Clustering Geo-tagged Tweets for Advanced Big Data Analytics

2016 IEEE International Congress on Big Data (BigData Congress) Pub Date : 2016-06-27 DOI:10.1109/BigDataCongress.2016.78

Gloria Bordogna, Luca Frigerio, A. Cuzzocrea, G. Psaila

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引用次数: 26

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.

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聚类地理标记推文用于高级大数据分析

在本文中，我们介绍了一种新颖的方法，利用Twitter用户在旅行时通过智能手机发布的带有时间戳的地理标记信息来跟踪他们的旅行。提出了一种新颖的聚类技术，将相似的行程分组来定义行程，并分析受欢迎的行程与当地地理位置资源的关系。这个目标与新兴的大数据分析工具非常相关。本文描述了用于重建和挖掘一个地区内最受欢迎的游客旅游的工具，这些工具通过一种基于知识的方法，利用游客在旅行时发送的Twitter消息相关的时间标记地理标记信息，识别、跟踪和分组游客的旅行。收集到的轨迹将按照OGC的标准在网上进行管理和共享，以便与其他开放数据(例如不同类型的兴趣点地图)进行空间叠加，分析游客到访地点的特征。其结果是一个基于web服务技术的新颖的可互操作框架。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2016 IEEE International Congress on Big Data (BigData Congress)

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