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Paper detail

OODIDA: On-board/Off-board Distributed Real-Time Data Analytics for Connected Vehicles

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.

preprint2021arXivOpen access
Gregor UlmSimon SmithAdrian NilssonEmil GustavssonMats Jirstrand
Programming LanguagesDistributed, Parallel, and Cluster Computing
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Gregor UlmSimon SmithAdrian NilssonEmil GustavssonMats Jirstrand

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