Abstract
A fleet of connected vehicles easily produces many gigabytes of data per hour, making centralized (off-board) data processing impractical. In addition, there is the issue of distributing tasks to on-board units in vehicles and processing them efficiently. Our solution to this problem is On-board/Off-board Distributed Data Analytics (OODIDA), which is a platform that tackles both task distribution to connected vehicles as well as concurrent execution of tasks on arbitrary subsets of edge clients. Its message-passing infrastructure has been implemented in Erlang/OTP, while the end points use a language-independent JSON interface. Computations can be carried out in arbitrary programming languages. The message-passing infrastructure of OODIDA is highly scalable, facilitating the execution of large numbers of concurrent tasks.
Highlights
Big data in the automotive industry is of increasing concern, considering that connected vehicles may produce large volumes of data per hour
While our system could be used for general distributed data processing tasks, it has been designed for data exploration and rapid prototyping in the automotive domain, targeting a fleet of reference vehicles
Off-board Distributed Data Analytics (OODIDA) is noteworthy for applying the paradigm of lightweight concurrent processing, via the programming language Erlang, to the automotive domain for real-time data analytics
Summary
Big data in the automotive industry is of increasing concern, considering that connected vehicles may produce large volumes of data per hour. With On-board/Off-board Distributed Data Analytics (OODIDA), which is a platform that facilitates the distribution and concurrent execution of real-time data analytics tasks in a heterogeneous system, we can conveniently process vehicle telemetry data as batches or pseudo-realtime streams close to the data source. OODIDA uses a virtual private network for communication It connects data analysts with a large number of on-board units (OBUs). While our system could be used for general distributed data processing tasks, it has been designed for data exploration and rapid prototyping in the automotive domain, targeting a fleet of reference vehicles.
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