System Arch 5
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5. System Architecture The system architecture describes the functional components and their purpose to facilitate qualitative reasoning in analysis of spatial entities of a specific geographic region of interest owing to the formulation of GEASE knowledge base discussed in section 4. The system requires spatio-temporal datasets about a geographic region collected at regular temporal granules j (months) of (year) in series. The functions of each component are detailed in this section. The logical view of spatial entities and temporal attributes is explained in Fig.3. The systematic organization of GEASE system architecture is shown in Fig.4. Spatio-Temporal Dataset – Topographic data in time series acquired using remote sensing technology in vector format is the source of input to the system from data store. The spatio- temporal datasets whose physical geographic feature is to be analyzed is loaded as shape file to data store. Geographic Event Attributed Spatial Entity (GEASE) Knowledge base The facts about geographic regions are present in the data store. These facts are imported as spatial objects to database over which the formulated GEASE knowledge base have to be constructed. Each sub region r in is a polygon denoting a spatial entity. The temporal granules of are the temporal attributes of spatial entity describing an event E, which denotes a spatial change in terms of P to codify the topological relations as formulated in RCC [16]. Thus, Allen’s Interval logic relations are used to codify the temporal attributes of spatial entity that will retrieve the events occurring at an instant of or at start and end of interval T. Spatio-Temporal Inference Engine The user raise query to GEASE which is built over relational database to infer events acted upon the spatial entities of a geographic region .
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Transcript of System Arch 5
5. System Architecture
The system architecture describes the functional components and their purpose to facilitate qualitative reasoning in analysis of spatial entities of a specific geographic region of interest owing to the formulation of GEASE knowledge base discussed in section 4. The system requires spatio-temporal datasets about a geographic region collected at regular temporal granules j
(months) of (year) in series. The functions of each component are detailed in this section. The logical view of spatial entities and temporal attributes is explained in Fig.3. The systematic organization of GEASE system architecture is shown in Fig.4.
Spatio-Temporal Dataset – Topographic data in time series acquired using remote sensing technology in vector format is the source of input to the system from data store. The spatio-temporal datasets whose physical geographic feature is to be analyzed is loaded as shape file to data store.
Geographic Event Attributed Spatial Entity (GEASE) Knowledge base
The facts about geographic regions are present in the data store. These facts are imported as spatial objects to database over which the formulated GEASE knowledge base have to be constructed. Each sub region r in is a polygon denoting a spatial entity. The temporal granules of are the temporal attributes of spatial entity describing an event E, which denotes a spatial change in terms of P to codify the topological relations as formulated in RCC [16]. Thus, Allen’s Interval logic relations are used to codify the temporal attributes of spatial entity that will retrieve the events occurring at an instant of or at start and end of interval T.
Spatio-Temporal Inference Engine
The user raise query to GEASE which is built over relational database to infer events acted upon the spatial entities of a geographic region . Spatial and Temporal deductive inference mechanism evaluates the query by deducing the codified facts with inference rule that are present in GEASE knowledge base. The evaluation returns the result that is prompted at the user terminal through user interface.
Fig.3. Logical view of entity and attributes in STR knowledge base
Fig.4. GEASE System Architecture
