SensorSA Catalogue Service

Catalogue services play an important role for the discovery of resources. Conventional catalogues usually contain meta-information about available data and service resources. A typical user query to a conventional catalogue could include ‘give me all services supporting standard interface x’ or ‘give me all datasets in a specific region, where the responsible party is y’.

A catalogue used for the discovery of sensor related meta-information needs to address additional requirements. Typical queries for such a catalogue differ from the conventional ones. Some examples may be: ‘give me all 'temperature' observations in Austria of May 2009’ or ‘give me all entries supporting a specific sensor type’. Looking at these queries it is clear that additional search criteria and specific meta-information are needed, which reflect the needs from the sensor domain.

SANY addressed these challenges in developing a meta-information schema for the catalogue which follows the Observation and Measurement Model (O&M) from the OGC (Cox). This model is used by Sensor Observation Services, which provide the meta-information necessary to answer the queries above. Besides conventional catalogue resource types (data and service) SANY defined the following new meta-information resource types according to the O&M Model for the catalogue:

  • The ‘Feature of Interest’ representing the observation target
  • The ‘Observed Property’ describing the phenomenon to be observed (e.g. temperature)
  • The ‘Procedure’ representing a specific sensor, sensor system(s) or algorithm(s) used by a system.

Supported Search Criteria

Following illustration shows the resource types of the so called SANY Application Schema for Meta-information. Each resource type supports mandatory meta-information sections (table of contents and core elements) containing common meta-information elements like ‘title’, ‘keywords’ or ‘source url’. Further meta-information can be provided by specifying optional sections. This has been used for the description of the new resource types. Additionally the ‘Procedure’ resource type supports a SensorML section for a detailed description of sensors.
SensorSA Resource Types
To support the possibilities of the SANY meta-information schema for the discovery process, the SensorSA Catalogue supports following search criteria ("queryables"):

  • ‘FeatureOfInterest’ allows search for specific feature of interests, like a specific test region.
  • ‘ObservedProperty’ allowa search for general phenomena, like ‘urn:ogc:phenomenon:temperature’.
  • ‘Procedure’ supporting the possibility to search for general sensor types (e.g. accelerometer) and sensor instances.
  • ‘DatasetType’ allows search for specific resource types like ‘Feature of Interest’, ‘Observed Property’ or ‘Procedure’.

Harvesting and Semantic Annotation

SensorSA Catalogue supports automatic creation of meta-information. Since the meta-information schema was designed according to O&M, which is also used by the SOS, the Catalogue relies on SOS operations GetCapabilities and DescribeSensor for automatically harvesting of the meta-information necessary to create SANY meta-information documents.

In addition to automatic creation of SANY meta-information documents, the SensorSA catalogue can also harvest INSPIRE meta-information. In this case the information provided by the SOS is not sufficient for the creation of
instance documents compliant with INSPIRE schemas and must be extended. For more information on this, please refer to chapter 8.1.3 of the SANY Book.

To overcome problems with the discovery of unharmonized URNs used in phenomenon or feature of interest descriptions of an SOS, the principle of semantic annotation has been tested. In the test example a SOS provides links to an ontology and a lifting schema, which describes the relation between the ontology concepts and the SOS phenomenons. In order to provide flexibility, the W3C recommendation ‘Semantic Annotation for WSDL and XML Schema’ (SAWSDL) has been used (Farell, Lausen).

SANY Catalogue Harvesting

The harvesting operation infers from the phenomenon to the related ontology concept and includes the concept into the created meta-information document. The catalogue client provides the user access to the used ontology. The advantage is, that a user using the ontology concepts for his search will get more results than a user performing a search with a-priori knowledge of phenomenons available in the catalogue: a search for the observable property ‘relativeHumidity’ leads to results of a specific SOS. A search for ‘rf ’ leads to results of another SOS using this observable identifier for
the very same phenomenon. But a search using the ontology concept ‘relative_moisture’ related to meteorology leads to results of both SOS.