Modelling autonomic dataspaces using answer sets

Abstract : This paper presents an approach for managing an autonomic data space, able to automatically define views that fulfill the requirements of a set of users, and adjust them as the data space evolves. An autonomic data space deals with incomplete knowledge to manage itself because of the heterogeneity and the lack of metadata related to the resources it integrates. Our approach exploits the expressiveness of stable models and the K action language for expressing the data space management functions. It is based on a model for specifying an autonomic dataspace expressed using answer set programming (ASP). ASP is a type of declarative logic programming particularly useful in knowledge-intensive applications. It is based on the stable semantics (answer sets), which allows negation as failure and applies the ideas of auto-epistemic logic to distinguish between what is true and what is believed.
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Article dans une revue
Journal de Inteligencia Artificial, Erevista, 2010, 14(48), pp.3--14
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https://hal.inria.fr/hal-00953109
Contributeur : Fabrice Jouanot <>
Soumis le : vendredi 28 février 2014 - 12:16:14
Dernière modification le : jeudi 11 janvier 2018 - 06:22:06

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  • HAL Id : hal-00953109, version 1

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Gabriela Montiel Moreno, José-Luis Zechinelli-Martini, Genoveva Vargas-Solar. Modelling autonomic dataspaces using answer sets. Journal de Inteligencia Artificial, Erevista, 2010, 14(48), pp.3--14. 〈hal-00953109〉

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