Integer Time Series Compression for Holistic Data Analytics in the Context of Vehicle Sensor Data

Christopher Vox, David Broneske, Jan Piewek, Andreas Udo Sass, Gunter Saake

Abstract: The amount of information which is gathered, pro cessed and sent by vehicles increases permanently. Thereby, V2X communication is subject to various limitations such as limited bandwidth and hardware constraints. Furthermore, processing and analyzing vehicle data as well as training artificial neural networks on this enormous data amount is highly computational expensive. In conclusion, there is a need of system-wide optimization of data processing, data transmission, and data mining to reduce environmental burdens with respect to the named limitations. Therefore, we have defined the following research question: How to optimize vehicle communication under consideration of limited bandwidth, computational constraints, real time capability as well as the subsequent utilization of the vehicle data in data mining methods? To answer this research question, we developed a lightweight but extremely powerful compression scheme, which we applied on multivariate vehicle sensor time series. Our approach achieved Pareto-optimal compression results regarding the quality measures compression ratio and compression speed. The results demonstrated that our proposed method enables an efficient linkage of data compression and data mining within a holistic and a real time capable context.