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Pervasive Data Science and AI

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Pervasive Data Science and AI. / Davies, N.; Lane, N.D.; Musolesi, M.

In: IEEE Pervasive Computing, Vol. 18, No. 3, 31.07.2019, p. 7-8.

Research output: Contribution to journalEditorialpeer-review

Harvard

Davies, N, Lane, ND & Musolesi, M 2019, 'Pervasive Data Science and AI', IEEE Pervasive Computing, vol. 18, no. 3, pp. 7-8. https://doi.org/10.1109/MPRV.2019.2944289

APA

Davies, N., Lane, N. D., & Musolesi, M. (2019). Pervasive Data Science and AI. IEEE Pervasive Computing, 18(3), 7-8. https://doi.org/10.1109/MPRV.2019.2944289

Vancouver

Davies N, Lane ND, Musolesi M. Pervasive Data Science and AI. IEEE Pervasive Computing. 2019 Jul 31;18(3):7-8. https://doi.org/10.1109/MPRV.2019.2944289

Author

Davies, N. ; Lane, N.D. ; Musolesi, M. / Pervasive Data Science and AI. In: IEEE Pervasive Computing. 2019 ; Vol. 18, No. 3. pp. 7-8.

Bibtex

@article{511346857e1b40688cb6f5fbbfcd900f,
title = "Pervasive Data Science and AI",
abstract = "Recent years have seen an explosion in the use of data science and AI as a central tenant in numerous computing applications, products, research, and innovation. Examples of the success of data science abound—applying new machine-learning techniques to problems such as vision and speech recognition and translation has achieved commonplace levels of performance that would have seemed impossible a few years ago. In parallel, developments in pervasive computing increasingly enable us to instrument our physical environment with complex sensors and actuators and create an interconnected world that generates huge volumes of data. The importance of these trends can be seen in the growing momentum of exemplars such as the Internet of Things (IoT), smart environments, and augmented cognition. Pervasive data science is characterized by a focus on the collection, analysis (inference), and use of data (actuation) in pursuit of the vision of ubiquitous computing1 and raises multiple new challenges, demanding new approaches to how we capture, process, and use data in pervasive environments. Beyond the hype, it is clear that our world is becoming increasingly data centric, in which both physical and electronic services depend on the collection, analysis, and application of large volumes of heterogeneous data. In this Special Issue, we present a series of articles that cover different aspects of the exciting work that is currently carried out in this area.",
author = "N. Davies and N.D. Lane and M. Musolesi",
year = "2019",
month = jul,
day = "31",
doi = "10.1109/MPRV.2019.2944289",
language = "English",
volume = "18",
pages = "7--8",
journal = "IEEE Pervasive Computing",
issn = "1536-1268",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "3",

}

RIS

TY - JOUR

T1 - Pervasive Data Science and AI

AU - Davies, N.

AU - Lane, N.D.

AU - Musolesi, M.

PY - 2019/7/31

Y1 - 2019/7/31

N2 - Recent years have seen an explosion in the use of data science and AI as a central tenant in numerous computing applications, products, research, and innovation. Examples of the success of data science abound—applying new machine-learning techniques to problems such as vision and speech recognition and translation has achieved commonplace levels of performance that would have seemed impossible a few years ago. In parallel, developments in pervasive computing increasingly enable us to instrument our physical environment with complex sensors and actuators and create an interconnected world that generates huge volumes of data. The importance of these trends can be seen in the growing momentum of exemplars such as the Internet of Things (IoT), smart environments, and augmented cognition. Pervasive data science is characterized by a focus on the collection, analysis (inference), and use of data (actuation) in pursuit of the vision of ubiquitous computing1 and raises multiple new challenges, demanding new approaches to how we capture, process, and use data in pervasive environments. Beyond the hype, it is clear that our world is becoming increasingly data centric, in which both physical and electronic services depend on the collection, analysis, and application of large volumes of heterogeneous data. In this Special Issue, we present a series of articles that cover different aspects of the exciting work that is currently carried out in this area.

AB - Recent years have seen an explosion in the use of data science and AI as a central tenant in numerous computing applications, products, research, and innovation. Examples of the success of data science abound—applying new machine-learning techniques to problems such as vision and speech recognition and translation has achieved commonplace levels of performance that would have seemed impossible a few years ago. In parallel, developments in pervasive computing increasingly enable us to instrument our physical environment with complex sensors and actuators and create an interconnected world that generates huge volumes of data. The importance of these trends can be seen in the growing momentum of exemplars such as the Internet of Things (IoT), smart environments, and augmented cognition. Pervasive data science is characterized by a focus on the collection, analysis (inference), and use of data (actuation) in pursuit of the vision of ubiquitous computing1 and raises multiple new challenges, demanding new approaches to how we capture, process, and use data in pervasive environments. Beyond the hype, it is clear that our world is becoming increasingly data centric, in which both physical and electronic services depend on the collection, analysis, and application of large volumes of heterogeneous data. In this Special Issue, we present a series of articles that cover different aspects of the exciting work that is currently carried out in this area.

U2 - 10.1109/MPRV.2019.2944289

DO - 10.1109/MPRV.2019.2944289

M3 - Editorial

VL - 18

SP - 7

EP - 8

JO - IEEE Pervasive Computing

JF - IEEE Pervasive Computing

SN - 1536-1268

IS - 3

ER -