When TEDDY meets GrizzLY: Temporal Dependency Discovery for Triggering Road Deicing Operations (Demo)

Céline Robardet 1 Vasile-Marian Scuturici 2 Marc Plantevit 1 Antoine Fraboulet
1 DM2L - Data Mining and Machine Learning
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
2 BD - Base de Données
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : Temporal dependencies between multiple sensor data sources link two types of events if the occurrence of one is repeatedly followed by the appearance of the other in a certain time interval. TEDDY algorithm aims at discovering such dependencies, identifying the statically significant time intervals with a $\chi^2$ test. We present how these dependencies can be used within the GrizzLY project to tackle an environmental and technical issue: the deicing of the roads. This project aims to wisely organize the deicing operations of an urban area, based on several sensor network measures of local atmospheric phenomena. A spatial and temporal dependency-based model is built from these data to predict freezing alerts.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-01339189
Contributor : Équipe Gestionnaire Des Publications Si Liris <>
Submitted on : Wednesday, June 29, 2016 - 3:48:17 PM
Last modification on : Thursday, November 21, 2019 - 2:31:13 AM

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CNRS | EC-LYON | UNIV-LYON2 | LIRIS | INSA-LYON | UNIV-LYON1 | INSA-GROUPE | LABEXIMU | UDL

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Céline Robardet, Vasile-Marian Scuturici, Marc Plantevit, Antoine Fraboulet. When TEDDY meets GrizzLY: Temporal Dependency Discovery for Triggering Road Deicing Operations (Demo). KDD, Aug 2013, Chicago, IL, United States. pp.1490-1493 ⟨10.1145/2487575.2487706⟩. ⟨hal-01339189⟩

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