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When TEDDY meets GrizzLY: temporal dependency discovery for triggering road deicing operations

Published:11 August 2013Publication History

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.

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  1. When TEDDY meets GrizzLY: temporal dependency discovery for triggering road deicing operations

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    • Published in

      cover image ACM Conferences
      KDD '13: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
      August 2013
      1534 pages
      ISBN:9781450321747
      DOI:10.1145/2487575

      Copyright © 2013 ACM

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 11 August 2013

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      KDD '13 Paper Acceptance Rate125of726submissions,17%Overall Acceptance Rate1,133of8,635submissions,13%

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