Paper Info
Title
When TEDDY meets GrizzLY: temporal dependency discovery for triggering road deicing operations
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 chi2 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.
Year
DOI
Venue
2013
10.1145/2487575.2487706
KDD
Keywords
Field
DocType
statically significant time interval,temporal dependency-based model,deicing operation,multiple sensor data source,temporal dependency discovery,certain time interval,teddy algorithm,chi2 test,grizzly project,temporal dependency,sensor network measure
Data mining,Computer science,Wireless sensor network,Urban area
Conference
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Céline Robardet169660.36
Vasile-Marian Scuturici211920.95
Marc Plantevit323330.78
Antoine Fraboulet434629.02