Concept drift describes unforeseeable changes in the underlying distribution of streaming
data overtime. Concept drift research involves the development of methodologies and …

Change points are abrupt variations in time series data. Such abrupt changes may represent
transitions that occur between states. Detection of change points is useful in modelling and …

Anomaly detection within multivariate time series (MTS) is an essential task in both data
mining and service quality management. Many recent works on anomaly detection focus on …

Change Point Detection (CPD) methods identify the times associated with changes in the
trends and properties of time series data in order to describe the underlying behaviour of the …

Closely monitoring service performance and detecting anomalies are critical for Internet-
based services. However, even though dozens of anomaly detectors have been proposed …

Change Point Detection (CPD) is the problem of discovering time points at which the
behavior of a time series changes abruptly. In this paper, we present a novel real-time …

Abstract The Hilbert–Schmidt independence criterion (HSIC) was originally designed to
measure the statistical dependence of the distribution-based Hilbert space embedding in …

Syslogs on switches are a rich source of information for both post-mortem diagnosis and
proactive prediction of switch failures in a datacenter network. However, such information …

Data-driven decision-making (D^ 3 D 3 M) is often confronted by the problem of uncertainty
or unknown dynamics in streaming data. To provide real-time accurate decision solutions …

Content Delivery Networks (CDNs) are critical for providing good user experience of cloud
services. CDN providers typically collect various multivariate Key Performance Indicators …