Approximation Algorithms for Schema-Mapping Discovery from Data Examples

• Autores: Balder Ten Cate, Phokion G. Kolaitis, Kun Qian, Wang-Chiew Tan
• Localización: ACM transactions on database systems, ISSN 0362-5915, Vol. 42, Nº 2, 2017
• Idioma: inglés
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• Resumen

  • In recent years, data examples have been at the core of several different approaches to schema-mapping design. In particular, Gottlob and Senellart introduced a framework for schema-mapping discovery from a single data example, in which the derivation of a schema mapping is cast as an optimization problem. Our goal is to refine and study this framework in more depth. Among other results, we design a polynomial-time log(n)-approximation algorithm for computing optimal schema mappings from a given set of data examples (where n is the combined size of the given data examples) for a restricted class of schema mappings; moreover, we show that this approximation ratio cannot be improved. In addition to the complexity-theoretic results, we implemented the aforementioned log(n)-approximation algorithm and carried out an experimental evaluation in a real-world mapping scenario.