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Semistructured data has no a-priori schema information, formation which causes some problems such as inefficient storage and query execution. To cope with such problems, extracting schema information from semistructured data has been all important issue. However, in most cases optimal schema information cannot be extracted efficiently, and few efficient approximation algorithms have been proposed. In this paper, we consider an approximation algorithm fur extracting "typical" classes from semistructured data. Intuitively, a class C is said to be typical if the structure of C is "similar" to those of "many" objects. We present the following results. First, we prove that the problem of deciding if a typical class can be extracted from given semistructured data is NP-complete. Second, we present an approximation algorithm for extracting typical classes from given semistructured data, and show a sufficient condition for the approximation algorithm tu run in polynomial time. Finally, by using extracted classes obtained by the approximation algorithm, we propose a polynomial-time algorithm for constructing a set R of classes such that R covers all the objects to form a database schema.
Research papers (academic journals)
