  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 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 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.
