Skip to main content
SpringerLink
Log in

Big Data Challenges and Issues: A Review

  • Conference paper
  • First Online:
Proceeding of the International Conference on Computer Networks, Big Data and IoT (ICCBI - 2018) (ICCBI 2018)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 31))

Included in the following conference series:

Abstract

Data is expanding immensely as well as colossally, multiplying each year. There is no denying the fact that data is and will keep on moulding our lives. Big Data can be thought of as the “development of perpetual information”. Big data is pulling in technologists, researchers, and analysts in the last couple of years in different areas of large databases. Big data gathers data from multiple distributed sources in large volumes which makes it a vital issue to process data accurately for better utilization and information quality. Big data poses great challenges in many areas. The paper relates to recent findings in big data science and technology.

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

Access this chapter

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 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
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

Institutional subscriptions

References

  1. Manyika, J., et al.: Big data: the next frontier for innovation, competition, and productivity, p. 1_137. McKinsey Global Institute, San Francisco (2011)

    Google Scholar 

  2. Kaiser, S., Armour, F., Espinosa, J.A., Money, W.: Big data: issues and challenges moving forward. In: 2013 46th Hawaii International Conference on Systems Science, pp. 995–1004 (2013)

    Google Scholar 

  3. Agrawal, D., Bernstein, P., Bertino, E.: Challenges and Opportunities with Big Data 2011-1 (2011)

    Google Scholar 

  4. Chen, M., Mao, S., Liu, Y.: Big Data: A Survey. Springer, New York (2014)

    Book  Google Scholar 

  5. Chen, J., Chen, Y., Du, X., Li, C., Lu, J., Zhao, S., Zhou, X.: Big Data challenge: a data management perspective. Front. Comput. Sci. 7(2), 157–164 (2013)

    Article  MathSciNet  Google Scholar 

  6. NIST Big Data Public Workinig Group: NIST Special Publication 1500- 4 NIST Big Data Interoperability Framework, Security and Privacy, vol. 4. September 2015

    Google Scholar 

  7. Murthy, P., Bharadwaj, A., Subrahmanyam, P., Roy, A., Rajan, S.: Big Data Taxonomy. Cloud Security. Alliance, no. September, p. 33 (2014)

    Google Scholar 

  8. Liu, A., et al.: Efficient secure similarity computation on encrypted trajectory data. In: 2015 IEEE 31st International Conference on Data Engineering (ICDE), pp. 66–77 (2015)

    Google Scholar 

  9. Chu, Y.W., et al.: Privacy-preserving SimRank over distributed Information network. In: 2012 IEEE 12th International Conference on Data Mining (ICDM) , pp. 840–845 (2012)

    Google Scholar 

  10. Bender, G., et al.: Explainable security for relational databases. In: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, SIGMOD 2014, pp. 1411–1422. ACM, New York (2014)

    Google Scholar 

  11. Meacham, A., Shasha, D.: JustMyFriends: full SQL, full transactional amenities, and access privacy. In: Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data, SIGMOD 2012, pp. 633–636. ACM, New York (2012)

    Google Scholar 

  12. Yang, K., Han, Q., Li, H., Zheng, K., Su, Z., Shen, X.: An efficient and fine-grained big data access control scheme with privacy-preserving policy. IEEE Internet Things J. 1–8 (2016)

    Google Scholar 

  13. Sudarshan, S., Jetley, R., Ramaswamy, S.: Security and privacy of big data. Studies in Big Data, pp. 121–136 (2015)

    Google Scholar 

  14. Jeong, Y., Shin, S.: An efficient authentication scheme to protect user privacy in seamless big data services. Wirel. Pers. Commun. 86(1), 7–19 (2015)

    Article  Google Scholar 

  15. Xiao, H., et al.: Is feature selection secure against training data poisoning? In: Proceedings of the 32nd International Conference on Machine Learning (ICML 2015), pp. 1689–1698 (2015)

    Google Scholar 

  16. Arasu, A., et al.: Secure database-as-a-service with cipherbase. In: Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data, SIGMOD 2013, pp. 1033–1036. ACM, New York (2013)

    Google Scholar 

  17. Lallali, S., et al.: A secure search engine for the personal cloud. In: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, SIGMOD 2015, pp. 1445–1450. ACM, New York (2015)

    Google Scholar 

  18. Xu, L., Shi, W.: Security theories and practices for big data. Big Data Concepts, Theories, and Applications, pp. 157–192 (2016)

    Google Scholar 

  19. Gao, Y., Fu, X., Luo, B., Du, X., Guizani, M.: Handle a framework for investigating data leakage attacks in hadoop. In: 2015 IEEE Global Communications Conference (GLOBECOM), San Diego, CA, pp. 1–6 (2015)

    Google Scholar 

  20. Puthal, D., Nepal, S., Ranjan, R., Chen, J.: A dynamic key length based approach for real-time security verification of big sensing data Stream. Lecture Notes in Computer Science, pp. 93–108 (2015)

    Google Scholar 

  21. Gorodov, E.Y., Gubarev, V.V.: Analytical review of data visualization methods in application to big data. J. Electr. Comput. Eng. 22 (2013)

    Google Scholar 

  22. To, H., et al.: PrivGeoCrowd: a toolbox for studying private spatial Crowdsourcing. In: 2015 IEEE 31st International Conference on Data Engineering (ICDE), pp. 1404–1407 (2015)

    Google Scholar 

  23. Lu, R., et al.: Toward efficient and privacy-preserving computing in big data era. IEEE Netw. 28(4), 46–50 (2014)

    Article  Google Scholar 

  24. Cao, J., Karras, P.: Publishing microdata with a robust privacy guarantee. Proc. VLDB Endow. 5(11), 1388–1399 (2012)

    Article  Google Scholar 

  25. Hu, X., et al.: Differential privacy in telco big data platform. Proc. VLDB Endow. 8(12), 1692–1703 (2015)

    Article  Google Scholar 

  26. Proserpio, D., et al.: Calibrating data to sensitivity in private data analysis: a platform for differentially private analysis of weighted dataset

    Google Scholar 

  27. McSherry, F.D.: Privacy integrated queries: an extensible platform for privacy-preserving data analysis. In: Proceedings of the 2009 ACM SIGMOD International Conference on Management of Data, pp. 19–30. ACM (2009)

    Google Scholar 

  28. Rahman, F., Ahamed, S., Yang, J., Wang, Q.: PriGen: a generic framework to preserve privacy of healthcare data in the cloud. In: Inclusive Society: Health and Wellbeing in the Community, and Care at Home, pp. 77–85 (2013)

    Google Scholar 

  29. Jain, P., Thakurta, A.: Differentially private learning with kernels. In: Proceedings of the 30th International Conference on Machine Learning (ICML 2013), pp. 118–126 (2013)

    Google Scholar 

  30. Elmehdwi, Y., et al.: Secure k-nearest neighbor query over encrypted data in outsourced environments. In: 2014 IEEE 30th International Conference on Data Engineering (ICDE), pp. 664–675 (2014)

    Google Scholar 

  31. Yao, B., et al.: Secure the nearest neighbor revisited. In: 2013 IEEE 29th International Conference on Data Engineering (ICDE), pp. 733–744 (2013)

    Google Scholar 

  32. Chung, W.: BizPro: intelligence factors from textual news articles. Int. J. Inf. Manag. 34(2), 272–284 (2014)

    Article  Google Scholar 

  33. Jiang, J.: Information extraction from text. In: Aggarwal, C.C., Zhai, C. (eds.) Mining text Data, pp. 11–41. Springer, New York (2012)

    Chapter  Google Scholar 

  34. Hahn, U., Mani, I.: The challenges of automatic summarization. Computer 33(11), 29–36 (2000)

    Article  Google Scholar 

  35. Liu, B.: Sentiment analysis and opinion mining. Synth. Lect. Hum. Lang. Technol. 5(1), 1–167 (2012)

    Google Scholar 

  36. Feldman, R.: Techniques and applications for sentiment analysis. Commun. ACM 56(4), 82–89 (2013)

    Article  Google Scholar 

  37. Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., et al.: Big data: the frontier for innovation, competition, and productivity. McKinsey Global Institute (2011). http://www.citeulike.org/group/18242/article/9341321

  38. Shockley, R., Smart, J., Romero-Morales, D., Tufano, P.: Gains with “big data,” according to a conducted on behalf of SAP (2012)

    Google Scholar 

  39. Patil, H.A.: “Crybaby”: assess neonatal health status from an infant’s cry. In: Neustein, A. (ed.) Advances in Speech Recognition, pp. 323–348. Springer, New York (2010)

    Chapter  Google Scholar 

  40. Hirschberg, J., Hjalmarsson, A., Elhadad, N.: “You’re as sick as you sound”: using computational approaches to gauge illness and recovery. In: Neustein, A. (ed.) Advances in Speech Recognition, pp. 305–322. Springer, New York (2010)

    Chapter  Google Scholar 

  41. Hu, W., Xie, N., Li, L., Zeng, X., Maybank, S.: A survey on visual content-based video indexing and retrieval. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 41(6), 797–819 (2011)

    Article  Google Scholar 

  42. Elser, B., Montresor, A.: An evaluation study of big data frameworks for graph processing. In: Proceedings of IEEE International Conference on Big Data, pp. 60–67. IEEE (2013)

    Google Scholar 

  43. Valiant, L.G.: A bridging model for parallelcomputation. Commun. ACM 33(8), 103–111 (1990). https://doi.org/10.1145/79173.79181

    Article  Google Scholar 

  44. Bertino, E., Ferrari, E.: Big Data Security and Privacy. Springer, Berlin (2018)

    Book  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science Department, Rajasthan Technical University, Kota, India

    Akanksha Mathur & C. P. Gupta

Authors
  1. Akanksha Mathur
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. C. P. Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Akanksha Mathur .

Editor information

Editors and Affiliations

  1. Department of CSE, Vaigai College of Engineering, Melur, India

    A. Pasumpon Pandian

  2. Department of Engineering, University of the Ryukyus, Okinawa, Japan

    Tomonobu Senjyu

  3. Edith Cowan University (ECU), Joondalup, WA, Australia

    Syed Mohammed Shamsul Islam

  4. Department of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA

    Haoxiang Wang

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

Mathur, A., Gupta, C.P. (2020). Big Data Challenges and Issues: A Review. In: Pandian, A.P., Senjyu, T., Islam, S.M.S., Wang, H. (eds) Proceeding of the International Conference on Computer Networks, Big Data and IoT (ICCBI - 2018). ICCBI 2018. Lecture Notes on Data Engineering and Communications Technologies, vol 31. Springer, Cham. https://doi.org/10.1007/978-3-030-24643-3_53

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-24643-3_53

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-24642-6

  • Online ISBN: 978-3-030-24643-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation