  Industry 5.0 aims at maximizing the collaboration between humans and
machines. Machines are capable of automating repetitive jobs, while humans
handle creative tasks. As a critical component of Industrial Internet of Things
(IIoT) systems for service delivery, network data stream analytics often
encounter concept drift issues due to dynamic IIoT environments, causing
performance degradation and automation difficulties. In this paper, we propose
a novel Multi-Stage Automated Network Analytics (MSANA) framework for concept
drift adaptation in IIoT systems, consisting of dynamic data pre-processing,
the proposed Drift-based Dynamic Feature Selection (DD-FS) method, dynamic
model learning & selection, and the proposed Window-based Performance Weighted
Probability Averaging Ensemble (W-PWPAE) model. It is a complete automated data
stream analytics framework that enables automatic, effective, and efficient
data analytics for IIoT systems in Industry 5.0. Experimental results on two
public IoT datasets demonstrate that the proposed framework outperforms
state-of-the-art methods for IIoT data stream analytics.
