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.

Abstract. Schema matching, the problem of finding mappings between the attributes of two semantically related database schemas, is an important aspect of many database applications such as schema integration, data warehousing, and electronic commerce. Unfortunately, schema matching remains largely a manual, labor-intensive process. Furthermore, the effort required is typically linear in the number of schemas to be matched; the next pair of schemas to match is not any easier than the previous pair. In this paper we describe a system, called Automatch, that uses machine learning techniques to automate schema matching. Based primarily on Bayesian learning, the system acquires probabilistic knowledge from examples that have been provided by domain experts. This knowledge is stored in a knowledge base called the attribute dictionary. When presented with a pair of new schemas that need to be matched (and their corresponding database instances), Automatch uses the attribute dictionary to find an optimal matching. We also report initial results from the Automatch project.

Schema matching, the problem of finding mappings between the attributes of two semantically related database schemas, is an important aspect of many database applications such as schema integration, data warehousing, and electronic commerce. Unfortunately, schema matching remains largely a manual, labor-intensive process. Furthermore, the effort required is typically linear in the number of schemas to be matched; the next pair of schemas to match is not any easier than the previous pair. In this paper we describe a system, called Automatch, that uses machine learning techniques to automate schema matching. Based primarily on Bayesian learning, the system acquires probabilistic knowledge from examples that have been provided by domain experts. This knowledge is stored in a knowledge base called the attribute dictionary. When presented with a pair of new schemas that need to be matched (and their corresponding database instances), Automatch uses the attribute dictionary to find an optimal matching. We also report initial results from the Automatch project.

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