Sander Vanden Hautte

Journal Article
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Benchmarking whole knowledge graph embedding techniques

Pieter Bonte (UGent) , Sander Vanden Hautte, Filip De Turck (UGent) , Sofie Van Hoecke (UGent) and Femke Ongenae (UGent)

(2024) INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING. 34(1). p.163-184

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Hierarchical pattern matching for anomaly detection in time series

Matthias Van Onsem (UGent) , Dieter De Paepe, Sander Vanden Hautte, Pieter Bonte (UGent) , Veerle Ledoux (UGent) , Annelies Lejon (UGent) , Femke Ongenae (UGent) , D. Dreesen and Sofie Van Hoecke (UGent)

(2022) COMPUTER COMMUNICATIONS. 193. p.75-81

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A complete software stack for IoT time-series analysis that combines semantics and machine learning-lessons learned from the dyversify project

Dieter De Paepe, Sander Vanden Hautte, Bram Steenwinckel (UGent) , Pieter Moens (UGent) , Jasper Vaneessen (UGent) , Steven Vandekerckhove, Bruno Volckaert (UGent) , Femke Ongenae (UGent) and Sofie Van Hoecke (UGent)

(2021) APPLIED SCIENCES-BASEL. 11(24).

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Event-driven dashboarding and feedback for improved event detection in predictive maintenance applications

Pieter Moens (UGent) , Sander Vanden Hautte, Dieter De Paepe, Bram Steenwinckel (UGent) , Stijn Verstichel (UGent) , Steven Vandekerckhove, Femke Ongenae (UGent) and Sofie Van Hoecke (UGent)

(2021) APPLIED SCIENCES-BASEL. 11(21).

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FLAGS : a methodology for adaptive anomaly detection and root cause analysis on sensor data streams by fusing expert knowledge with machine learning

Bram Steenwinckel (UGent) , Dieter De Paepe, Sander Vanden Hautte, Pieter Heyvaert (UGent) , Mohamed Bentefrit, Pieter Moens (UGent) , Anastasia Dimou (UGent) , Bruno Van Den Bossche (UGent) , Filip De Turck (UGent) , Sofie Van Hoecke (UGent) , et al.

(2021) FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE. 116. p.30-48

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Unsupervised anomaly detection for communication networks : an autoencoder approach

Pieter Bonte (UGent) , Sander Vanden Hautte, Annelies Lejon, Veerle Ledoux, Filip De Turck (UGent) , Sofie Van Hoecke (UGent) and Femke Ongenae (UGent)

(2020) IoT streams for data-driven predictive maintenance and IoT, edge, and mobile for embedded machine learning. In Communications in Computer and Information Science 1325. p.160-172

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Scalable fleet monitoring and visualization for smart machine maintenance and industrial IoT applications

Pieter Moens (UGent) , Vincent Bracke (UGent) , Colin Soete (UGent) , Sander Vanden Hautte, Diego Nieves Avendano (UGent) , Ted Ooijevaar, Steven Devos, Bruno Volckaert (UGent) and Sofie Van Hoecke (UGent)

(2020) SENSORS. 20(15).

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A generalized matrix profile framework with support for contextual series analysis

Dieter De Paepe, Sander Vanden Hautte, Bram Steenwinckel (UGent) , Filip De Turck (UGent) , Femke Ongenae (UGent) , Olivier Janssens (UGent) and Sofie Van Hoecke (UGent)

(2020) ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE. 90.

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A dynamic dashboarding application for fleet monitoring using semantic web of things technologies

Sander Vanden Hautte, Pieter Moens (UGent) , Joachim Van Herwegen (UGent) , Dieter De Paepe, Bram Steenwinckel (UGent) , Stijn Verstichel (UGent) , Femke Ongenae (UGent) and Sofie Van Hoecke (UGent)

(2020) SENSORS. 20(4).

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Conference Paper
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Towards adaptive anomaly detection and root cause analysis by automated extraction of knowledge from risk analyses

Bram Steenwinckel (UGent) , Pieter Heyvaert (UGent) , Dieter De Paepe, Olivier Janssens (UGent) , Sander Vanden Hautte, Anastasia Dimou (UGent) , Filip De Turck (UGent) , Sofie Van Hoecke (UGent) and Femke Ongenae (UGent)

(2018) Proceedings of the 9th international semantic sensor networks workshop, co-located with 17th international semantic web conference (ISWC 2018). In CEUR Workshop Proceedings 2213. p.17-31

Academic Bibliography

Sander Vanden Hautte

Benchmarking whole knowledge graph embedding techniques

Hierarchical pattern matching for anomaly detection in time series

A complete software stack for IoT time-series analysis that combines semantics and machine learning-lessons learned from the dyversify project

Event-driven dashboarding and feedback for improved event detection in predictive maintenance applications

FLAGS : a methodology for adaptive anomaly detection and root cause analysis on sensor data streams by fusing expert knowledge with machine learning

Unsupervised anomaly detection for communication networks : an autoencoder approach

Scalable fleet monitoring and visualization for smart machine maintenance and industrial IoT applications

A generalized matrix profile framework with support for contextual series analysis

A dynamic dashboarding application for fleet monitoring using semantic web of things technologies

Towards adaptive anomaly detection and root cause analysis by automated extraction of knowledge from risk analyses

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