Semantic Event Processing in ENVISION Alejandro Llaves, Patrick Maué, Henry Michels, & Marcell Roth Institute for Geoinformatics University of Muenster.

Semantic Event Processing in ENVISION

Alejandro Llaves, Patrick Maué, Henry Michels, & Marcell Roth

Institute for GeoinformaticsUniversity of Muenster

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Overview

Intro Semantic Sensor Web & Event Processing Approach

– Semantic Annotations for Sensor Data Services

– A Layered Event Ontology Model

– Semantic Event Processing – Architecture Overview

Example of Use: Flood Monitoring in the Danube Conclusion

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Intro

Integration of geospatial information across different communities Inferring occurrences (events) from time-series of observations

Motivation Lack of standardized methods to process and represent environmental

information describing change causes semantic interoperability problems

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Semantic Sensor Web & Event Processing

Sensor Web

Why Event Processing?

Semantic Event Processing

„Use of semantic event models and rules to enhance the results of Event Processing.“ [Teymourian & Paschke, 2009]

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Enablement (SWE)Semantic

Approach (1/3)

Semantic Annotations for Sensor Data Services: Extending Semantic annotations in OGC standards [Maué et al., 2009]

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Approach (2/3)

A Layered Event Ontology Model

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The Event-Observation ontology (W3C’s SSN ontology extension)

Domain micro-ontologies

Example:

Approach (3/3)

Semantic Event Processing – Architecture Overview

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Example of Use: Flood Monitoring in the Danube

A flood monitoring ontology - http://purl.org/ifgi/water/flood Semantic annotation of a water level SOS

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Example of Use: Flood Monitoring in the Danube

A flood monitoring ontology - http://purl.org/ifgi/water/flood Semantic annotation of a water level SOS Description of relevant situations: HighWaterLevel events

– Water level must be maintained below 69,59 metres at Iron Gates I.

– Water level must be maintained below 41,00 metres at Iron Gates II.

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SELECT *FROM WaterLevel.win:length(1)WHERE (sensor.id == 'IronGatesI') and(value >= 6959)

HighWaterLevel

SELECT *FROM WaterLevel.win:length(1)WHERE (sensor.id == 'IronGatesII') and(value >= 4100)

HighWaterLevel

Example of Use: Flood Monitoring in the Danube

A flood monitoring ontology - http://purl.org/ifgi/water/flood Semantic annotation of a water level SOS Description of relevant situations: HighWaterLevel events Event Subscription interface allows users subscribing to specific

situations to receive notifications– Video demo at http://www.envision-project.eu/resources/

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Conclusion

Summary– Applying Semantic Event Processing to time-series of sensor data

– The layered ontology model presented eases maintenance tasks and enables modularity

– Loosely coupled event-driven service oriented architecture

Contribution– Semantic Event Processing methodology that allows near real-time

analysing and integrating different views for the same event type

Current status and open issues– Upgrading EPS to pull heterogeneous sensor data

– A event pattern editor is under development

– SNB will be extended to work on additional use cases

– Ontologies http://www.envision-project.eu/resources/ontologies/

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Thanks!

http://www.envision-project.eu/

alejandro.llaves@uni-muenster.de

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