Mike Botts (UAH), Tony Cook (UAH), Janet Fredericks (WHOI), Julie Bosch (NCDDC)

1. Supporting QA/QC for Ocean Observations using Sensor Web Enablement (SWE)and SensorMLAugust 2008 Mike Botts (UAH), Tony Cook (UAH), Janet Fredericks (WHOI), Julie Bosch (NCDDC)

2. Why is SensorML Important? • Importance: • Discovery of sensors and processes / plug-n-play sensors – SensorML is the means by which sensors and processes make themselves and their capabilities known; describes inputs, outputs and taskable parameters • Observation lineage – SensorML provides history of measurement and processing of observations; supports quality knowledge of observations • On-demand processing – SensorML supports on-demand derivation of higher-level information (e.g. geolocation or products) without a priori knowledge of the sensor system • Intelligent, autonomous sensor network – SensorML enables the development of taskable, adaptable sensor networks, and enables higher-level problem solving anticipated from the Semantic Web

3. Where can QA/QC be supported? • Sensor Descriptions (SensorML-SWE Common) • Discovery • Capabilities • Detector Parameters • Error curves, latency • Accuracy of parameters and output values • QC Process Description • Defined as SensorML Process • Included as part of lineage of Observation (see below) • Observations (O&M-Swe Common) • QA/QC expressions for the values • e.g. accuracy, confidence levels, etc • either constants or themselves as output values • Lineage of the Observation (procedure property) • SensorML System and ProcessChains

4. QA/QC metadata suitable for discoveryof sensors and processes Find all remote sensor systems measuring in the visible spectral range with ground resolution less than 20m. Key is to define terms for QA/QC characteristics

5. QA/QC in Detectors, Actuators, and Sensor Systems • Detector and Actuator parameters support some QA/QC • Sensitivity, latency, error curves, etc • Need to make certain that any additional QA/QC parameters are supported in sensor profiles for Oceans Community • Any SWE Common values can have QA/QC properties • Accuracy, Confidence, etc. • Useful for defining quality and confidence of • Outputs of sensors • Observation values in O&M • quality values can be constant or defined as output of sensors and processes

6. What does an SOS provide? Buoy Data at TAMU • getCapabilities: • http://sos-ws.tamu.edu/tethys/madis?request=GetCapabilities&version=1.0&service=SOS •  getObservation: • http://sos-ws.tamu.edu/tethys/madis?request=GetObservation&version=1.0&service=SOS&offering=44029&format=text/xml;%20subtype=%22om/1.0%22&observables=http://marinemetadata.org/cf%23sea_surface_temperature,http://marinemetadata.org/cf%23air_pressure_at_sea_level,http://marinemetadata.org/cf%23air_temperature&time=2008-08-22T12:00:00Z/2008-08-23T12:00:00Z • describeSensor: • http://vastserver.nsstc.uah.edu/vast/cdip?service=SOS&version=1.0&request=DescribeSensor&procedure=urn:CDIP:process:CDIP_144

7. SensorMLWorkhorse Observationwith flags SensorML Supports description of Lineage for an Observation SensorML:ADCP System SOS serving processed wave data SensorML:QC Process Within an Observation, SensorML can describe how that Observation came to be using the “procedure” property; Let’s explore the pieces of this graph in the following slides

8. Waves Lineage • Janet’s Slide

9. SensorMLWorkhorse Observationwith flags Sensor Description SensorML:ADCP System SOS serving processed wave data SensorML:QC Process The lineage can include the description of the sensor itself (click on yellow box to see example); Notice the Quality measures within capabilities and the listing of events in history All or part of the output of this sensor system can serve as input to the QC process chain

10. SensorMLWorkhorse Observationwith flags QC Test Descriptions SensorML:ADCP System SOS serving processed wave data SensorML:QC Process Individual QC tests can make up the components of the QC Process chain (click on yellow boxes to see examples); These could include, for instance, range test, threshold test, spike test, etc. Julie Bosch’s list

11. SensorMLWorkhorse Observationwith flags QC Process Chain Description SensorML:ADCP System SOS serving processed wave data SensorML:QC Process The QC Process can be described as a separate process chain from the sensor itself ;

12. SensorMLWorkhorse Observationwith flags The complete ADCP Observation Lineage SensorML:ADCP System SOS serving processed wave data SensorML:QC Process The sensor and QC process description makes up the lineage of the Observation; The output of this process should be reflected in the components of the observation

13. SensorMLWorkhorse Observationwith flags The ADCP Observation procedure SensorML:ADCP System SOS serving processed wave data SensorML:QC Process The Observation components should reflect the output from the complete processing chain but include the encoding as well (could be ascii, binary, or a particular simple MIME type) The “procedure” property of the Observation should point to the lineage process.

14. SensorMLWorkhorse Observationwith flags The ADCP SOS SensorML:ADCP System SOS serving processed wave data SensorML:QC Process The Observation can then be served from a Sensor Observation Service (SOS) The capabilities document of the SOS should reflect the data components of the Observation

15. Conclusions • SensorML and SWE can support QA/QC in several ways • Quality capabilities of sensors • Quality specifications for individual data components in observations and sensor output • SensorML description of individual QC tests and process chain • Lineage specification of observation • The use of SWE Common data components throughout SWE specifications provides many benefits • Robustness – uom, semantics, constraints, quality specification • Consistency of property and data component descriptions for encodings and services • Reflection of common data components within inputs, outputs, and parameters of processes and the components of an observation