Presenter/Author Information

Omar Doukari
Robert Jeansoulin

Keywords

spatial information, uncertainty, belief revision, local processing

Start Date

1-7-2012 12:00 AM

Description

Geographic Information Systems (GIS) often use incomplete and uncertain informationleading to inconsistency. Thus, a clear definition of information revision is required. Inlogic, most of the usual belief revision operations are characterized by a high computationalcomplexity, which implies actual intractability even for rather small data sets. Onthe other hand, GIS deal with large amounts of data, what means that revision is practicallyimpossible. This challenge can be addressed only if a local belief revision strategycan be defined. In this paper, a new model for spatial information representation isdefined, called G-structure model, and a local belief revision is designed, in order to answer,piece-by-piece, the problem of large data. This model complies with the principleof minimal change, as in any revision operation, by assuming the hypothesis that the size(spatial extent) of minimal inconsistencies is spatially bounded.

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Jul 1st, 12:00 AM

A Logic-based Model for uncertainty reduction: parallel processing

Geographic Information Systems (GIS) often use incomplete and uncertain informationleading to inconsistency. Thus, a clear definition of information revision is required. Inlogic, most of the usual belief revision operations are characterized by a high computationalcomplexity, which implies actual intractability even for rather small data sets. Onthe other hand, GIS deal with large amounts of data, what means that revision is practicallyimpossible. This challenge can be addressed only if a local belief revision strategycan be defined. In this paper, a new model for spatial information representation isdefined, called G-structure model, and a local belief revision is designed, in order to answer,piece-by-piece, the problem of large data. This model complies with the principleof minimal change, as in any revision operation, by assuming the hypothesis that the size(spatial extent) of minimal inconsistencies is spatially bounded.