Approximate string-matching with q-grams and maximal matches
Theoretical Computer Science - Selected papers of the Combinatorial Pattern Matching School
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Machine Learning
PostgreSQL: introduction and concepts
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A fast algorithm for computing longest common subsequences
Communications of the ACM
A linear space algorithm for computing maximal common subsequences
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A vector space model for automatic indexing
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Data-driven understanding and refinement of schema mappings
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SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
MFCS '94 Proceedings of the 19th International Symposium on Mathematical Foundations of Computer Science 1994
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The VLDB Journal — The International Journal on Very Large Data Bases
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Proceedings of the 2003 ACM SIGMOD international conference on Management of data
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SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Proceedings of the 2004 international workshop on Information quality in information systems
The Data Mapping Problem: Algorithmic and Logical Characterizations
ICDEW '05 Proceedings of the 21st International Conference on Data Engineering Workshops
Top-k generation of mediated schemas over multiple data sources
DASFAA'10 Proceedings of the 15th international conference on Database systems for advanced applications
Enhanced geographically typed semantic schema matching
Web Semantics: Science, Services and Agents on the World Wide Web
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DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications: Part II
Learning semantic string transformations from examples
Proceedings of the VLDB Endowment
Appearance-Order-Based schema matching
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part I
An evolutionary approach to complex schema matching
Information Systems
Discovering linkage points over web data
Proceedings of the VLDB Endowment
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We describe a method for discovering complex schema translations involving substrings from multiple database columns. The method does not require a training set of instances linked across databases and it is capable of dealing with both fixed-and variable-length field columns. We propose an iterative algorithm that deduces the correct sequence of concatenations of column substrings in order to translate from one database to another. We introduce the algorithm along with examples on common database data values and examine its performance on real-world and synthetic datasets.