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An Integrated GIS-Expert System Framework for Live Hazard Monitoring and Detection

View Article: PubMed Central - PubMed

ABSTRACT

In the context of hazard monitoring, using sensor web technology to monitor and detect hazardous conditions in near-real-time can result in large amounts of spatial data that can be used to drive analysis at an instrumented site. These data can be used for decision making and problem solving, however as with any analysis problem the success of analyzing hazard potential is governed by many factors such as: the quality of the sensor data used as input; the meaning that can be derived from those data; the reliability of the model used to describe the problem; the strength of the analysis methods; and the ability to effectively communicate the end results of the analysis. For decision makers to make use of sensor web data these issues must be dealt with to some degree. The work described in this paper addresses all of these areas by showing how raw sensor data can be automatically transformed into a representation which matches a predefined model of the problem context. This model can be understood by analysis software that leverages rule-based logic and inference techniques to reason with, and draw conclusions about, spatial data. These tools are integrated with a well known Geographic Information System (GIS) and existing geospatial and sensor web infrastructure standards, providing expert users with the tools needed to thoroughly explore a problem site and investigate hazards in any domain.

No MeSH data available.


Related in: MedlinePlus

ENGINE Transformation Chain.
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f4-sensors-08-00830: ENGINE Transformation Chain.

Mentions: This transformation is achieved using a series of XSLT templates which are used to map the various structures found in the SensorML and O&M documents to concepts in the OWL ontology. When a structure is encountered in the input document, the corresponding OWL concept is created in the output document, along with any relationships and attributes that are required. This process continues for all structures in the input document. Our REASON system is built to work on knowledge stored in the CLIPS language so that it can integrate with other tools (such as ArcGIS) and analysis code written in other languages such as C++ or Java. Therefore, we must undertake an additional step of converting the OWL representation of our ontology into a CLIPS representation. Fortunately, the Protege ontology editor [23] provides just such a facility. The editor can be used to load an OWL ontology and export that ontology as CLIPS code, and since the source code is available, this functionality was automated using some Java code. An overarching controller program called ENGINE (ENcoding Geospatial INformation and Expertise) controls the conversion of SensorML and O&M documents through to CLIPS code with a single command (Figure 4). This automation makes it simple for an SDSS (or an adventurous expert user) to request that their data be transformed into CLIPS without having to worry about the inner workings of the transformation engines.


An Integrated GIS-Expert System Framework for Live Hazard Monitoring and Detection
ENGINE Transformation Chain.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3927503&req=5

f4-sensors-08-00830: ENGINE Transformation Chain.
Mentions: This transformation is achieved using a series of XSLT templates which are used to map the various structures found in the SensorML and O&M documents to concepts in the OWL ontology. When a structure is encountered in the input document, the corresponding OWL concept is created in the output document, along with any relationships and attributes that are required. This process continues for all structures in the input document. Our REASON system is built to work on knowledge stored in the CLIPS language so that it can integrate with other tools (such as ArcGIS) and analysis code written in other languages such as C++ or Java. Therefore, we must undertake an additional step of converting the OWL representation of our ontology into a CLIPS representation. Fortunately, the Protege ontology editor [23] provides just such a facility. The editor can be used to load an OWL ontology and export that ontology as CLIPS code, and since the source code is available, this functionality was automated using some Java code. An overarching controller program called ENGINE (ENcoding Geospatial INformation and Expertise) controls the conversion of SensorML and O&M documents through to CLIPS code with a single command (Figure 4). This automation makes it simple for an SDSS (or an adventurous expert user) to request that their data be transformed into CLIPS without having to worry about the inner workings of the transformation engines.

View Article: PubMed Central - PubMed

ABSTRACT

In the context of hazard monitoring, using sensor web technology to monitor and detect hazardous conditions in near-real-time can result in large amounts of spatial data that can be used to drive analysis at an instrumented site. These data can be used for decision making and problem solving, however as with any analysis problem the success of analyzing hazard potential is governed by many factors such as: the quality of the sensor data used as input; the meaning that can be derived from those data; the reliability of the model used to describe the problem; the strength of the analysis methods; and the ability to effectively communicate the end results of the analysis. For decision makers to make use of sensor web data these issues must be dealt with to some degree. The work described in this paper addresses all of these areas by showing how raw sensor data can be automatically transformed into a representation which matches a predefined model of the problem context. This model can be understood by analysis software that leverages rule-based logic and inference techniques to reason with, and draw conclusions about, spatial data. These tools are integrated with a well known Geographic Information System (GIS) and existing geospatial and sensor web infrastructure standards, providing expert users with the tools needed to thoroughly explore a problem site and investigate hazards in any domain.

No MeSH data available.


Related in: MedlinePlus