In this paper, we introduce an original approach thatexploits timestamped geo-tagged messages posted by Twitter usersthrough 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 relationwith local geo-located territorial resources. This objective is veryrelevant for emerging big data analytics tools.Tools developed to reconstruct and mine the most populartours of tourists within a region are described which identify,track and group tourists' trips through a knowledge-based approachexploiting timestamped geo-tagged information associatedwith Twitter messages sent by tourists while traveling.The collected tracks are managed and shared on the Webin compliance with OGC standards so as to be able to analyzethe characteristic of localities visited by the tourists by spatialoverlaying with other open data, such as maps of Points Of Interest(POIs) of distinct type. The result is an novel Interoperableframework, based on web-service technology.
['Gloria Bordogna', 'Luca Frigerio', 'Alfredo Cuzzocrea', 'Giuseppe Psaila']
Clustering Geo-tagged Tweets for Advanced Big Data Analytics