Efficiently Characterizing Non-Redundant Constraints in Large Real World Qualitative Spatial Networks

RCC8 is a constraint language that serves for qualitative spatial representation and reasoning by encoding the topological relations between spatial entities. We focus on efficiently characterizing nonredundant constraints in large real world RCC8 networks and obtaining their prime networks. For a RCC8 network N a constraint is redundant, if removing that constraint from N does not change the solution set of N . A prime network of N is a network which contains no redundant constraints, but has the same solution set as N . We make use of a particular partial consistency, namely, G-consistency, and obtain new complexity results for various cases of RCC8 networks, while we also show that given a maximal distributive subclass for RCC8 and a network N defined on that subclass, the prunning capacity of G-consistency and -consistency is identical on the common edges of G and the complete graph of N , when G is a triangulation of the constraint graph of N . Finally, we devise an algorithm based on G-consistency to compute the unique prime network of a RCC8 network, and show that it significantly progresses the stateof-the-art for practical reasoning with real RCC8 networks scaling up to millions of nodes.