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Semantic Event Processing in ENVISION

Semantic Event Processing in ENVISION. Alejandro Llaves , Patrick Maué , Henry Michels, & Marcell Roth Institute for Geoinformatics University of Muenster. Overview. Intro Semantic Sensor Web & Event Processing Approach Semantic Annotations for Sensor Data Services

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Semantic Event Processing in ENVISION

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

  2. 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

  3. 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

  4. Semantic Sensor Web & Event Processing Sensor Web • Why Event Processing? • Semantic Event Processing „Useofsemanticeventmodelsandrulestoenhancetheresultsof Event Processing.“ [Teymourian & Paschke, 2009] • Semantic • Enablement (SWE)

  5. Approach (1/3) Semantic Annotations for Sensor Data Services: Extending Semantic annotations in OGC standards[Maué et al., 2009]

  6. Approach (2/3) A Layered Event Ontology Model • The Event-Observation ontology (W3C’s SSN ontology extension) • Domain micro-ontologies • Example:

  7. Approach (3/3) Semantic Event Processing – Architecture Overview

  8. 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

  9. 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. 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

  10. 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/

  11. 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/

  12. Thanks! http://www.envision-project.eu/ alejandro.llaves@uni-muenster.de

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