In this paper, we introduce an original approach that exploits time stamped geo-tagged messages posted by Twitter users through their smartphones when they travel to trace their trips.An original clustering technique is presented, that groups similartrips to define tours and analyze the popular tours in relation with local geo-located territorial resources. This objective is veryrelevant for emerging big data analytics tools.Tools developed to reconstruct and mine the most popular tours of tourists within a region are described which identify, track and group tourists' trips through a knowledge-based approach exploiting time stamped geo-tagged information associated with Twitter messages sent by tourists while traveling.The collected tracks are managed and shared on the Web in compliance with OGC standards so as to be able to analyze the characteristic of localities visited by the tourists by spatial overlaying with other open data, such as maps of Points Of Interest (POIs) of distinct type. The result is an novel Interoperable framework, based on web-service technology.

Clustering geo-tagged tweets for advanced big data analytics

Cuzzocrea Alfredo;
2016

Abstract

In this paper, we introduce an original approach that exploits time stamped geo-tagged messages posted by Twitter users through their smartphones when they travel to trace their trips.An original clustering technique is presented, that groups similartrips to define tours and analyze the popular tours in relation with local geo-located territorial resources. This objective is veryrelevant for emerging big data analytics tools.Tools developed to reconstruct and mine the most popular tours of tourists within a region are described which identify, track and group tourists' trips through a knowledge-based approach exploiting time stamped geo-tagged information associated with Twitter messages sent by tourists while traveling.The collected tracks are managed and shared on the Web in compliance with OGC standards so as to be able to analyze the characteristic of localities visited by the tourists by spatial overlaying with other open data, such as maps of Points Of Interest (POIs) of distinct type. The result is an novel Interoperable framework, based on web-service technology.
9781509026227
Big Data Analytics
Intelligent Systems
Knowledge Discovery from Geo-Located Tweets
Information Systems
Computer Science Applications1707 Computer Vision and Pattern Recognition
Information Systems and Management
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11770/312525
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