Deciphering Location Context – A Semantic Web Approach

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Abstract:

Mobile user location data has been commercially exploited and studied due to the commoditization of GPS position sensors and the popularity of Location Based Ser- vices (LBS). Context researchers have already studied how to understand human mo- bility using location histories, and how to model location context using ontologies. However, these studies make surprisingly little use of rich geospatial data and knowledge about the world to a) explicitly describe user locations, and b) possibly infer implicit contexts. In this paper, we demonstrate that openly accessible geospatial data can facilitate both a) and b), thus resulting in improved understanding of mobile user location context.

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Related Research Areas:

Data Science
Lead Professor: Peter Fox
Description: Science has fully entered a new mode of operation. Data science is advancing inductive conduct of science driven by the greater volumes, complexity and heterogeneity of data being made available over the Internet. Data science combines of aspects of data management, library science, computer science, and physical science using supporting cyberinfrastructure and information technology. As such it is changing the way all of these disciplines do both their individual and collaborative work.

Data science is helping scienists face new global problems of a magnitude, complexity and interdisciplinary nature whose progress is presently limited by lack of available tools and a fully trained and agile workforce.

At present, there is a lack formal training in the key cognitive and skill areas that would enable graduates to become key participants in escience collaborations. The need is to teach key methodologies in application areas based on real research experience and build a skill-set.

At the heart of this new way of doing science, especially experimental and observational science but also increasingly computational science, is the generation of data.

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