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ference in the network and computing consumption,
which can cause a bottleneck in the Data Station net-
work. The case study worked adequately with a sim-
ple aggregation algorithm, so we believe that our sys-
tem can alleviate the IT infrastructure constraints that
the healthcare organizations can have to ensure the
PHT execution while respecting the principles of the
PHT approach.
Future research should be performed to test our
solution with other use cases, by including machine
learning algorithms in the Train or dependent transac-
tions, for instance, to experience idle moments wait-
ing for input data. Other Trains with different interac-
tion mechanisms such as APIs, queries, and messages
should be created and then tested with extensions of
our system. We also propose some future work to as-
sess the solution developed in this research, integrat-
ing the implementation to existing proof of concepts
developed by organizations in the PHT project. Some
of these implementations already have deployed a
vast majority of the PHT workflow and have elabo-
rated more robust case studies. It would beneficial to
combine these efforts and evaluate how our solution
behaves as well as other metrics like performance and
execution time from when the end-user dispatches the
Train until the results are made available.
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