Autoplex: Automated Discovery of Content for Virtual Databases

  • Jacob Berlin
  • Amihai Motro
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2172)


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.


Global Scheme Projective Transformation Automate Discovery Maximum Weighted Match Probabilistic Knowledge 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Jacob Berlin
    • 1
  • Amihai Motro
    • 1
  1. 1.Information and Software Engineering DepartmentGeorge Mason UniversityFairfax

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