In this paper, we carefully explore the assumptions behind using information capacity equivalence as a measure of correctness for judging transformed schemas in schema integration and translation methodologies. We present a classification of common integration and translation tasks based on their operational goals and derive from them the relative information capacity requirements of the original and transformed schemas. We show that for many tasks, information capacity equivalence of the schemas is not strictly required. Based on this, we present a new definition of correctness that reflects each undertaken task. We then examine existing methodologies and show how anomalies can arise when using those that do not meet the proposed correctness criteria.
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