Advertisement

Mastering Data-Intensive Collaboration and Decision Making: The Dicode Project

  • Nikos KaracapilidisEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 454)

Abstract

Many collaboration and decision making settings are nowadays associated with huge, ever-increasing amounts of multiple types of data, which often have a low signal-to-noise ratio for addressing the problem at hand. The Dicode project aimed at facilitating and augmenting collaboration and decision making in such data-intensive and cognitively-complex settings. To do so, whenever appropriate, it built on prominent high-performance computing paradigms and proper data processing technologies to meaningfully search, analyze and aggregate data existing in diverse, extremely large, and rapidly evolving sources. At the same time, particular emphasis was given to the deepening of our insights about the proper exploitation of big data, as well as to collaboration and sense making support issues. This chapter reports on the overall context of the Dicode project, its scientific and technical objectives, the exploitation of its results and its potential impact.

Keywords

Opinion Mining Information Overload Decision Making Setting Global Knowledge Economy Consortium Partner 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Eppler, M., Mengis, J.: The concept of information overload: a review of literature from organization science, accounting, marketing, MIS, and related disciplines. Inf. Soc. 20(5), 325–344 (2004)CrossRefGoogle Scholar
  2. 2.
    IDC. The Diverse and Exploding Digital Universe. White Paper, March 2008. www.idc.com
  3. 3.
    Economist. A Special Report on managing information: Data, data everywhere. Economist (2010)Google Scholar
  4. 4.
    Hara, N., Solomon, P., Kim, S.L., Sonnenwald, D.H.: An emerging view of scientific collaboration: Scientists’ perspectives on collaboration and factors that impact collaboration. J. Am. Soc. Inform. Sci. Technol. 54, 952–965 (2003)CrossRefGoogle Scholar
  5. 5.
    Shim, J.P., Warkentin, M., Courtney, J.F., Power, D.J., Sharda, R., Carlsson, C.: Past, present and future of decision support technology. Decis. Support Syst. 33, 111–126 (2002)CrossRefGoogle Scholar
  6. 6.
    Karacapilidis, N. (ed.): Mastering Data-Intensive Collaboration and Decision Making: Cutting-edge research and practical applications in the Dicode project, Studies in Big Data Series, vol. 5, Springer (2014)Google Scholar
  7. 7.
    Tsiliki, G., Kossida, G.: Clinico-genomic research assimilator: a dicode use case. In: [6], pp. 165–180 (2014)Google Scholar
  8. 8.
    Löffler, R.: Opinion mining from unstructured web 2.0 data: a dicode use case. In: [6], pp. 181–200 (2014)Google Scholar
  9. 9.
    Lau, L., Yang-Turner, F., Karacapilidis, N.: Requirements for big data analytics supporting decision making: a sensemaking perspective. In: [6], pp. 49–70 (2014)Google Scholar
  10. 10.
    Friesen, N., Jakob, M., Kindermann, J., Maassen, D., Poigné, A., Rüping, S., Trabold, D.: The dicode data mining services. In: [6], pp. 89–118 (2014)Google Scholar
  11. 11.
    Tzagarakis, M., Karacapilidis, N., Christodoulou, S., Yang-Turner, F., Lau, L.: The dicode collaboration and decision making support services. In: [6], pp. 119–139 (2014)Google Scholar
  12. 12.
    de la Calle, G., Alonso-Martínez, E., Rojas-Vera, M., García-Remesal, M.: Integrating dicode services: the dicode workbench. In: [6], pp. 141–164 (2014)Google Scholar
  13. 13.
    Friesen, N., Kindermann, J., Maassen, D., Rüping, S.: Data mining in data-intensive and cognitively-complex settings: lessons learned from the dicode project. In: [6], pp. 201–212 (2014)Google Scholar
  14. 14.
    Christodoulou, S., Tzagarakis, M., Karacapilidis, N., Yang-Turner, F., Lau, L., Dimitrova, V.: Collaboration and decision making in data-intensive and cognitively-complex settings: lessons learned from the dicode project. In: [6], pp. 213–226 (2014)Google Scholar
  15. 15.
    Karacapilidis, N.: An Overview of Future Challenges of Decision Support Technologies. In: Gupta, J., Forgionne, G., Mora, M. (eds.) Intelligent Decision-Making Support Systems: Foundations, pp. 385–399. Applications and Challenges, Springer-Verlag, London, UK (2006)CrossRefGoogle Scholar
  16. 16.
    SAS. Data Visualization: Making Big Data Approachable and Valuable. White Paper (2013). http://www.sas.com/content/dam/SAS/en_us/doc/whitepaper2/sas-data-visualization-marketpulse-106176.pdf
  17. 17.
    Computing Community Consortium - Computing Research Association. Challenges and Opportunities with Big Data: A community white paper developed by leading researchers across the United States. White Paper, February 2012. http://www.cra.org/ccc/files/docs/init/bigdatawhitepaper.pdf

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Computer Technology Institute and Press “Diophantus”University of PatrasRio PatrasGreece

Personalised recommendations