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.