当TEDDY遇到GrizzLY:触发道路除冰操作的时间依赖性发现

Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining Pub Date : 2013-08-11 DOI:10.1145/2487575.2487706

C. Robardet, Vasile-Marian Scuturici, M. Plantevit, A. Fraboulet

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引用次数: 1

摘要

多个传感器数据源之间的时间依赖关系将两种类型的事件联系起来，如果其中一种事件在一定时间间隔内重复出现，另一种事件就会出现。TEDDY算法旨在发现这种依赖关系，通过chi2测试识别静态显著的时间间隔。我们介绍了如何在GrizzLY项目中使用这些依赖关系来解决环境和技术问题:道路除冰。该项目旨在基于多个传感器网络对当地大气现象的测量，明智地组织城市地区的除冰作业。根据这些数据建立了一个基于空间和时间依赖性的模型来预测冰冻警报。

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

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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Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining

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