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Big Personal Data

Authors: Peter Wlodarczak;

Big Personal Data

Abstract

This paper describes how personal data collected through channels like Social Media or mobile apps has given rise to a new type of application called anticipatory systems. Anticipatory systems use Big Data analysis techniques for monitoring the activities of millions of people to give timely information on traffic jams, suggesting alternative routes; on delayed flights; on disease outbreaks or on potential crimes. Smartphones are always-on devices and are equipped with GPS, accelerometers and cameras. Data, like location and movement, are added to the collected data without user interaction or the user even being aware. The total amount of digital data created is doubling every two years (Simonite 2013). Seventy five per cent of all digital data is created by consumers. Social Media sites are one of the biggest sources of user created content. The economic potential of this swelling amount of data has been realised in many industries, yet only 0.5 per cent of the data is currently analysed. As more companies exploit the real power of Big Data and personal data, Big Personal Data, new applications and business cases have begun to appear in the market. Facebook suggests who might wish to make friends with, Google Now tells you when your flight is delayed, Samsung’s Geo News App alerts you on earth quakes to name a few. This paper gives an overview of current developments in the area of Big Personal Data like predictive policing, medical care, disease control or traffic forecasting.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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