Skip to main content
Springer Nature Link
Log in

Disruptive Innovation: Large Scale Multimedia Data Mining

  • Chapter
  • First Online:
Multimedia Data Mining and Analytics

  • 2639 Accesses

Abstract

This chapter gives an overview of multimedia data processing history as a sequence of Disruptive innovations and identifies the trends of its future development. Multimedia data processing and mining penetrates into all spheres of human life to improve efficiency of businesses and governments, facilitate social interaction, enhance sporting and entertainment events, and moderate further innovations in science, technology and arts. The disruptive innovations in mobile, social, cognitive, cloud and organic based computing will enable the current and future maturation of Multimedia data mining . The chapter concludes with an overview of the other chapters included in the book.

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
$34.99 /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
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    http://opendefinition.org/.

  2. 2.

    http://www.data.gov/.

  3. 3.

    http://www.nist.gov/.

  4. 4.

    http://www.top500.org/lists/, July 22, 2014.

References

  1. Kurzweil R (2005) The singularity is near: when humans transcend biology. Penguin Group, New York

    Google Scholar 

  2. Baker JM, Deng L, James G, Khudanpur S, Lee C-H, Morgan N, O’Shaughnessy D (2009) Research developments and directions in speech recognition and understanding, part 1. IEEE Signal Process Mag 26(3):75–80

    Article  Google Scholar 

  3. Hinton G, Deng L, Mohamed A-R, Jaitly N, Senior A, Vanhoucke V, Nguyen P, Sainath T, Dahl G, Kingsbury B (2012) Deep neural networks for acoustic modeling in speech recognition. IEEE Signal Process Mag 29(6):82–97

    Article  Google Scholar 

  4. Madriga AC (2012) How Google builds its maps—and what it means for the future of everything. The Atlantic, 6 September 2012

    Google Scholar 

  5. Hafner K, Lyon M (1996) Where wizards stay up late: the origins of the internet. Simon and Schuster Paperbacks, New York

    Google Scholar 

  6. Global Technology Outlook 2013 (2013) IBM research

    Google Scholar 

  7. The iPhone is not a smartphone (2007) Engadget.com, 9 January 2007. Retrieved 24 July 2014

    Google Scholar 

  8. T-mobile G1 event round-up (Press release) (2008) Talk media Inc. US, 22 October 2008. Retrieved 24 July 2014

    Google Scholar 

  9. Kessler S (2011) Facebook photos by the numbers. Mashable, Retrieved 23 July 2014

    Google Scholar 

  10. Yong JL, Ghosh J, Grauman K (2012) Discovering important people and objects for Egocentric video summarization. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), Providence, June 2012

    Google Scholar 

  11. Zheng L, Grauman K (2013) Story-driven summarization for Egocentric video. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), Portland, June 2013

    Google Scholar 

  12. Moore GE (1998) Cramming more components onto integrated circuits. Electronics 38(8):114–117

    Google Scholar 

  13. Dewdney AK (1998) On the Spaghetti computer and other analog gadgets for problem solving. Sci Am 250(6):19–26

    Google Scholar 

  14. OECD declaration on open access to publicly funded data (2014) http://www.oecd.org/science/sci-tech/sciencetechnologyandinnovationforthe21stcenturymeetingoftheoecdcommitteeforscientificandtechnologicalpolicyatministeriallevel29-30january2004-finalcommunique.htm, 21 July 2014

  15. Fountain JE (2001) Building the virtual state: information technology and institutional change, Brookings Institution Press, August 2001

    Google Scholar 

  16. CV datasets on the web (2014) www.cvpaper.com/datasets.html, 21 July 2014

  17. The babbage engine (2014) http://www.computerhistory.org/babbage/engines/, 22 July 2014

  18. The next generation: semiconductors (2014) http://www.computerhistory.org/revolution/digital-logic/12/272, 22 July 2014

  19. Kephart JO, David M (2003) Chess the vision of autonomic computing. Computer 36(1):41–50

    Article  MathSciNet  Google Scholar 

  20. de Castro, Leandro R, Jonathan T (2002) Artificial immune systems: a new computational intelligence paradigm. Springer, New York, September 2002

    Google Scholar 

  21. Holland JH (1992) Adaptation in natural and artificial systems [Book]. - [s.l.], 5th edn. MIT Press

    Google Scholar 

  22. Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning, reading. Addison-Wesley, Boston

    Google Scholar 

  23. Collins RJ (1992) Studies in artificial evolution. Dissertation, Doctor of Philosophy in Computer Science, University of California, Los Angeles

    Google Scholar 

  24. Gordon D (1999) Ants at work: how an insect society is organized. Free Press, New York

    Google Scholar 

  25. Sato M, Fukaya M, Iwasaki T (2002) Serpentine locomotion with robotic snakes. IEEE Control Syst 22(1):64–81

    Article  Google Scholar 

  26. Yasong L, Ausama A, Sameoto D, Menon C (2012) Abigaille ii: toward the development of a spider-inspired climbing robot. Robitica 30(1):79–89

    Article  Google Scholar 

  27. Merolla P, Arthur J, Akopyan F, Imam N, Manohar R, Modha DS (2011) A digital Neurosynaptic core using embedded crossbar memory with 45 pJ per spike in 45 nm. In: IEEE custom integrated circuits conference (CICC), San Jose

    Google Scholar 

  28. Christian M-S, Schmeck H, Ungerer T (eds) (2011) Organic computing—a paradigm shift for complex systems. Springer

    Google Scholar 

  29. Paun G, Rozenberg G, Salomaa A (1998) DNA computing: new computing paradigms. Springer, Berlin

    Book  MATH  Google Scholar 

  30. Church GM, Gao Y, Kosuri S (2012) Next-generation digital information storage in DNA. Science 337(6102):1628. doi:10.1126/science.1226355

    Article  Google Scholar 

  31. Gudemann M, Nafz F, Ortmeier F, Seebach H, Reif W (2008) A specification and construction paradigm for organic computing systems. In: IEEE self-adaptive and self-organizing systems (SASO)

    Google Scholar 

  32. Fey D, Komann M, Shurtz F, Loos A (2007) An organic computing architecture for visual microprocessors based on marching pixels. In: IEEE international symposium on circuits and systems (ICAS)

    Google Scholar 

  33. Seebach H, Ortmeier F, Reif W (2007) Design and construction of organic computing systems. In: IEEE congress on evolutionary computation

    Google Scholar 

  34. Wurtz RP (2007) Organic computing for video analysis. In: Hybrid Intelligent Systems

    Google Scholar 

  35. Wen X, Gu G, Li Q, Gao Y, Zhang X (2012) Comparison of open-source cloud management platforms: OpenStack and OpenNebula. In: 9th international conference on Fuzzy systems and knowledge discovery (FSKD). doi:10.1109/FSKD.2012.6234218

  36. DRAFT NIST big data interoperability framework: volume 1, definitions. NIST special publication, Information Technology Laboratory, Gaithersburg, 23 April 2014. http://jtc1bigdatasg.nist.gov/_uploadfiles/N0028_NBD-PWG_Vol1-Definitions_V1Draft_Pre-release.pdf

  37. Dean J, Ghemawat S (2008) MapReduce: simplified data processing on large clusters. Commun ACM 51(1):107–113

    Article  Google Scholar 

  38. Carney D, Cetintemel U, Cherniack M, Convey C, Lee S, Seidman G, Stonebraker M, Tatbul N, Zdonik S (2002) Monitoring streams: a new class of data management applications. VLDB’02

    Google Scholar 

  39. Chandrasekaran S, Cooper O, Deshpande A, Franklin MJ, Hellerstein JM, Hong W, Krishnamurthy S, Madden S, Raman V, Reiss F, Shah M (2003) TelegraphCQ: continuous dataflow processing for an uncertain world. CIDR

    Google Scholar 

  40. Balazinska M, Balakrishnan H, Madden SR, Stonebraker M (2008) Fault-tolerance in the Borealis distributed stream processing system. ACM Trans Database Syst, pp 13–24

    Google Scholar 

  41. Arasu A, Babcock B, Babu S, Datar M, Ito K, Nishizawa I, Rosenstein J, Widom J (2003) STREAM: the Stanford stream data management system. SIGMOD

    Google Scholar 

  42. Zaharia M, Chowdhury M, Das T, Dave A, Ma J, McCauley M, Franklin MJ, Shenker S, Stoica I (2002) Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing. NSDI 2012, April 2012

    Google Scholar 

  43. Ferrucci D (2012) Introduction to ‘This is Watson’. IBM J Res Dev 56(3/4), May/July 2012

    Google Scholar 

  44. Metropolis N, Howlett J, Rota G-C (eds) (1980) History of computing in the twentieth century. Academic Press, Orlando Florida

    MATH  Google Scholar 

  45. IBM System/360 (2014) http://www.computerhistory.org/revolution/mainframe-computers/7/161, 22 July 2014

  46. Intel’s Microprocessor (2014) http://www.computerhistory.org/revolution/digital-logic/12/285, 22 July 2014

  47. Murray C, Hoane Jr AJ, Hsu F (2002) Deep blue. Artif Intell 134:57–83

    Article  MATH  Google Scholar 

  48. Shah H (2011) Turing’s misunderstood imitation game and IBM’s Watson success. In: AISB 2011 Convention, University of York

    Google Scholar 

  49. Hayes P, Ford K (1995) Turing test considered harmful. In: Proceeding of 14th international joint conference artificial intelligence, vol 1, pp 972–977

    Google Scholar 

  50. Baughman AK, Chuang W, Dixon KR, Benz Z, Basilico J (2014) DeepQA Jeopardy! gamification: a machine-learning perspective. IEEE Trans Comput Intell AI Games 6(1):55–66

    Article  Google Scholar 

Download references

Acknowledgments

Special thanks to David McQueeney and Michele Merler for guidance and content review.

Author information

Authors and Affiliations

  1. IBM Corporation, 3039 Cornwallis Road, Research Triangle Park, NC, 27709, USA

    Aaron K. Baughman

  2. Google Inc., 1600 Amphitheatre Parkway, Mountain View, CA, 94043, USA

    Jia-Yu Pan

  3. Technologies, Nokia, Inc, 200 South Mathilda Avenue, Sunnyvale, CA, 94086, USA

    Jiang Gao

  4. Research and Development, 4i, Inc., 6256 Citracado Circle, Carlsbad, CA, 92009, USA

    Valery A. Petrushin

Authors
  1. Aaron K. Baughman
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jia-Yu Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jiang Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Valery A. Petrushin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Aaron K. Baughman .

Editor information

Editors and Affiliations

  1. IBM Corp., Durham, North Carolina, USA

    Aaron K. Baughman

  2. Nokia Inc., Sunnyvale, California, USA

    Jiang Gao

  3. Google Inc., Mountain View, California, USA

    Jia-Yu Pan

  4. 4i, Inc., Carlsbad, California, USA

    Valery A. Petrushin

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Baughman, A.K., Pan, JY., Gao, J., Petrushin, V.A. (2015). Disruptive Innovation: Large Scale Multimedia Data Mining. In: Baughman, A., Gao, J., Pan, JY., Petrushin, V. (eds) Multimedia Data Mining and Analytics. Springer, Cham. https://doi.org/10.1007/978-3-319-14998-1_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-14998-1_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14997-4

  • Online ISBN: 978-3-319-14998-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics