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Titlebook: Big Social Data and Urban Computing; First Workshop, BiDU Jonice Oliveira,Claudio M. Farias,Giancarlo Fortin Conference proceedings 2019 Sp

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DMEK: Improving Profile Matching in Opportunistic Collaborationss. A possible solution to this problem is to use the mobile devices wireless network capabilities to exchange data by creating mobile P2P networks. These networks should opportunistically collaborate to exchange information to other devices in their proximity, only requiring users to specify their i
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https://doi.org/10.1007/978-0-387-85359-8omplex network metrics. The results demonstrated the public transportation system efficiency showing regions of Chicago, Dubai and Taichung well served and regions which are key points to the transportation city interconnecting various areas. Moreover, we could evaluate how improved the transportati
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Final Comments and Perspectiveshe city entities data streams. We discuss the generality, performance, and limitations of the proposed constructs through a real-world case study that was used in the Olympic Games of Rio in 2016 to detect, in real-time, existing and new situations that could affect the city mobility infrastructure.
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https://doi.org/10.1007/978-3-030-79569-6 casualties (number of kills/injuries); tweeters expressing fear and sadness tend to share words of affection and support, while tweeters expressing anger tend to use intense words of hate, intolerance and anger.
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https://doi.org/10.1007/978-3-030-79569-6ontologies and published in RDF using Linked Open Data standards in a semantic knowledge graph, allowing information to be identified, disambiguated and interconnected by software agents in future research.
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