48th ACM Southeast Conference. ACMSE 2010. Oxford, Mississippi.  April 15-17, 2010.Provenance Aware Linked Sensor DataHarshalPatni, Satya S. Sahoo, Cory Henson, Amit P. ShethOhio Center of Excellence in Knowledge enabled Computing (Kno.e.sis) Wright State University, Dayton, OHSPOT2010 – 2nd Workshop on Trust and Privacy on the Social and Semantic Web
OUTLINE Motivating Scenario
 Provenance
 Sensor Provenance Management System (Sensor PMS)
 Workflow Implementation
 Future Work
 Conclusion3
Motivating ScenarioSpatial informationSensors in USAFind all the sensors which have observations related to a blizzard occurring in Nevada on 24th August 2005 at 11 AM Thematic informationTemporal information4
PROVENANCESpatial informationFind all the sensors which have observations related to a blizzard occurring in Nevada on 24th August 2005 at 11 AM Thematic informationTemporal informationPROVENANCE informationof the observation is required for SENSOR DISCOVERYPROVENANCE : History or Lineage of data entity5
Sensor PMSData capture phaseStore the Provenance Aware Sensor DataAnnotating data with Sensor Provenance Ontology6
Provenance CaptureProvenance Aware Linked Sensor DataWeather SensorsSensor DatasetGPS SensorsSatellite SensorsCamera Sensors7
Provenance RepresentationProvenance Aware Linked Sensor Data Annotate data using concepts in Provenance Sensor OntologySensor DatasetSensor Provenance Ontology8
Provenance Representation Sensor Ontology9
Provenance Representation10
ProvenanceStorageGeoNames Dataset: Geographic dataset contaning    	information about countries and 8 million place nameslocatedNearProvenance Aware Linked Sensor DataSensor DatasetPublicly AccessibleProvenance Aware Sensor is adding provenance to Linked Sensor Data (on LoD).11
Workflow ImplementationSensor Provenance OntologyMesoWest is a Project at University of Utah, Department of Meteorology  that collects observations for ~20,000 sensors in United StatesOpen Geo-Spatial Consortium standard (O&M) for encoding sensor descriptions and observations12

Provenance Aware Linked Sensor Data

Editor's Notes

  • #6 The main goal of this work is to model provenance within the sensors domain by extending the provenir upper ontology with the sensors ontology
  • #7 Provenance Capture – Data Generation PhaseProvenance Representation – data generated is annotated using the concepts in the Sensor Provenance OntologyProvenance Storage – the data annotated with provenance information is stored in the Virtuoso RDF store
  • #12 Once we have all this data openly accessible on the Linked Open Data Cloud it is possible to for anyone in the world to search for sensors using the provenance information as shown in the motivating scenario
  • #15 The main goal of this work is to model provenance within the sensors domain by extending the provenir upper ontology with the sensors ontology
  • #16 Change this page look to make it more descriptive