Skip to main content
SpringerLink
Log in

High-Speed Big Data Streams: A Literature Review

  • Conference paper
  • First Online:
Second International Conference on Computer Networks and Communication Technologies (ICCNCT 2019)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 44))

  • 1430 Accesses

Abstract

In today’s word, high-speed data streams are continuously generated via a variety of sources like social media and organizational business related data. We have listed the basic characteristics of big data and challenges in handling big data and data streams. This paper shows present work on processing and analyzing big data and data streams, real-time data analytics, decision making, and business intelligence. Our aim is to research different trends in distributed data analysis, a study on security of big data, applications of big data and processing of data streams. Even though there is vast research happening in the field of big data across the globe, still there is a scope of improvement in this field.

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 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

Institutional subscriptions

References

  1. Zicari, R.V.: Big data: challenges and opportunities. Big Data Comput. 564, 104–110 (2014)

    Google Scholar 

  2. Blumberg, R., Atre, S.: The problem with unstructured data, pp. 42–46 (2013). http://soquelgroup.com/Articles/dmreview_0203_problem.pdf. Accessed 1 July 2012

  3. Li, M., et al.: Conditional random field for text segmentation from images with complex background. Pattern Recognit. Lett. 31(14), 2295–2308 (2010)

    Google Scholar 

  4. Jaseena, K.U., David, J.M.: Issues, challenges, and solutions: big data mining. CS & IT-CSCP 4(13), 131–140 (2014)

    Google Scholar 

  5. Hu, Q., Zhang, Y.: An effective selecting approach for social media big data analysis—taking commercial hotspot exploration with Weibo check-in data as an example. In: 2018 IEEE 3rd International Conference on Big Data Analysis (ICBDA), pp. 28–32. IEEE (2018)

    Google Scholar 

  6. HongJu, X., et al.: Some key problems of data management in army data engineering based on big data. In: 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA), pp. 149–152. IEEE (2017)

    Google Scholar 

  7. McHugh, J., et al.: Integrated access to big data polystores through a knowledge-driven framework. In: 2017 IEEE International Conference on Big Data (Big Data), pp. 1494–1503. IEEE (2017)

    Google Scholar 

  8. Moon, H., et al.: Ecosystem design of big data through previous study analysis in the world: policy design for big data as public goods. In: 2017 IEEE International Congress on Big Data (BigData Congress), pp. 525–528 IEEE (2017)

    Google Scholar 

  9. Yang, L., Zhang, J.-J.: Realistic plight of enterprise decision-making management under big data background and coping strategies. In: 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA), pp 402–405. IEEE (2017)

    Google Scholar 

  10. Cho, S., Hong, S., Lee, C.: ORANGE: spatial big data analysis platform. In: 2016 IEEE International Conference on Big Data (Big Data), pp. 3963–3965. IEEE (2016)

    Google Scholar 

  11. Strang, K.D., Sun, Z.: Meta-analysis of big data security and privacy: scholarly literature gaps. In: 2016 IEEE International Conference on Big Data (Big Data), pp. 4035–4037. IEEE (2016)

    Google Scholar 

  12. Rong, H., et al.: Privacy-preserving k-nearest neighbor computation in multiple cloud environments. IEEE Access 4, 9589–9603 (2016)

    Google Scholar 

  13. Zhang, S., et al.: Impacts of public transportation fare reduction policy on urban public transport sharing rate based on big data analysis. In: 2018 IEEE 3rd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), pp. 280–284. IEEE (2018)

    Google Scholar 

  14. Balan, S., et al.: Big data analysis of youth tobacco smoking trends in the United States. In: 2017 IEEE International Conference on Big Data (Big Data), pp. 4727–4729. IEEE (2017)

    Google Scholar 

  15. Xianglan, L.: Digital construction of coal mine big data for different platforms based on life cycle. In: 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA), pp. 456–459. IEEE (2017)

    Google Scholar 

  16. Kang, D., et al.: Energy information analysis using data algorithms based on big data platform. In: 2016 IEEE 18th International Conference on High Performance Computing and Communications; IEEE 14th International Conference on Smart City; IEEE 2nd International Conference on Data Science and Systems (HPCC/SmartCity/DSS), pp. 1530–1531. IEEE (2016)

    Google Scholar 

  17. Ramírez-Gallego, S., et al.: Nearest neighbor classification for high-speed big data streams using spark. IEEE Trans. Syst. Man Cybern.: Syst. 47(10), 2727–2739 (2017)

    Google Scholar 

  18. Yang, A., et al.: The research of policy big data retrieval and analysis based on elastic search. In: 2018 International Conference on Artificial Intelligence and Big Data (ICAIBD), pp. 43–46. IEEE (2018)

    Google Scholar 

  19. Awaghad, S.: SCEM: smart & effective crowd management with a novel scheme of big data analytics. In: 2016 IEEE International Conference on Big Data (Big Data), pp. 2000–2003. IEEE (2016)

    Google Scholar 

  20. Chen, J., et al.: Study of data analysis model based on big data technology. In: 2016 IEEE International Conference on Big Data Analysis (ICBDA), pp. 3–6. IEEE (2016)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Rajarambapu Institute of Technology Rajaramnagar, Sangli, Islampur, 415414, Maharashtra, India

    R. Patil Sneha & V. Dharwadkar Nagaraj

Authors
  1. R. Patil Sneha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. V. Dharwadkar Nagaraj
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to R. Patil Sneha .

Editor information

Editors and Affiliations

  1. Department of Computer Science Engineering, RVS Technical Campus, Coimbatore, Tamil Nadu, India

    S. Smys

  2. University of the Ryukyus, Okinawa, Japan

    Tomonobu Senjyu

  3. Department of Telecommunication Engineering, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic

    Pavel Lafata

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

Patil Sneha, R., Dharwadkar Nagaraj, V. (2020). High-Speed Big Data Streams: A Literature Review. In: Smys, S., Senjyu, T., Lafata, P. (eds) Second International Conference on Computer Networks and Communication Technologies. ICCNCT 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 44. Springer, Cham. https://doi.org/10.1007/978-3-030-37051-0_36

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-37051-0_36

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-37050-3

  • Online ISBN: 978-3-030-37051-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation