Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Semantic Enrichment of Mobile Phone Data Records Using Background
  Knowledge   Every day, billions of mobile network events (i.e. CDRs) are generated by
cellular phone operator companies. Latent in this data are inspiring insights
about human actions and behaviors, the discovery of which is important because
context-aware applications and services hold the key to user-driven,
intelligent services, which can enhance our everyday lives such as social and
economic development, urban planning, and health prevention. The major
challenge in this area is that interpreting such a big stream of data requires
a deep understanding of mobile network events' context through available
background knowledge. This article addresses the issues in context awareness
given heterogeneous and uncertain data of mobile network events missing
reliable information on the context of this activity. The contribution of this
research is a model from a combination of logical and statistical reasoning
standpoints for enabling human activity inference in qualitative terms from
open geographical data that aimed at improving the quality of human behaviors
recognition tasks from CDRs. We use open geographical data, Openstreetmap
(OSM), as a proxy for predicting the content of human activity in the area. The
user study performed in Trento shows that predicted human activities (top
level) match the survey data with around 93% overall accuracy. The extensive
validation for predicting a more specific economic type of human activity
performed in Barcelona, by employing credit card transaction data. The analysis
identifies that appropriately normalized data on points of interest (POI) is a
good proxy for predicting human economical activities, with 84% accuracy on
average. So the model is proven to be efficient for predicting the context of
human activity, when its total level could be efficiently observed from cell
phone data records, missing contextual information however.

27
The categories of this paper are: comp-gas
24
The categories of this paper are: dg-ga
19
The categories of this paper are: eess
0
The categories of this paper are: hep-ph
37
The categories of this paper are: supr-con
11
The categories of this paper are: hep-lat
14
The categories of this paper are: math-ph
33
The categories of this paper are: plasm-ph
8
The categories of this paper are: hep-ex
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The categories of this paper are: hep-th
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The categories of this paper are: alg-geom
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The categories of this paper are: bayes-an
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The categories of this paper are: ao-sci
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The categories of this paper are: q-bio
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The categories of this paper are: nucl-th
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The categories of this paper are: solv-int