The second Maria de Maeztu Strategic Research Program (CEX2021-001195-M) of the Department of Information and Communication Technologies (DTIC) takes place between 2023 and 2026. The website for this program is under construction. You can find some details in this news.

The first María de Maeztu Strategic Research Program (MDM-2015-0502) took place between January 2016 and June 2020. It was focused on data-driven knowledge extraction, boosting synergistic research initiatives across our different research areas.

Back First International Workshop on Data Science for Internet of Things at IEEE MASS 2016

First International Workshop on Data Science for Internet of Things at IEEE MASS 2016

The Workshop on Data Science for Internet of Things at IEEE MASS 2016 will address techniques used for data management planning into IoT scenarios in order to optimize data acquisition, management and later discovery.

09.05.2016

 

Data science is an interdisciplinary field that involves techniques to acquire, store, analyze, manage and publish data. For example, data can be analyzed using machine learning, data analysis and statistics, optimizing processes and maximizing their power in larger scenarios.

 

In the Internet of Things (IoT), smartphones and household appliances can easily become sensor node and compose sensor networks, measuring environmental parameters and generating user interaction data. As sensor networks are mainly data-oriented networks, i.e., sensed data is their most valuable asset and the reason for the operation of the whole network, data science techniques have been adopted to improve the IoT in terms of data throughput, self-optimization and self-management. In fact, incorporating the lifecycle proposed by the data scientists will impact the future of the IoT, allowing researchers to reproduce scenarios, and optimize the acquisition, analysis and visualization of the data acquired by IoT devices.

The Workshop on Data Science for Internet of Things at IEEE MASS 2016 will address techniques used for data management planning into IoT scenarios in order to optimize data acquisition, management and later discovery.

logo-ds-iot.pngWeb: http://ds-iot.org/#

 

Topics (detailed list on the web):

Data Collection, Data Analysis, Data Management Solutions, Reproducibility of IoT Scenarios, Autonomous IoT, Data Management Planning Use Cases.

Steering Committee         

Programm Committee

  • Ruizhi Liao (Oxford Brookes University, Great Britain)
  • Cristina Cano (Trinity College Dublin, Ireland)

  • Alessandro Checco (University of Sheffield, Great Britain)

  • Antonio Loureiro (Universidade Federal de Minas Gerais, Brazil)

  • Elena Gaura (Coventry University, Great Britain)

  • Luiz Fernando Bittencourt (Universidade Estadual de Campinas, Brazil)

  • Liansheng Tan (Central China Normal University, China)

  • Juan José Murillo Fuentes (Universidad de Sevilla, Spain)

  • Amy Lynn Murphy (Bruno Kessler Foundation, Italy)

  • Vanesa Daza (Dept. Information and Communication Technologies, Universitat Pompeu Fabra, Spain)

Multimedia

Categories:

SDG - Sustainable Development Goals:

Els ODS a la UPF

Contact

Department of Information and Communication Technologies, UPF

Grant CEX2021-001195-M funded by MCIN/AEI /10.13039/501100011033


 

Department of Information and Communication Technologies, UPF

Tànger building (Poblenou campus)
Tànger, 122-140
08018 Barcelona

[email protected]

  • Àngel Lozano - Scientific director
  • Aurelio Ruiz - Program management