Urban Anomaly Detection: a Use-Case for Participatory Infra-Structure Monitoring.
Urb-IoT(2016)
摘要
Internet-enabled, location aware smart phones with sensor inputs have led to novel urban infra-structure monitoring applications exploiting unprecedented high levels of citizen participation in dense metropolitan areas. For policy makers, it is a key task to keep track of trends and developments of reported infra-structure issues for understanding and effectively reacting to problems around a city, specially in their early stages. In contrast to previous strategies which consider only limited information such as text and geographic locations, we analyze the urban dynamics of crowdsourced collected data using an existing approach that considers a novel modeling of heterogeneous attributes and relationships in the data. First, the underlying data is modeled into a heterogeneous network, in which it's measured for each node its current level of anomalousness for a desired time interval (e.g. a week) and then the most anomalous network's subgraph is extracted and described by means of problem category, geographical area, time and participants. First experiments illustrate the effectiveness and efficiency of leveraging this anomaly detection approach in our use-case (participatory infra-structure monitoring).
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关键词
Anomaly Detection,Urban Computing,Crowdsourcing
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