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

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Presentation on theme: "Semantic Event Processing in ENVISION Alejandro Llaves, Patrick Maué, Henry Michels, & Marcell Roth Institute for Geoinformatics University of Muenster."— Presentation transcript:

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

2 17/05/2014 2

3 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 17/05/2014 3

4 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 17/05/2014 4

5 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] 17/05/ Enablement (SWE)Semantic

6 Approach (1/3) Semantic Annotations for Sensor Data Services: Extending Semantic annotations in OGC standards [Maué et al., 2009] 17/05/2014 6

7 Approach (2/3) A Layered Event Ontology Model 17/05/ The Event-Observation ontology (W3Cs SSN ontology extension) Domain micro-ontologies Example:

8 Approach (3/3) Semantic Event Processing – Architecture Overview 17/05/2014 8

9 Example of Use: Flood Monitoring in the Danube A flood monitoring ontology - Semantic annotation of a water level SOS 17/05/2014 9

10 Example of Use: Flood Monitoring in the Danube A flood monitoring ontology - 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. 17/05/ 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

11 Example of Use: Flood Monitoring in the Danube A flood monitoring ontology - 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 17/05/

12 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 17/05/

13 Thanks! 17/05/


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