Concept drift detection techniques can be used to discover substantial changes of the patterns encoded in data streams in real-time. If left unaddressed, these changes can render deployed machine learning models unreliable because their training data no longer matches the patterns present in the data stream. Most algorithms proposed in the literature depend on the immediate availability of ground truth class labels. This is unrealistic for many applications due to the associated cost of labeling. Therefore, this study reviews the availability of fully unsupervised concept drift detectors, which can operate entirely without labeled data. Ten algorithms are analyzed in terms of architectural choices, core ideas and assumptions about data because they fulfilled several inclusion criteria designed to ensure faithful and reliable implementations. Seven of these algorithms are evaluated with common concept drift detection metrics on eleven real-world data streams; the remaining three performed too slow or depended on chance. Based on the results of these experiments, three concept drift detectors—Discriminative Drift Detector, Image-Based Drift Detector and Semi-Parametric Log-Likelihood—can be recommended depending on the desired target metric. This study further reveals issues with the evaluation metrics Mean Time Ratio and lift-per-drift. Finally, it highlights open research challenges.

Autonomy is the core capability of future systems, and architecture design is one of the critical issues in system development and implementation. To discuss the architecture of autonomous systems in the future, this paper reviews the developing progress of architectures from automation systems to autonomous systems. Firstly, the autonomy and autonomous systems in different fields are summarized. The article classifies and summarizes the architecture of typical automated systems and infer three suggestions for building an autonomous system architecture: extensibility, evolvability, and collaborability. Accordingly, this paper builds an autonomous waterborne transportation system, and the architecture is composed of the object layer, cyberspace layer, cognition layer, and application layer, the proposed suggestions made in the construction of the architecture are reflected in the inter-relationships at all layers. Through the cooperation of four layers, the autonomous waterborne transportation system can autonomously complete the system functions, such as system control and transportation service. In the end, the characteristics of autonomous systems are concluded, from which the future primary research directions and the challenges of autonomous systems are provided.