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Studying the use and effect of graph decomposition in qualitative spatial and temporal reasoning

Published online by Cambridge University Press:  12 October 2016

Michael Sioutis ,
Yakoub Salhi  and
Jean-François Condotta
Michael Sioutis
Affiliation:
Université Lille-Nord de France, Artois, CRIL-CNRS UMR 8188, Rue Jean Souvraz – SP 18, F 62307 Lens, France e-mail: [email protected], [email protected], [email protected]
Yakoub Salhi
Affiliation:
Université Lille-Nord de France, Artois, CRIL-CNRS UMR 8188, Rue Jean Souvraz – SP 18, F 62307 Lens, France e-mail: [email protected], [email protected], [email protected]
Jean-François Condotta
Affiliation:
Université Lille-Nord de France, Artois, CRIL-CNRS UMR 8188, Rue Jean Souvraz – SP 18, F 62307 Lens, France e-mail: [email protected], [email protected], [email protected]
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Abstract

We survey the use and effect of decomposition-based techniques in qualitative spatial and temporal constraint-based reasoning, and clarify the notions of a tree decomposition, a chordal graph, and a partitioning graph, and their implication with a particular constraint property that has been extensively used in the literature, namely, patchwork. As a consequence, we prove that a recently proposed decomposition-based approach that was presented in the study by Nikolaou and Koubarakis for checking the satisfiability of qualitative spatial constraint networks lacks soundness. Therefore, the approach becomes quite controversial as it does not seem to offer any technical advance at all, while results of an experimental evaluation of it in a following work presented in the study by Sioutis become questionable. Finally, we present a particular tree decomposition that is based on the biconnected components of the constraint graph of a given large network, and show that it allows for cost-free utilization of parallelism for a qualitative constraint language that has patchwork for satisfiable atomic networks.

Type
Articles
Information
The Knowledge Engineering Review , Volume 32 , 2017 , e4
Copyright
© Cambridge University Press, 2017 

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