A preprint of our paper “OODIDA: On-board/Off-board Distributed Data Analytics for Connected Vehicles” is now available on arXiv. It describes a distributed system for data analytics for the automotive industry, targeting a fleet of reference vehicles. The abstract is reproduced below:

OODIDA: On-board/Off-board Distributed Data Analytics
for Connected Vehicles
Gregor Ulm, Emil Gustavsson, and Mats Jirstrand

Connected vehicles may produce gigabytes of data per 
hour, which makes centralized data processing 
impractical at the fleet level. In addition, there 
are the problems of distributing tasks to edge 
devices and processing them efficiently. Our solution 
to this problem is OODIDA (On-board/off-board 
Distributed Data Analytics), which is a platform that 
tackles both task distribution to connected vehicles 
as well as concurrent execution of large-scale tasks 
on arbitrary subsets of clients. Its message-passing 
infrastructure has been implemented in Erlang/OTP, 
while the end points are language-agnostic. OODIDA is 
highly scalable and able to process a significant 
volume of data on resource-constrained clients.