Real-time streaming mobility analytics

被引：0

作者：

Garzo, Andras ^{[1
,2
]}

Benczur, Andras A. ^{[1
,2
]}

Sidlo, Csaba Istvan ^{[1
,2
]}

Tahara, Daniel ^{[3
]}

Wyatt, Erik Francis ^{[4
]}

机构：

[1] Univ Debrecen, Comp & Automat Res Inst, Hungarian Acad Sci MTA SZTAKI, Fac Informat, Budapest, Hungary

[2] Eotvos Lorand Univ, Budapest, Hungary

[3] Yale Univ, New Haven, CT USA

[4] St Olaf Coll, Northfield, MN 55057 USA

来源：

2013 IEEE INTERNATIONAL CONFERENCE ON BIG DATA | 2013年

关键词：

Mobility; Big Data; Data Mining; Visualization; Distributed Algorithms; Streaming Data; DISCOVERY;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Location prediction over mobility traces may find applications in navigation, traffic optimization, city planning and smart cities. Due to the scale of the mobility in a metropolis, real time processing is one of the major Big Data challenges. In this paper we deploy distributed streaming algorithms and infrastructures to process large scale mobility data for fast reaction time prediction. We evaluate our methods on a data set derived from the Orange D4D Challenge data representing sample traces of Ivory Coast mobile phone users. Our results open the possibility for efficient real time mobility predictions of even large metropolitan areas.

引用

页数：6

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