document: A theoretical framework for learning from a pool of disparate data sources Many enterprises incorporate information gathered from a variety of data sources into an integrated input for some learning task . For example , aiming towards the design of an automated diagnostic tool for some disease , one may wish to integrate data gathered in many different hospitals . A major obstacle to such endeavors is that different data sources may vary considerably in the way they choose to represent related data . In practice , the problem is usually solved by a manual construction of semantic mappings and translations between the different sources . Recently there have been attempts to introduce automated algorithms based on machine learning tools for the construction of such translations . In this work we propose a theoretical framework for making classification predictions from a collection of different data sources , without creating explicit translations between them . Our framework allows a precise mathematical analysis of the complexity of such tasks , and it provides a tool for the development and comparison of different learning algorithms . Our main objective , at this stage , is to demonstrate the usefulness of computational learning theory to this practically important area and to stimulate further theoretical and experimental research of questions related to this framework .
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