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Big data: It's a relatively new concept, and as a society we’ve never really had it before. But what does it really mean?

“We’ve got the devices which can collect and store this data, but what shall we do with the data and what are the advantages of understanding it - will it help us navigate society better and help us improve the way we live?” Professor Graham Cormode, Department of Computer Science.

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Digital data and death: Some people are now turning to digital spaces to continue their bonds with the dead.

“The dead are no longer hidden away, they are with us on our digital devices, such as smartphones, and take the form of voicemails, messages and photos. But these social networks and messaging services were designed for people to stay in touch with the living. Using them to talk with the dead is blurring the distinction between the living and the ‘socially active dead’.” Debra Bassett, Department of Sociology.

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    What trends will we see in a big data future? Find out how our leading academics believe we can tap into the potential of big data.

    "One promise of big data is personalised medicine; the possibility of accurately predicting the course of a disease and the most adequate treatment for each individual patient. We can now attempt to understand the big picture, rather than very narrowly defined aspects of a disease.” Professor David Rossell, Department of Statistics.

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    We're exploring ways of using data to diagnose cancer and provide improved, more personalised treatments for patients.

    “Using the predictive power of the data we can look for patterns – and where we see something which fits a known pattern and looks aggressive, the disease can be nipped in the bud early on. If we find something which registers as not so aggressive, clinicians can be less heavy handed with treatment – which is better for the patient." Professor Nasir Rajpoot, Department of Computer Science.

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    Monitoring London's air quality is central to a project with the Alan Turing Institute, helping inform pollution reduction interventions

    “We will develop and deploy state of the art statistical and machine learning algorithms on the air quality sensor networks in order to extract knowledge, inform policy, and monitor interventions.” Dr Theo Damoulas, Department of Computer Science.

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