The dynamicity of real-world systems poses a significant challenge to deployed predictive
machine learning (ML) models. Changes in the system on which the ML model has been …

In recent years, the introduction and exploitation of innovative information technologies in
industrial contexts have led to the continuous growth of digital shop floor environments. The …

Data management and processing to enable predictive analytics in cyber physical systems
holds the promise of creating insight over underlying processes, discovering anomalous …

This work is driven by a practical question: corrections of Artificial Intelligence (AI) errors.
These corrections should be quick and non-iterative. To solve this problem without …

Machine Learning (ML) is quickly becoming a prominent method in many data management
components, including query optimizers which have recently shown very promising results …

Concept Drift is a phenomenon in which the underlying data distribution and statistical
properties of a target domain change over time, leading to a degradation of the model's …

Despite the significant improvements made by deep learning models, their adoption in real-
world dynamic applications is still limited. Concept drift is among the open issues preventing …

This paper proposes a management system designed to evaluate and enhance the
optimization degree within manufacturing operations for improved business planning. The …

Concept drift refers to changes in data distribution over time that can lead to performance
degradation of deep learning systems. Production models need to be continuously …

In the last few years, manufacturing systems are getting gradually transformed into smart
factories. In this context, an increasing number of information and communication …