Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Evaluating queries with generalized path expressions
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Enhanced hypertext categorization using hyperlinks
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Scalable collection summarization and selection
Proceedings of the fourth ACM conference on Digital libraries
Presenting XML
Discrete Mathematical Structures with Applications to Computer Science
Discrete Mathematical Structures with Applications to Computer Science
Optimizing Regular Path Expressions Using Graph Schemas
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
DataGuides: Enabling Query Formulation and Optimization in Semistructured Databases
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Using Probabilistic Information in Data Integration
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
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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.