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== Big Data Analytics Expert Group ==
 
== Big Data Analytics Expert Group ==
   
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Sensing big data at a societal scale, and the transparent interlinking of digital and physical reality, has the potential of providing a powerful social microscope, which can help us understand many complex and hidden socio-economic phenomena, such as mobility behaviors, economic and financial crises, the spread of epidemics, the diffusion of opinions and so on. It is clear that such challenge requires high-level analytics, modeling and reasoning across all the social dimensions.
[[Call]]
 
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In practice, however, there is a big gap from the opportunities offered by the big data to the challenges posed by social, economical, scientific phenomena: Big data are fragmented and low-level. They reside in diverse databases and repositories, often inaccessible for proprietary and legal constraints, and have limited power to portray different social dimensions. There are many regulatory, business and technological barriers to set the power of big data free.
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As a response, the paradigm of Big Data Analytics is emerging at the convergence of several disciplines, including machine learning, data mining, statistics, complex systems, socioeconomic sciences, etc. There is a flourishing body of research about making sense of Big Data, but a coherent scientific and technological framework is still missing. We need to put at work scientists and technologists from different disciplines to shape a research and innovation agenda that might drive the ERICM future actions.
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We propose here a group that involves prominent research leaders in industry and academia to share their visions and elaborate together what should be the challenging research issues in this promising research frontier.
   
 
[[Venue]]
 
[[Venue]]

Revision as of 12:43, 24 April 2014

Big Data Analytics Expert Group

Sensing big data at a societal scale, and the transparent interlinking of digital and physical reality, has the potential of providing a powerful social microscope, which can help us understand many complex and hidden socio-economic phenomena, such as mobility behaviors, economic and financial crises, the spread of epidemics, the diffusion of opinions and so on. It is clear that such challenge requires high-level analytics, modeling and reasoning across all the social dimensions. In practice, however, there is a big gap from the opportunities offered by the big data to the challenges posed by social, economical, scientific phenomena: Big data are fragmented and low-level. They reside in diverse databases and repositories, often inaccessible for proprietary and legal constraints, and have limited power to portray different social dimensions. There are many regulatory, business and technological barriers to set the power of big data free. As a response, the paradigm of Big Data Analytics is emerging at the convergence of several disciplines, including machine learning, data mining, statistics, complex systems, socioeconomic sciences, etc. There is a flourishing body of research about making sense of Big Data, but a coherent scientific and technological framework is still missing. We need to put at work scientists and technologists from different disciplines to shape a research and innovation agenda that might drive the ERICM future actions. We propose here a group that involves prominent research leaders in industry and academia to share their visions and elaborate together what should be the challenging research issues in this promising research frontier.

Venue

Experts List