Exploiting Big VGI to Improve Routing and Navigation Services
Big DataPublished 22 January 2014
Johannes Lauer Mohamed Bakillah
Citations28
SJR quartileQ2
SJR score0.69
SNIP1.33
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This work states that Facebook announced in 2012 that its system deals with petabyte scale data* as it processes 2.5 billion content elements and over 500 TB of data daily.
Abstract
The recent technological advances in data production and dissemination have enabled the generation of unprecedented volumes of geospatial data, giving rise to the paradigm of big data. Part of this trend is volunteered geographic information (VGI), that is, geographic information (GI) produced by volunteer contributors (Goodchild 2007), and crowdsourced data such as those obtained from social media. Whether big data refer to these huge volumes of data, which no longer fit traditional9.1 Introduction .................................................................................................. 177 9.2 What Is Big Data? ......................................................................................... 178 9.3 VGI as Big Data ............................................................................................ 180 9.4 Traditional Routing Services ........................................................................ 181 9.5 Routing Services Using Big VGI/Crowdsourced Data ................................. 1829.5.1 Routing with Landmarks Extracted from Big VGI/ Crowdsourced Data .......................................................................... 1829.5.2 GPS Traces ....................................................................................... 183 9.5.3 Social Media Reports ....................................................................... 1849.6 Challenges for Exploiting Big VGI to Improve Routing Services ................ 184 9.6.1 Limitations of VGI and Crowdsourced Data .................................... 184 9.6.2 Impact on the Development of Routing and Navigation Services .... 1859.6.2.1 Interoperability .................................................................. 185 9.6.2.2 Finding the Right Data....................................................... 185 9.6.2.3 Analyzing and Interpreting Data ....................................... 1869.6.3 Applicability of Big Data Solutions to Big VGI ............................... 187 9.7 Summary ...................................................................................................... 188 References .............................................................................................................. 189database structures (Dumbill 2013), or to the new technologies and techniques that must be developed to deal with these massive data sets (Davenport et al. 2012), there is a common understanding that the full potential of such amount of data can only be exploited if information and knowledge with added value can be extracted from it.
Keywords
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