A Logic-based Model for uncertainty reduction: parallel processing

Geographic Information Systems (GIS) often use incomplete and uncertain information leading to inconsistency. Thus, a clear definition of information revision is required. In logic, most of the usual belief revision operations are characterized by a high computational complexity, which implies actual intractability even for rather small data sets. On the other hand, GIS deal with large amounts of data, what means that revision is practically impossible. This challenge can be addressed only if a local belief revision strategy can be defined. In this paper, a new model for spatial information representation is defined, 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 principle of minimal change, as in any revision operation, by assuming the hypothesis that the size (spatial extent) of minimal inconsistencies is spatially bounded.