Abstract
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.
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References
Budka, M., Gabrys, B.: Ridge regression ensemble for toxicity prediction. Procedia Computer Science 1(1), 193–201 (2010)
Budka, M., Gabrys, B., Ravagnan, E.: Robust predictive modelling of water pollution using biomarker data. Water Research 44(10), 3294–3308 (2010)
Davenport, T.H., Harris, J.G.: Competing on Analytics: The New Science of Winning, 1st edn. Harvard Business School Press (2007)
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)
Gantz, J., Reinsel, D.: The digital universe in 2020: Big data, bigger digital shadows, and biggest growth in the far east (2012), http://www.emc.com/collateral/analyst-reports/idc-the-digital-universe-in-2020.pdf (Sponsored by EMC Corporation)
Hey, T., Tansley, S., Tolle, K. (eds.): The Fourth Paradigm: Data-Intensive Scientific Discovery. Microsoft Research (2009)
Kadlec, P., Gabrys, B.: Architecture for development of adaptive on-line prediction models. Memetic Computing 1(4), 241–269 (2009)
Kadlec, P., Gabrys, B., Strandt, S.: Data-driven soft sensors in the process industry. Computers and Chemical Engineering 33(4), 795–814 (2009)
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
Riedel, S., Gabrys, B.: Pooling for combination of multi level forecasts. IEEE Transactions on Knowledge and Data Engineering 21(12), 1753–1766 (2009)
Ruta, D., Gabrys, B.: Classifier selection for majority voting. Information Fusion 6(1), 63–81 (2005); Special Issue on Diversity in Multiple Classifier Systems
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)
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Gabrys, B. (2014). Robust Adaptive Predictive Modeling and Data Deluge (Extended Abstract). In: Gruca, D., Czachórski, T., Kozielski, S. (eds) Man-Machine Interactions 3. Advances in Intelligent Systems and Computing, vol 242. Springer, Cham. https://doi.org/10.1007/978-3-319-02309-0_3
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DOI: https://doi.org/10.1007/978-3-319-02309-0_3
Publisher Name: Springer, Cham
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