3 citations · 4 across the 6 of their papers we have counts for
4 papers · 1 filter
Real-Time Anomaly Detection in Data Centers for Log-based Predictive Maintenance using an Evolving Fuzzy-Rule-Based Approach
Leticia Decker, Daniel Leite, Luca Giommi +1
Detection of anomalous behaviors in data centers is crucial to predictive maintenance and data safety. With data centers, we mean any computer network that allows users to transmit…
EGFC: Evolving Gaussian Fuzzy Classifier from Never-Ending Semi-Supervised Data Streams -- With Application to Power Quality Disturbance Detection and Classification
Daniel Leite, Leticia Decker, Marcio Santana +1
Power-quality disturbances lead to several drawbacks such as limitation of the production capacity, increased line and equipment currents, and consequent ohmic losses; higher opera…
Comparison of Evolving Granular Classifiers applied to Anomaly Detection for Predictive Maintenance in Computing Centers
Leticia Decker, Daniel Leite, Fabio Viola +1
Log-based predictive maintenance of computing centers is a main concern regarding the worldwide computing grid that supports the CERN (European Organization for Nuclear Research) p…
Unsupervised Fuzzy eIX: Evolving Internal-eXternal Fuzzy Clustering
Charles Aguiar, Daniel Leite
Time-varying classifiers, namely, evolving classifiers, play an important role in a scenario in which information is available as a never-ending online data stream. We present a ne…