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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.


REASON Evaluation Loop.
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Related In: Results  -  Collection


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f2-sensors-08-00830: REASON Evaluation Loop.

Mentions: Figure 2 shows the detailed methodology of the REASON workflow loop in the case of interaction with an SOS server. The abstracted data source mechanism is used to connect to an SOS and retrieve values in order to drive analysis. When the system in initialized the ontologies are loaded into the CLIPS knowledge base. These ontologies contain the majority of the code that are used to operate the system. Initialization is completed when the sensor descriptions are retrieved from the SOS and converted into CLIPS code and stored in the knowledge base. The “Bind Data Sources” step consists of binding an instance of the data source class to the CLIPS code representing the sensors. This tells REASON where it can retrieve information about the given sensor, including new measurements. The “Update” portion of the workflow retrieves the newest observations for the sensors through SOS queries. The resulting XML documents are converted on-the-fly into CLIPS code which is used to update the GIS layers and CLIPS code associated with the sensors. Evaluation is then carried out on the new values as defined in the application ontology (see Section 4 for a sample decision tree that is used to analyze sensor data). When evaluation is completed, new values are acquired from the data source and the process repeats itself until the system is told to release the resources associated with the data source and terminate.


An Integrated GIS-Expert System Framework for Live Hazard Monitoring and Detection
REASON Evaluation Loop.
© Copyright Policy
Related In: Results  -  Collection

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

f2-sensors-08-00830: REASON Evaluation Loop.
Mentions: Figure 2 shows the detailed methodology of the REASON workflow loop in the case of interaction with an SOS server. The abstracted data source mechanism is used to connect to an SOS and retrieve values in order to drive analysis. When the system in initialized the ontologies are loaded into the CLIPS knowledge base. These ontologies contain the majority of the code that are used to operate the system. Initialization is completed when the sensor descriptions are retrieved from the SOS and converted into CLIPS code and stored in the knowledge base. The “Bind Data Sources” step consists of binding an instance of the data source class to the CLIPS code representing the sensors. This tells REASON where it can retrieve information about the given sensor, including new measurements. The “Update” portion of the workflow retrieves the newest observations for the sensors through SOS queries. The resulting XML documents are converted on-the-fly into CLIPS code which is used to update the GIS layers and CLIPS code associated with the sensors. Evaluation is then carried out on the new values as defined in the application ontology (see Section 4 for a sample decision tree that is used to analyze sensor data). When evaluation is completed, new values are acquired from the data source and the process repeats itself until the system is told to release the resources associated with the data source and terminate.

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.