Abstract
In grid computing, we believe that keywords and ontologies can not always be defined or interpreted precisely enough to achieve semantic agreement in a truly distributed, heterogeneous computing environment. In this paper, we present the functional validation concept in grid computing, analyze the possible validation situations and apply basic machine learning theory such as PAC learning and Chernoff bounds to explore the relationship between sample size and confidence in service semantics.
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