Abstract
Heterogeneous information sources are organized in various different degrees from well-structured data, to unstructured and semi-structured data. Such information sources do not have rigid schema available in advance or even if each source has its own schema, there are no enforced modeling constraints or formats for data across information sources. In this paper, we propose a novel method for abstracting schemas for heterogeneous information sources. At the most detailed level, information sources are represented in a labeled directed graph. We develop several abstraction operations for label generalization and aggregation. One of more of these operations can be applied to a labeled directed graph to “levelize” schemas. Each such level of the schemas is a potentially useful paradigm for query formation and optimization.
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Yoon, J.P., Raghavan, V. (2000). Multi-level Schema Extraction for Heterogeneous Semi-structured Data. In: Lu, H., Zhou, A. (eds) Web-Age Information Management. WAIM 2000. Lecture Notes in Computer Science, vol 1846. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45151-X_39
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DOI: https://doi.org/10.1007/3-540-45151-X_39
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