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Autoplex: Automated Discovery of Content for Virtual Databases


Jacob Berlin and Amihai Motro

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Abstract

Most virtual database systems are suitable for environments in which the set of member information sources is small and stable. Consequently, present virtual database systems do not scale up very well. The main reason is the complexity and cost of incorporating new information sources into the virtual database. In this paper we describe a system, called Autoplex, which uses machine learning techniques for automating the discovery of new content for virtual database systems. Autoplex assumes that several information sources have already been incorporated (``mapped'') into the virtual database system by human experts (as done in standard virtual database systems). Autoplex learns the features of these examples. It then applies this knowledge to new candidate sources, trying to infer views that ``resemble'' the examples. In this paper we report initial results from the Autoplex project.


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