Abstract
The wireless communication technologies built-in or brought in the vehicle enable new in-car telematics services. The development of connected cars emphasizes the use of sophisticated computation framework for gathering, analyzing a large volume of data generated in all aspects of vehicle operations using Big Data technologies. Since these data are essential for many connected cars applications, the design and monitoring of MapReduce algorithms for processing vehicle's data using Hadoop framework will allow to build a hosting of analytics data source. This hosting data source allows different connected cars industry ecosystem to access useful data they need to afford connected cars applications.This paper studies design steps to take in consideration when implementing MapReduce patterns to analyze vehicle's data in order to produce accurate useful data that are hosted at the automakers and connect cars services providers. Experiment results show that MapReduce join algorithm is highly scalable and optimized for distributed computing than Statistical Analysis System (SAS) framework and HiveQL declarative language.
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