New Preprint: OODIDA: On-board/Off-board Distributed Data Analytics for Connected Vehicles

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.

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