Time series are the primary data type used to record dynamic system measurements and
generated in great volume by both physical sensors and online processes (virtual sensors) …

Efficient anomaly detection and diagnosis in multivariate time-series data is of great
importance for modern industrial applications. However, building a system that is able to …

The synergy of distributed frameworks with Artificial Intelligence (AI) is pivotal for advancing
applications in cloud computing. This review focuses on AI-powered applications in …

In the context of big data, if the task of multivariate time series data anomaly detection cannot
be performed efficiently and accurately, it will bring great security risks to industrial systems …

Logs are extensively used during the development and maintenance of software systems.
They collect runtime events and allow tracking of code execution, which enables a variety of …

With the advent of the big data era, the detection of anomalies in time series data, especially
multidimensional time series data, has received a great deal of attention from researchers in …

Prompt and accurate detection of system anomalies is essential to ensure the reliability of
software systems. Unlike manual efforts that exploit all available run-time information …

Purpose In this study, the authors seek to introduce ways that show that in the age of artificial
intelligence (AI), selective dissemination of information (SDI) performance can be greatly …

Detecting anomalies in massive volumes of multivariate time series data, particularly in the
IoT domain, is critical for maintaining stable systems. Existing anomaly detection models …

With the rapid development of deep learning, researchers are actively exploring its
applications in the field of industrial anomaly detection. Deep learning methods differ …