Bisimulations on Data Graphs

Abstract : Bisimulation provides structural conditions to characterize indistinguishability from an external observer between nodes on labeled graphs. It is a fundamental notion used in many areas, such as verification, graph-structured databases, and constraint satisfaction. However, several current applications use graphs where nodes also contain data (the so called " data graphs "), and where observers can test for equality or inequality of data values (e.g., asking the attribute 'name' of a node to be different from that of all its neighbors). The present work constitutes a first investigation of " data aware " bisimulations on data graphs. We study the problem of computing such bisimulations, based on the observational indistinguishability for XPath —a language that extends modal logics like PDL with tests for data equality— with and without transitive closure operators. We show that in general the problem is PSPACE-complete, but identify several restrictions that yield better complexity bounds (CO-NP, PTIME) by controlling suitable parameters of the problem, namely the amount of non-locality allowed, and the class of models considered (graphs, DAGs, trees). In particular, this analysis yields a hierarchy of tractable fragments.
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Journal of Artificial Intelligence Research (JAIR), 2018, 61, pp.171-213. 〈10.1613/jair.5637〉
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Soumis le : mercredi 10 janvier 2018 - 11:58:28
Dernière modification le : mercredi 1 août 2018 - 15:02:03

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Sergio Abriola, Pablo Barceló, Diego Figueira, Santiago Figueira. Bisimulations on Data Graphs. Journal of Artificial Intelligence Research (JAIR), 2018, 61, pp.171-213. 〈10.1613/jair.5637〉. 〈hal-01679889〉

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