Abstract
Recently, the problem of data integration has been newly addressed by methods based on machine learning and discovery. Such methods are intended to automate, at least in part, the laborious process of information integration, by which existing data sources are incorporated in a virtual database. Essentially, these methods scan new data sources, attempting to discover possible mappings to the virtual database. Like all discovery processes, this process is intrinsically probabilistic; that is, each discovery is associated with a specific value that denotes assurance of its appropriateness. Consequently, the rows in a discovered virtual table have mixed assurance levels, with some rows being more credible than others. We argue that rows in discovered virtual databases should be ranked, and we describe a ranking method, called TupleRank, for calculating such a ranking order. Roughly speaking, TupleRank calibrates the probabilities calculated during a discovery process with historical information about the performance of the system. The work is done in the framework of the Autoplex system for discovering content for virtual databases, and initial experimentation is reported and discussed.
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Berlin, J., Motro, A. (2006). TupleRank: Ranking Discovered Content in Virtual Databases. In: Etzion, O., Kuflik, T., Motro, A. (eds) Next Generation Information Technologies and Systems. NGITS 2006. Lecture Notes in Computer Science, vol 4032. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11780991_2
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DOI: https://doi.org/10.1007/11780991_2
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