Skip to main content
SpringerLink
Log in

The Challenges and Promises of Big Data—An Engineering Perspective

  • Conference paper
  • First Online:
Advanced Manufacturing and Automation VII (IWAMA 2017)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 451))

Included in the following conference series:

  • 2879 Accesses

Abstract

The term “big data” have been used for the analysis of data with high volume and veracity. Typically the size of the data is so large that commercial processing software, such as Excel and SPSS is inadequate to deal with them. This paper evaluates and examines the proper means of applications in which big data can be successfully deployed. Special attention is given to product design, which is one of the most recent of the big data approaches. Current trends and recent developments in big data analytics research are also discussed. The paper concludes with a summary of some of the key research issues in big data analyses.

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 299.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 379.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 379.99
Price excludes VAT (USA)
  • Durable hardcover 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

Similar content being viewed by others

References

  1. Lohr S (2013) The origins of ‘big data’: an etymological detective story. New York Times. http://bits.blogs.nytimes.com/2013/02/01/the-origins-of-big-data-an-etymological-detective-story/. Retrieved 28 Aug 2017

  2. Snijders C, Matzat U, Reips U-D (2012) ‘Big data’: big gaps of knowledge in the field of internet. Int J Internet Sci 7:1–5

    Google Scholar 

  3. Dedić N, Stanier C (2017) Towards differentiating business intelligence, big data, data analytics and knowledge discovery (285). Springer International Publishing, Berlin, Heidelberg

    Google Scholar 

  4. Everts Sarah (2016) Information overload. Distillations 2(2):26–33

    Google Scholar 

  5. Ibrahim TH, Abaker Y, Ibrar BA, Nor MS, Gani A, Ullah Khan S (2015) “Big data” on cloud computing. Inform Syst 47:98–115

    Google Scholar 

  6. Laney D (2011) 3D data management: controlling data volume, velocity and variety (PDF). http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf. Retrieved 6 Feb 2017

  7. Beyer M (2011) Solving ‘big data’ challenge involves more than just managing volumes of data. http://www.gartner.com/it/page.jsp?id=1731916. Retrieved 13 July 2017

  8. De Mauro A, Greco M, Grimaldi M (2016) “A formal definition of big data based on its essential features”. Libr Rev 65:122–135

    Google Scholar 

  9. Grimes S (2011) Big data: avoid ‘Wanna V’ confusion. InformationWeek. http://www.informationweek.com/big-data/big-data-analytics/big-data-avoid-wanna-v-confusion/d/d-id/1111077? Retrieved 5 Jan 2017

  10. Hilbert M, López P (2011) The world’s technological capacity to store, communicate, and compute information. Science 332(6025):60–65

    Article  Google Scholar 

  11. Kimble C, Milolidakis G (2015) Big data and business intelligence: debunking the myths. Glob Bus Organ Excellence 35(1):23–34

    Article  Google Scholar 

  12. Anderson C (2008) The end of theory: the data deluge makes the scientific method obsolete. WIRED. https://www.wired.com/science/discoveries/magazine/16-07/pb_theory. Retrieved 5 Jan 2017

  13. Graham M (2012) Big data and the end of theory?. The Guardian. London. https://www.theguardian.com/news/datablog/2012/mar/09/big-data-theory. Retrieved 5 Jan 2017

  14. Shah S, Horne A, Capellá J (2012) Good data won’t guarantee good decisions. Harvard Bus Rev 35(1):23–34

    Google Scholar 

  15. Hilbert M (2014) Big data requires big visions for big change, London: organized TED talks. https://www.youtube.com/watch?v=UXef6yfJZAI. Retrieved 5 Jan 2017

  16. Rauch J (2002) Seeing around corners. The Atlantic. https://www.theatlantic.com/magazine/archive/2002/04/seeing-around-corners/302471. Retrieved 5 Mar 2017

  17. Epstein JM, Axtell RL (1996) Growing artificial societies: social science from the bottom up. A Bradford Book, UK

    Google Scholar 

  18. Delort P (2012) Big data in biosciences, big data Paris. http://www.bigdataparis.com/documents/Pierre-Delort-INSERM.pdf#page=5. Retrieved 5 Jan 2017

  19. Hawkins RD, Hon* GC, Ren B (2010) Next-generation genomics: an integrative approach. Nat Rev 11

    Google Scholar 

  20. Tambe SS (2015) “Big data in biosciences”, insights in biology—2025. CSIR-National Chemical Laboratory, Pune, India, pp 25–28

    Google Scholar 

  21. Ohm P (2012) Don’t build a database of ruin. Harvard Bus Rev. http://blogs.hbr.org/cs/2012/08/dont_build_a_database_of_ruin.html

  22. Wares F (2010) Failure to launch: from big data to big decisions. http://www.fortewares.com/Administrator/userfiles/Banner/forte-wares–pro-active-reporting_EN.pdf

  23. Pelt M (2015) “Big Data” is an over used buzzword and this Twitter bot proves it. siliconangle.com. SiliconANGLE. http://siliconangle.com/blog/2015/10/26/big-data-is-an-over-used-buzzword-and-this-twitter-bot-proves-it/

  24. Gregory P (2014) Interview: Michael Berthold, KNIME Founder, on research, creativity, big data, and privacy, part 2. KDnuggets. http://www.kdnuggets.com/2014/08/interview-michael-berthold-knime-research-big-data-privacy-part2.html

  25. Harford T (2014) Big data: are we making a big mistake? Financial Times. http://www.ft.com/cms/s/2/21a6e7d8-b479-11e3-a09a-00144feabdc0.html

  26. Ioannidis JPA (2005) Why most published research findings are false. PLoS Med 2(8):e124. PMC 1182327

    Google Scholar 

  27. Lohr S, Singer N (2016) How data failed us in calling an election. The New York Times. ISSN 0362-4331. https://www.nytimes.com/2016/11/10/technology/the-data-said-clinton-would-win-why-you-shouldnt-have-believed-it.html

  28. Markman J (2016) Big data and the 2016 election. Forbes. http://www.forbes.com/sites/jonmarkman/2016/08/08/big-data-and-the-2016-election/#4802f20846d7

  29. Calvanese D, Cogrel B, Komla-Ebri S, Kontchakov R, Lanti D, Rezk M, Rodriguez-Muro M, Xiao G (2017) Ontop: answering SPARQL queries over relational databases. Semant Web J 8:471–487

    Article  Google Scholar 

  30. Oracle semantic technologies developer’s guide 11 g release 2. Available online: http://docs.oracle.com/cd/E11882_01/appdev.112/e25609/title.htm. Accessed on 1 Dec 2016

  31. Mutch A (2010) Technology, organization and structure—a morphogenetic approach. Organ Sci 21(2):507–520

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Business, Plymouth University, Plymouth, UK

    Yi Wang

Authors
  1. Yi Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yi Wang .

Editor information

Editors and Affiliations

  1. Department of Production and Quality Engineering, Norwegian University of Science and Technology, Trondheim, Sør-Trøndelag Fylke, Norway

    Kesheng Wang

  2. School of Business, Plymouth University, Plymouth, United Kingdom

    Yi Wang

  3. Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology, Trondheim, Sør-Trøndelag Fylke, Norway

    Jan Ola Strandhagen

  4. Shanghai Second Polytechnic University, Shanghai, China

    Tao Yu

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wang, Y. (2018). The Challenges and Promises of Big Data—An Engineering Perspective. In: Wang, K., Wang, Y., Strandhagen, J., Yu, T. (eds) Advanced Manufacturing and Automation VII. IWAMA 2017. Lecture Notes in Electrical Engineering, vol 451. Springer, Singapore. https://doi.org/10.1007/978-981-10-5768-7_63

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-5768-7_63

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-5767-0

  • Online ISBN: 978-981-10-5768-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation