Research Article
Using the MapReduce Approach for the Spatio-Temporal Data Analytics in Road Traffic Crowdsensing Application
@INPROCEEDINGS{10.1007/978-3-030-00916-8_38, author={Sandhya Armoogum and Shevam Munchetty-Chendriah}, title={Using the MapReduce Approach for the Spatio-Temporal Data Analytics in Road Traffic Crowdsensing Application}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 13th International Conference, CollaborateCom 2017, Edinburgh, UK, December 11--13, 2017, Proceedings}, proceedings_a={COLLABORATECOM}, year={2018}, month={10}, keywords={Crowdsensing Big data MapReduce Spatio-temporal data Data analysis}, doi={10.1007/978-3-030-00916-8_38} }- Sandhya Armoogum
Shevam Munchetty-Chendriah
Year: 2018
Using the MapReduce Approach for the Spatio-Temporal Data Analytics in Road Traffic Crowdsensing Application
COLLABORATECOM
Springer
DOI: 10.1007/978-3-030-00916-8_38
Abstract
Crowdsensing applications are becoming more popular with time. In this work, we present a crowdsensing application for capturing road traffic information to help citizens to get real-time traffic condition. Such real-time information can be beneficial for citizens to plan their journeys. However, crowdsensing in this specific case, generates spatio-temporal data collected from numerous users; storing and processing such data in real-time can be quite challenging. The MapReduce programming approach has been proposed for processing data in this context. The MapReduce jobs used to process and analyze the data captured from the crowdsensing application are presented as well as the design of the crowdsensing application. Implementation of the MapReduce jobs proposed shows that data can be effectively processed and analyzed to present near real-time information about the road traffic flow while at the same time discarding used data which is no longer required.