An interoperable open data framework for discovering popular tours based on geo-tagged tweets. (2017)

• Record Type: Journal Article
• Authors: Bordogna, Gloria; Cuzzocrea, Alfredo; Frigerio, Luca; Psaila, Giuseppe; Toccu, Maurizio
• Is Part Of: International journal of intelligent information and database systems. Volume 10:Number 3/4(2017)
• Page Start: 246
• Page End: 268
• Publication Date: 2017
• Subjects: big data analytics -- knowledge discovery from geo-located tweets -- intelligent systems; Database management -- Computer programs -- Periodicals; Information retrieval -- Computer programs -- Periodicals; Information storage and retrieval systems -- Computer programs -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligent agents (Computer software) -- Periodicals; 006.33
• Languages: English
• ISSNs: 1751-5858

Abstract

In this paper, we introduce an original approach that exploits timestamped geo-tagged messages posted by Twitter users through their smartphones when they travel to trace their trips. A clustering approach is applied to group similar trips to identify tours, and an interoperable framework is used to share the popular tours on the web, in order to analyse them in relation with local geo-located territorial resources. Tools developed to reconstruct and mine the tours of tourists within a region are described, which identify, track, and group the tourists' trips through a knowledge-based approach, exploiting timestamped geo-tagged information associated with Twitter messages sent by tourists while travelling. The collected tracks are managed and shared on the web in compliance with OGC standards so as to be able to analyse the characteristic of localities visited by the tourists by spatial overlaying with other open geo-spatial data, such as maps of points of interest (POIs) of distinct type. The result is a novel interoperable framework, based on web-service technology.