OpenGIS Sensor Interface Descriptors.
Bröring, A.; and Below, S.
Technical Report OGC 10-134, Open Geospatial Consortium, June 2010.
Paper
link
bibtex
@techreport{BroeringOGC10,
author = {Br\"{o}ring, Arne and Below, Stefan},
institution = {Open Geospatial Consortium},
keywords = {OGC\_standards,discovery,sensor\_web},
mendeley-tags = {OGC\_standards,discovery,sensor\_web},
month = jun,
number = {OGC 10-134},
title = {{OpenGIS Sensor Interface Descriptors}},
type = {OpenGIS Discussion Paper},
url = {http://portal.opengeospatial.org/files/?artifact\_id=39664},
year = {2010}
}
Handling the semantics of sensor observables within SWE discovery solutions.
Jirka, S.; Broring, A.; and Foerster, T.
In
2010 International Symposium on Collaborative Technologies and Systems, pages 322–329, May 2010. IEEE
Paper
link
bibtex
abstract
@inproceedings{Jirka2010,
abstract = {When searching for sensor data, sensor instances, or Sensor Web Enablement (SWE) services the description of the observed phenomenon plays an important role. Obviously, every user searching for sensor data needs to specify in which kind of sensor data he is interested. In current SWE applications, the information about the observed phenomenon is provided as a unique link encoded as a Uniform Resource Name (URN). However, relying on those URNs to perform string based search for sensor observables has serious drawbacks when it comes to realizing advanced sensor discovery tools as the meaning of the observables is ignored. This work presents an approach that makes use of semantic annotations of SWE resources. The presented solution relies on a dictionary for sensor observables, the Sensor Observable Registry (SOR). This dictionary comprises URNs identifying observables, definitions of these observables in natural language, and pointers to formal phenomenon definitions contained in ontologies. This makes it possible to rely on existing reasoning mechanisms for determining equivalent or related observables (e.g., specializations or generalizations) to the one specified by a user. Finally, an approach is presented, how the SOR can be used for enhancing the sensor discovery process by linking it to sensor catalogues and registries.},
author = {Jirka, Simon and Broring, Arne and Foerster, Theodor},
booktitle = {2010 International Symposium on Collaborative Technologies and Systems},
keywords = {OGC\_standards,discovery,sensor\_web},
mendeley-tags = {OGC\_standards,discovery,sensor\_web},
month = may,
pages = {322--329},
publisher = {IEEE},
title = {{Handling the semantics of sensor observables within SWE discovery solutions}},
url = {http://ifgi.uni-muenster.de/~arneb/SOR\_for\_SWE2010\_draft.pdf},
year = {2010}
}
When searching for sensor data, sensor instances, or Sensor Web Enablement (SWE) services the description of the observed phenomenon plays an important role. Obviously, every user searching for sensor data needs to specify in which kind of sensor data he is interested. In current SWE applications, the information about the observed phenomenon is provided as a unique link encoded as a Uniform Resource Name (URN). However, relying on those URNs to perform string based search for sensor observables has serious drawbacks when it comes to realizing advanced sensor discovery tools as the meaning of the observables is ignored. This work presents an approach that makes use of semantic annotations of SWE resources. The presented solution relies on a dictionary for sensor observables, the Sensor Observable Registry (SOR). This dictionary comprises URNs identifying observables, definitions of these observables in natural language, and pointers to formal phenomenon definitions contained in ontologies. This makes it possible to rely on existing reasoning mechanisms for determining equivalent or related observables (e.g., specializations or generalizations) to the one specified by a user. Finally, an approach is presented, how the SOR can be used for enhancing the sensor discovery process by linking it to sensor catalogues and registries.
Using Semantic Annotation for Knowledge Extraction from Geographically Distributed and Heterogeneous Sensor Data.
Moraru, A.; Fortuna, C.; and Mladenic, D.
In
4th International Workshop on Knowledge Discovery from Sensor Data (SensorKDD-2010), 2010.
Paper
link
bibtex
@inproceedings{Moraru2010,
author = {Moraru, Alexandra and Fortuna, Carolina and Mladenic, Dunja},
booktitle = {4th International Workshop on Knowledge Discovery from Sensor Data (SensorKDD-2010)},
keywords = {discovery,ontology,semantic\_markup,semantic\_sensor\_web},
mendeley-tags = {discovery,ontology,semantic\_markup,semantic\_sensor\_web},
title = {{Using Semantic Annotation for Knowledge Extraction from Geographically Distributed and Heterogeneous Sensor Data}},
url = {http://www.ornl.gov/sci/knowledgediscovery/SensorKDD-2010/SensorKDD'10\_Proceedings.pdf},
year = {2010}
}
Linked Sensor Data.
Patni, H; Henson, C; and Sheth, A
In
2010 International Symposium on Collaborative Technologies and Systems, (CTS 2010), pages 362–370, 2010. IEEE
Paper
link
bibtex
@inproceedings{Patni2010a,
author = {Patni, H and Henson, C and Sheth, A},
booktitle = {2010 International Symposium on Collaborative Technologies and Systems, (CTS 2010)},
keywords = {OGC\_standards,SSN,discovery,linked\_data,observation},
mendeley-tags = {OGC\_standards,SSN,discovery,linked\_data,observation},
pages = {362--370},
publisher = {IEEE},
title = {{Linked Sensor Data}},
url = {http://knoesis.wright.edu/library/download/CTS-LinkedSensorData.pdf},
year = {2010}
}