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Bigger data for big data: From Twitter to brain–computer interfaces

Published online by Cambridge University Press:  26 February 2014

Etienne B. Roesch
Affiliation:
School of Systems Engineering, University of Reading, Reading RG6 6AH, United Kingdom. contact@etienneroes.chf.t.stahl@reading.ac.ukhttp://etienneroes.chhttp://fredericstahl.wordpress.com Centre for Integrative Neuroscience and Neurodynamics, University of Reading, Reading RG6 6AH, United Kingdom
Frederic Stahl
Affiliation:
School of Systems Engineering, University of Reading, Reading RG6 6AH, United Kingdom. contact@etienneroes.chf.t.stahl@reading.ac.ukhttp://etienneroes.chhttp://fredericstahl.wordpress.com
Mohamed Medhat Gaber
Affiliation:
School of Computing Science and Digital Media, The Robert Gordon University, Aberdeen AB25 1HG, United Kingdom. m.gaber1@rgu.ac.ukhttp://mohamedmgaber.weebly.com/

Abstract

We are sympathetic with Bentley et al.’s attempt to encompass the wisdom of crowds in a generative model, but posit that a successful attempt at using big data will include more sensitive measurements, more varied sources of information, and will also build from the indirect information available through technology, from ancillary technical features to data from brain–computer interfaces.

Type
Open Peer Commentary
Copyright
Copyright © Cambridge University Press 2014 

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References

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Stahl, F., Gaber, M. M., Bramer, M. & Yu, P. S. (2010) Pocket Data Mining: Towards collaborative Data Mining in mobile computing environments. In: Proceedings of the Twenty-Second IEEE International Conference on Tools with Artificial Intelligence (ICTAI), Arras, France, 27–29 October, 2010, ed. Grégoire, É., pp. 323–30. IEEE Computer Society.Google Scholar

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