1 citations · 2 across the 13 of their papers we have counts for
13 papers
Collaborative Streaming Anomaly Detection with Interactive Explanations and Ensemble Consensus
Diogo Risca, Afonso Lourenço, Ricardo Martins +1
We present a collaborative streaming anomaly detection system for high-speed data streams that explicitly integrates human analysts into the decision loop. The system combines hete…
Streaming Hierarchical Inference with Tabular Foundation Models
Vitor Crista, Afonso Lourenço, Diogo Martinho +1
Tabular Foundation Models (TFMs) have recently demonstrated strong predictive performance through in-context learning, but their deployment in high-throughput data streams remains…
Pitfalls of Unlabeled Disagreement-Based Drift Detection in Streaming Tree Ensembles
Lara Sá Neves, Afonso Lourenço, Lizy K. John +1
Detecting concept drift in high-speed data streams remains challenging, particularly when models must operate on unlabeled data and avoid false alarms caused by benign shifts. Whil…
Exploring a Virtual Pet to Provide Context Notifications in a Tourism Recommender System: a Pilot Study
Patrícia Alves, Joana Neto, Ana Barreiro +5
While context-aware personalization has been widely explored in modern tourism Recommender Systems (RS), the delivery of real-time notifications remains a significant design challe…
Axle Sensor Fusion for Online Continual Wheel Fault Detection in Wayside Railway Monitoring
Afonso Lourenço, Francisca Osório, Diogo Risca +1
Reliable and cost-effective maintenance is essential for railway safety, particularly at the wheel-rail interface, which is prone to wear and failure. Predictive maintenance framew…
Bridging Streaming Continual Learning via In-Context Large Tabular Models
Afonso Lourenço, João Gama, Eric P. Xing +1
In streaming scenarios, models must learn continuously, adapting to concept drifts without erasing previously acquired knowledge. However, existing research communities address the…
Explainable Anomaly Detection for Industrial IoT Data Streams
Ana Rita Paupério, Diogo Risca, Afonso Lourenço +2
Industrial maintenance is being transformed by the Internet of Things and edge computing, generating continuous data streams that demand real-time, adaptive decision-making under l…
Online hierarchical partitioning of the output space in extreme multi-label data stream
Lara Neves, Afonso Lourenço, Alberto Cano +1
Mining data streams with multi-label outputs poses significant challenges due to evolving distributions, high-dimensional label spaces, sparse label occurrences, and complex label…
Continual learning for rotating machinery fault diagnosis with cross-domain environmental and operational variations
Diogo Risca, Afonso Lourenço, Goreti Marreiros
Although numerous machine learning models exist to detect issues like rolling bearing strain and deformation, typically caused by improper mounting, overloading, or poor lubricatio…
Boosting-inspired online learning with transfer for railway maintenance
Diogo Risca, Afonso Lourenço, Goreti Marreiros
The integration of advanced sensor technologies with deep learning algorithms has revolutionized fault diagnosis in railway systems, particularly at the wheel-track interface. Alth…
On-device edge learning for IoT data streams: a survey
Afonso Lourenço, João Rodrigo, João Gama +1
This literature review explores continual learning methods for on-device training in the context of neural networks (NNs) and decision trees (DTs) for classification tasks on smart…
In-context Learning of Evolving Data Streams with Tabular Foundational Models
Afonso Lourenço, João Gama, Eric P. Xing +1
State-of-the-art data stream mining has long drawn from ensembles of the Very Fast Decision Tree, a seminal algorithm honored with the 2015 KDD Test-of-Time Award. However, the eme…
DFDT: Dynamic Fast Decision Tree for IoT Data Stream Mining on Edge Devices
Afonso Lourenço, João Rodrigo, João Gama +1
The Internet of Things generates massive data streams, with edge computing emerging as a key enabler for online IoT applications and 5G networks. Edge solutions facilitate real-tim…