The "big data" explicitly produced by people through social applications, or implicitly gathered through sensors and transaction records, enables a new generation of mining and analysis tools to understand the trends and dynamics of today's interconnected society. While important steps have been made towards personal, urban, and social awareness, several research challenges still need to be addressed to fully realize the pervasive computing vision. On the one hand, the lack of standard languages and common semantic frameworks strongly limit the possibility to opportunistically acquire available context data, reason with it, and provide proactive services. On the other hand, existing techniques for identifying complex contextual situations are mainly restricted to the recognition of simple actions and activities. Most importantly, due to the unprecedented quantity of digital traces that people leave as they go about their everyday lives, formal privacy methods and trust models must be enforced to avoid the "big data" vision turning into a "big brother" nightmare. In this chapter, the authors discuss the above-mentioned research issues and highlight promising research directions.
|Titolo:||Research challenges for personal and collective awareness|
|Data di pubblicazione:||2013|
|Tipologia:||2.1 Contributo in volume (Capitolo o Saggio)|
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