Abstract Model calibration, critical to the success and safety of clinical prediction models,
deteriorates over time in response to the dynamic nature of clinical environments. To support …

The world is constantly changing, and so are the massive amount of data produced.
However, only a few studies deal with online class imbalance learning that combines the …

Most methods for classifying data streams operate under the hypothesis that the distribution
of classes is balanced. Unfortunately, the phenomenon of class imbalance widely exists in …

Window aggregations and windowed joins are central operators of modern real-time
analytic workloads and significantly impact the performance of stream processing systems …

Abstract The Internet of Things (IoT) presents a novel computing architecture for data
management: a distributed, highly dynamic, and heterogeneous environment of massive …

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 …

Abstract Machine learning (ML) models often fail with data that deviates from their training
distribution. This is a significant concern for ML-enabled devices as data drift may lead to …

Event-time based stream processing is concerned with analyzing data with respect to its
generation time. In most of the cases, data gets delayed during its journey from the source …

Adapting to drifting data streams is a significant challenge in online learning. Concept drift
must be detected for effective model adaptation to evolving data properties. Concept drift …