Abstract
Recently, we have witnessed a period which things are connected to the Internet. Connected cars are currently among things connected to the Internet. Wireless communications technologies built-in or brought in connected cars enable data generated by in car sensors to be transmitted to external computers where it is analyzed. The main challenge for connected cars services providers is that the collection of same vehicle’s data such as engine temperature, engine Revolutions per minute (RPM), vehicle speed are subjected to different connected cars applications which the final purpose of each of them differs. This paper studies design steps to take in consideration when implementing Map Reduce patterns to analyze vehicle’s data in order to produce accurate useful outputs. These outputs obtained through big data technology forms a storage repository for the automakers and connect cars services providers. The proposed analytical model is based on a data-driven approach. This approach consists of collecting data sets uploaded from connected cars. Those data are then monitored based on different aspects of activity of the vehicles that we quote as “Events”. Hadoop supplements by Map-Reduce functions based reduce side joins with One-To-One joins has been deployed to process a large data and delivered useful outputs. The outputs merged with external information constitute a great insights to connected cars in order to afford connected cars applications.
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Acknowledgments
This work was supported by the Brain Busan 21 Project (2016), Nurimaru R&BD project (Busan IT Industry Promotion Agency, in 2016) and Research Institute of Dong-Eui University (2016).
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Nkenyereye, L., Jang, J.W. (2017). Design of Processing Model for Connected Car Data Using Big Data Technology. In: Park, J., Pan, Y., Yi, G., Loia, V. (eds) Advances in Computer Science and Ubiquitous Computing. UCAWSN CUTE CSA 2016 2016 2016. Lecture Notes in Electrical Engineering, vol 421. Springer, Singapore. https://doi.org/10.1007/978-981-10-3023-9_23
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DOI: https://doi.org/10.1007/978-981-10-3023-9_23
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