178 citations · 369 across the 22 of their papers we have counts for
22 papers
Drift-Aware Variational Autoencoder-based Anomaly Detection with Two-level Ensembling
Jin Li, Kleanthis Malialis, Christos G. Panayiotou +1
In today's digital world, the generation of vast amounts of streaming data in various domains has become ubiquitous. However, many of these data are unlabeled, making it challengin…
Low Rank Transformer for Multivariate Time Series Anomaly Detection and Localization
Charalampos Shimillas, Kleanthis Malialis, Konstantinos Fokianos +1
Multivariate time series (MTS) anomaly diagnosis, which encompasses both anomaly detection and localization, is critical for the safety and reliability of complex, large-scale real…
Unsupervised Online Detection of Pipe Blockages and Leakages in Water Distribution Networks
Jin Li, Kleanthis Malialis, Stelios G. Vrachimis +1
Water Distribution Networks (WDNs), critical to public well-being and economic stability, face challenges such as pipe blockages and background leakages, exacerbated by operational…
SiameseDuo++: Active Learning from Data Streams with Dual Augmented Siamese Networks
Kleanthis Malialis, Stylianos Filippou, Christos G. Panayiotou +1
Data stream mining, also known as stream learning, is a growing area which deals with learning from high-speed arriving data. Its relevance has surged recently due to its wide rang…
Online Detection of Water Contamination Under Concept Drift
Jin Li, Kleanthis Malialis, Stelios G. Vrachimis +1
Water Distribution Networks (WDNs) are vital infrastructures, and contamination poses serious public health risks. Harmful substances can interact with disinfectants like chlorine,…
Incremental Learning with Concept Drift Detection and Prototype-based Embeddings for Graph Stream Classification
Kleanthis Malialis, Jin Li, Christos G. Panayiotou +1
Data stream mining aims at extracting meaningful knowledge from continually evolving data streams, addressing the challenges posed by nonstationary environments, particularly, conc…