Successful analytics solutions that provide valuable insights often hinge on
the connection of various data sources. While it is often feasible to generate
larger data pools within organizations, the application of analytics within
(inter-organizational) business networks is still severely constrained. As data
is distributed across several legal units, potentially even across countries,
the fear of disclosing sensitive information as well as the sheer volume of the
data that would need to be exchanged are key inhibitors for the creation of
effective system-wide solutions -- all while still reaching superior prediction
performance. In this work, we propose a meta machine learning method that deals
with these obstacles to enable comprehensive analyses within a business
network. We follow a design science research approach and evaluate our method
with respect to feasibility and performance in an industrial use case. First,
we show that it is feasible to perform network-wide analyses that preserve data
confidentiality as well as limit data transfer volume. Second, we demonstrate
that our method outperforms a conventional isolated analysis and even gets
close to a (hypothetical) scenario where all data could be shared within the
network. Thus, we provide a fundamental contribution for making business
networks more effective, as we remove a key obstacle to tap the huge potential
of learning from data that is scattered throughout the network.

该篇论文提出了一种元机器学习方法，以应对业务网络中的数据共享问题，这一方法能够维护数据机密性，限制数据传输量，并展示出优于传统分析和接近理想共享情况的性能，因此可以有效地挖掘分散在网络中的数据潜力。