TY - JOUR
TI - An efficient approach for tackling large real world qualitative spatial networks
AU - Sioutis, M.
AU - Condotta, J.-F.
AU - Koubarakis, M.
JO - INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS
PY - 2016
VL - 25
TODO - 2
SP - null
PB - World Scientific Publishing Co. Pte. Ltd.
SN - 0218-2130
TODO - 10.1142/S0218213015500311
TODO - Adjacency lists;  Constraint graph;  Experimental evaluation;  Implementation scheme;  Preferential attachments;  Spatial datasets;  State-of-the-art approach;  State-of-the-art methods, Artificial intelligence
TODO - We improve the state-of-the-art method for checking the consistency of large qualitative spatial networks that appear in the Web of Data by exploiting the scale-free-like structure observed in their constraint graphs. We propose an implementation scheme that triangulates the constraint graphs of the input networks and uses a hash table based adjacency list to efficiently represent and reason with them. We generate random scale-free-like qualitative spatial networks using the Barabási-Albert (BA) model with a preferential attachment mechanism. We test our approach on the already existing random datasets that have been extensively used in the literature for evaluating the performance of qualitative spatial reasoners, our own generated random scale-free-like spatial networks, and real spatial datasets that have been made available as Linked Data. The analysis and experimental evaluation of our method presents significant improvements over the state-of-the-art approach, and establishes our implementation as the only possible solution to date to reason with large scale-free-like qualitative spatial networks efficiently. © 2016 World Scientific Publishing Company.
ER -