analytics. In: EMC Education Services, pp. 1–508 (2012)

    Google Scholar 

  10. He, Y., Lee, R., Huai, Y., Shao, Z., Jain, N., Zhang, X., Xu, Z.: RCFile: A Fast and Space-efficient Data Placement Structure in MapReduce-based Warehouse Systems. In: IEEE International Conference on Data Engineering (ICDE), pp. 1199–1208 (2011)

    Google Scholar 

  11. Herodotou, H., Lim, H., Luo, G., Borisov, N., Dong, L., Cetin, F.B., Babu, S.: Starfish: A Self-tuning System for Big Data Analytics. In: Proceedings of the Conference on Innovative Data Systems Research, pp. 261–272 (2011)

    Google Scholar 

  12. Kubick, W.R.: Big Data, Information and Meaning. In: Clinical Trial Insights, pp. 26–28 (2012)

    Google Scholar 

  13. Lee, R., Luo, T., Huai, Y., Wang, F., He, Y., Zhang, X.: Ysmart: Yet Another SQL-to-MapReduce Translator. In: IEEE International Conference on Distributed Computing Systems (ICDCS), pp. 25–36 (2011)

    Google Scholar 

  14. Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., Byers, A.H.: Big Data: The Next Frontier for Innovation, Competition, and Productivity. In: McKinsey Global Institute Reports, pp. 1–156 (2011)

    Google Scholar 

  15. Mouthami, K., Devi, K.N., Bhaskaran, V.M.: Sentiment Analysis and Classification Based on Textual Reviews. In: International Conference on Information Communication and Embedded Systems (ICICES), pp. 271–276 (2013)

    Google Scholar 

  16. Plattner, H., Zeier, A.: In-Memory Data Management: An Inflection Point for Enterprise Applications. Springer, Heidelberg (2011)

    Book  Google Scholar 

  17. Russom, P.: Big Data Analytics. In: TDWI Best Practices Report, pp. 1–40 (2011)

    Google Scholar 

  18. Sanchez, D., Martin-Bautista, M.J., Blanco, I., Torre, C.: Text Knowledge Mining: An Alternative to Text Data Mining. In: IEEE International Conference on Data Mining Workshops, pp. 664–672 (2008)

    Google Scholar 

  19. Serrat, O.: Social Network Analysis. Knowledge Network Solutions 28, 1–4 (2009)

    Google Scholar 

  20. Shen, Z., Wei, J., Sundaresan, N., Ma, K.L.: Visual Analysis of Massive Web Session Data. In: Large Data Analysis and Visualization (LDAV), pp. 65–72 (2012)

    Google Scholar 

  21. Song, Z., Kusiak, A.: Optimizing Product Configurations with a Data Mining Approach. International Journal of Production Research 47(7), 1733–1751 (2009)

    Article  Google Scholar 

  22. TechAmerica: Demystifying Big Data: A Practical Guide to Transforming the Business of Government. In: TechAmerica Reports, pp. 1–40 (2012)

    Google Scholar 

  23. Van der Valk, T., Gijsbers, G.: The Use of Social Network Analysis in Innovation Studies: Mapping Actors and Technologies. Innovation: Management, Policy & Practice 12(1), 5–17 (2010)

    Google Scholar 

  24. Zeng, D., Hsinchun, C., Lusch, R., Li, S.H.: Social Media Analytics and Intelligence. IEEE Intelligent Systems 25(6), 13–16 (2010)

    Article  Google Scholar 

  25. Zhang, L., Stoffel, A., Behrisch, M., Mittelstadt, S., Schreck, T., Pompl, R., Weber, S., Last, H., Keim, D.: Visual Analytics for the Big Data Era—A Comparative Review of State-of-the-Art Commercial Systems. In: IEEE Conference on Visual Analytics Science and Technology (VAST), pp. 173–182 (2012)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Business Informatics & Operations, German University in Cairo (GUC), Cairo, Egypt

    Nada Elgendy & Ahmed Elragal

Authors
  1. Nada Elgendy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ahmed Elragal
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Institute of Computer Vision and Applied Computer Sciences, IBaI, Leipzig, Germany

    Petra Perner

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Elgendy, N., Elragal, A. (2014). Big Data Analytics: A Literature Review Paper. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2014. Lecture Notes in Computer Science(), vol 8557. Springer, Cham. https://doi.org/10.1007/978-3-319-08976-8_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-08976-8_16

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08975-1

  • Online ISBN: 978-3-319-08976-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation