Robust Adaptive Predictive Modeling and Data Deluge (Extended Abstract)

  • Bogdan GabrysEmail author
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 242)


We are currently experiencing an incredible, explosive growth in digital content and information. According to IDC [5], there currently exists over 2.7 zetabytes of data. It is estimated that the digital universe in 2020 will be 50 times as big as in 2010 and that from now until 2020 it will double every two years. Research in traditionally qualitative disciplines is fundamentally changing due to the availability of such vast amounts of data. In fact, data-intensive computing has been named as the fourth paradigm of scientific discovery [6] and is expected to be key in unifying the theoretical, experimental and simulation based approaches to science.


Simulation Base Approach Soft Sensor Intelligent Data Analysis Airline Ticket Real World Prob 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Budka, M., Gabrys, B.: Ridge regression ensemble for toxicity prediction. Procedia Computer Science 1(1), 193–201 (2010)CrossRefGoogle Scholar
  2. 2.
    Budka, M., Gabrys, B., Ravagnan, E.: Robust predictive modelling of water pollution using biomarker data. Water Research 44(10), 3294–3308 (2010)CrossRefGoogle Scholar
  3. 3.
    Davenport, T.H., Harris, J.G.: Competing on Analytics: The New Science of Winning, 1st edn. Harvard Business School Press (2007)Google Scholar
  4. 4.
    Gabrys, B., Leiviskä, K., Strackeljan, J. (eds.): Do Smart Adaptive Systems Exist? - Best Practice for Selection and Combination of Intelligent Methods. STUDFUZZ, vol. 173. Springer, Heidelberg (2005)Google Scholar
  5. 5.
    Gantz, J., Reinsel, D.: The digital universe in 2020: Big data, bigger digital shadows, and biggest growth in the far east (2012), (Sponsored by EMC Corporation)
  6. 6.
    Hey, T., Tansley, S., Tolle, K. (eds.): The Fourth Paradigm: Data-Intensive Scientific Discovery. Microsoft Research (2009)Google Scholar
  7. 7.
    Kadlec, P., Gabrys, B.: Architecture for development of adaptive on-line prediction models. Memetic Computing 1(4), 241–269 (2009)CrossRefGoogle Scholar
  8. 8.
    Kadlec, P., Gabrys, B., Strandt, S.: Data-driven soft sensors in the process industry. Computers and Chemical Engineering 33(4), 795–814 (2009)CrossRefGoogle Scholar
  9. 9.
    Riedel, S., Gabrys, B.: Combination of multi level forecasts. International Journal of VLSI Signal Processing Systems for Signal, Image, and Video Technology 49(2), 265–280 (2007); Special issue on Data Fusion for Medical, Industrial, and Environmental Applications Google Scholar
  10. 10.
    Riedel, S., Gabrys, B.: Pooling for combination of multi level forecasts. IEEE Transactions on Knowledge and Data Engineering 21(12), 1753–1766 (2009)CrossRefGoogle Scholar
  11. 11.
    Ruta, D., Gabrys, B.: Classifier selection for majority voting. Information Fusion 6(1), 63–81 (2005); Special Issue on Diversity in Multiple Classifier SystemsGoogle Scholar
  12. 12.
    Ruta, D., Gabrys, B., Lemke, C.: A generic multilevel architecture for time series prediction. IEEE Transactions on Knowledge and Data Engineering 23(3), 350–359 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Smart Technology Research Centre, Computational Intelligence Research GroupBournemouth UniversityBournemouthUnited Kingdom

Personalised recommendations