Join the 2018 DEBS Grand Challenge and use machine learning to make
maritime transportation more reliable! Explore multiple gigabytes of
real maritime spatio-temporal streaming data and compete with peers from
academia and industry for the Grand Challenge prize of 1000 USD.
(Website: http://www.cs.otago.ac.nz/debs2018/calls/gc.html).
Challenge start: 15th of January 2018 (i.e. HOBBIT platform is available
for testing)
Release of Data: 1st of February
Submission deadline: 15th of April 2018
The Grand Challenge data is provided by MarineTraffic and hosted by
BigDataOcean (EU Horizon 2020 project, No 732310). The evaluation
platform is provided by the HOBBIT, EU Horizon 2020 project.
General Description
The DEBS Grand Challenge is a series of competitions, that started in
2010, in which both academics and professionals compete with the goal of
building faster and more accurate distributed and event based system.
Every year, the DEBS Grand Challenge participants have a chance to
explore a new data set and a new problem and can compare their results
based on the common evaluation criteria.
The 2018 DEBS Grand Challenge focuses on the application of machine
learning to spatio-temporal streaming data. The goal of the challenge is
to make the naval transportation industry more reliable by providing
predictions for vessels' destinations and arrival times. Predicting both
correct destinations and arrival times of vessels are relevant problems,
that once solved, will boost the efficiency of the overall supply chain
management. The Grand Challenge data is provided by the MarineTraffic
company and hosted by the Big Data Ocean project, which has received
funding from the European Union's H2020 research and innovation action
program under grant agreement number No 732310. The evaluation platform
is provided by the <https://project-hobbit.eu/> HOBBIT project
represented by AGT International ( <http://www.agtinternational.com/>
http://www.agtinternational.com/), an EU Horizon 2020 project. The
HOBBIT project has received funding from the European Union's H2020
research and innovation action program under grant agreement number 688227.
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