Big Data Architectures for Vehicle Data Analysis

In this paper, we consider Big Data applications for connected vehicles, where vehicle data is sent several times per second, permitting fine-grained and near real-time analysis of vehicle status and operating behavior. We focus on typical streaming applications and present implementations in Apache Spark and Apache Flink in different architectures and analyze several aspects of Big Data architectures for connected vehicles. First, we aim to show the potential of fine-grained analysis in terms of time resolution and level of detail for vehicular services. For instance, we show how to compute energy efficiency, comparing used energy vs needed energy, on the level of seconds. Secondly, we compare the architecture challenges in vehicular systems, including in-vehicle processing as well as cloud and edge processing. For these, we compare different approaches and architecture solutions. Third, we show that the performance and scalability of our different Big Data processing options differ significantly different for our use case.