Discovering Geosensor Data By Means of an Event Abstraction Layer

Discovering Geosensor Data By Means of an Event Abstraction Layer

Alejandro Llaves (University of Muenster, Germany) and Thomas Everding (University of Muenster, Germany)
DOI: 10.4018/978-1-4666-2038-4.ch120
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Environmental monitoring is a critical process in areas potentially affected by natural disasters. Nowadays, the distributed processing of vast amounts of heterogeneous sensor data in real time is a challenging task. Event processing tools allow creating an event abstraction layer on top of sensor data. Users can define event patterns to filter in real-time the information they are interested in and avoid irrelevant data. Extreme events are usually related to other environmental occurrences, e.g. landslides are related (among others) to precipitations and earthquakes. To be able to determine whether an occurrence could potentially lead to an extreme event, domain knowledge is necessary. Ontologies are helpful for this task, since they are able to capture a representation of knowledge as a set of concepts and relations, within a specific domain. The research presented in this chapter aims at combining event-processing tools with semantic technologies to improve the discovery of environmental data.
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In this section, existing event processing technologies and eventing standards are described. Previous research on integration of landslide information is presented, as well as some literature documenting threshold-based approaches for the initiation of landslides.

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