Skip to main content

Integration of Academic Research into Innovation Projects: The Case of Collaboration with a University Research Institute

  • Chapter
  • 1790 Accesses

Abstract

International academic institutions produce a rich pool of knowledge which is relevant for innovation processes. The challenge is to find an effective approach to make this knowledge accessible and usable on a larger scale. The structured approach to setting up cooperation between industry and academia described in this chapter helps transfer knowledge between those two parties, regardless of geographical distance.

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

Buying options

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
Hardcover Book
USD   54.99
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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • AOL and NCSA. 2005. AOL/NCSA Online Safety Study.

    Google Scholar 

  • Bauer, E. and Kohavi, R. 1999. An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants. Machine Learning 35: 1–38.

    Google Scholar 

  • Barthélemy, M., Barrat, A., Pastor-Satorras, R. and Vespignani, A. 2004. Velocity and Hierarchical Spread of Epidemic Outbreaks in Scale Free Networks. Phys. Rev. Lett 92.178701 (April).

    Article  Google Scholar 

  • Chen, L. C. and Carley, M. 2004. The Impact of Countermeasure Propagation on the Prevalence of Computer Viruses. IEEE Systems, Man and Cybernetics, Part B 34(1) (April): 823–833.

    Article  Google Scholar 

  • Christodorescu, M. and Jha, S. 2003. Static Analysis of Executables to Detect Malicious Patterns.” Proceedings of the 12th USENIX Security Symposium (Security’03), Washington DC, August 4–8: 169–186.

    Google Scholar 

  • Dharmapurikar, S. and Lockwood, J. W. 2006. Fast and Scalable Pattern Matching for Network Intrusion Detection Systems. IEEE Journal on Selected Areas in Communications 24(10) (October): 1781–1792.

    Article  Google Scholar 

  • Elovici, Y., Shabtai, A., Moskovitch, R., Tahan, G. and Glezer, C. 2007. Applying Machine Learning Techniques to Detect Malicious Code in Network Traffic. 30th Annual German Conference on Artificial Intelligence (KI-2007), Osnabrück, Germany, Sep. 10–13.

    Google Scholar 

  • Englert, R. and Joost, G. 2007. Design and Usability for Personalized User Interfaces of Telecommunication Services. 16th International Conference on Engineering Design (ICED07), Paris, France.

    Google Scholar 

  • Ertoz, L., Eilertson, E., Lazarevic, A., Tan, P., Srivastava, J., Kumar, V. and Dokas, P. 2004. The MINDS – Minnesota Intrusion Detection System, Next Generation Data Mining. MIT Press.

    Google Scholar 

  • Gassmann, O. and Enkel, E. 2004. Towards a Theory of Open Innovation: Three Core Process Archetypes. Proceedings of the R&D Management Conference.

    Google Scholar 

  • Golub, T. et al. 1999. Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring. Science 286: 531–537.

    Article  Google Scholar 

  • Hariri, S., Qu, G., Dharmagadda, T. and Ramkishore, M. 2003. Vulnerability Analysis of Faults and Attacks in Large-scale Networks. IEEE Security and Privacy Magazine (October & November): 49–54.

    Google Scholar 

  • Hauschildt. 1997. Innovationsmanagement. Munich: Vahlen.

    Google Scholar 

  • Jiang, J. and Papavassiliou, S. 2004. Detecting Network Attacks in the Internet via Statistical Network Traffic Normality Prediction. Journal of Networks and Systems Management 12(1): 51–72.

    Article  Google Scholar 

  • Jung, J., Schechter, St. and Berger, A. 2004 Fast Detection of Scanning Worm Infections. LNCS 3224: 59–81.

    Google Scholar 

  • Kanlayasiri, U., Sanguanpong, S. and Jaratmanachot, W. 2000. A Rule-based Approach for Port Scanning Detection. Proceedings of the 23rd Electrical Engineering Conference, Chiang Mai, Thailand.

    Google Scholar 

  • Locasto, M. E., Sidiroglou, S. and Keromytis, A. D. Software Self-healing Using Collaborative Application Communities. The 13th Annual Network and Distributed System Security, Symposium, San Diego, California.

    Google Scholar 

  • Mitzenmacher, M. 2002. Compressed Bloom filters. IEEE/ACM Transactions on Networking 10(5): 604–612.

    Article  Google Scholar 

  • Prost, A. 2003. The Danger of Spyware, Symantec Security Response.

    Google Scholar 

  • Symantec. 2006. Symantec Internet Security Threat Report.

    Google Scholar 

  • Wang, H., Guo, Ch., Simon, D. and Zugenmaier, A. 2004. “Shield: Vulnerability-driven Network Filters for Preventing Known Vulnerability Exploits.” SIGCOMM’04, Portland, Oregon, USA. Aug. 30–Sept. 3.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Arnold, H., Erner, M., Möckel, P., Schläffer, C. (2010). Integration of Academic Research into Innovation Projects: The Case of Collaboration with a University Research Institute. In: Arnold, H., Erner, M., Möckel, P., Schläffer, C. (eds) Applied Technology and Innovation Management. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88827-7_4

Download citation

Publish with us

Policies and ethics