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Determining View dependencies using tableaux
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Computational problems related to the design of normal form relational schemas
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Extending the database relational model to capture more meaning
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Journal of the ACM (JACM)
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Journal of the ACM (JACM)
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On the Structure of Armstrong Relations for Functional Dependencies
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A relational model of data for large shared data banks
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Synthesizing independent database schemas
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Exploiting Uniqueness in Query Optimization
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Dependency Satisfaction in Databases with Incomplete Information
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Information Systems
Automated elicitation of inclusion dependencies from the source code for database transactions
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A normal form for XML documents
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CORDS: automatic discovery of correlations and soft functional dependencies
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Strong functional dependencies and their application to normal forms in XML
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Theory of Relational Databases
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Acta Cybernetica
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A formal analysis of information disclosure in data exchange
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A consideration on normal form of not-necessarily-normalized relation in the relational data model
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A sophisticate's introduction to database normalization theory
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The VLDB Journal — The International Journal on Very Large Data Bases
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Letting keys and functional dependencies out of the bag
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Efficiency frontiers of XML cardinality constraints
Data & Knowledge Engineering
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A database is C-Armstrong for a given set of constraints in a class C if it satisfies every constraint of the set and violates every constraint in C not implied by the set. Therefore, Armstrong databases are test data that perfectly illustrate the current perceptions about the semantics of a schema. We extend the existing theory of Armstrong relations to a toolbox of Armstrong tables. That is, we investigate structural and computational properties of Armstrong tables for the class of functional dependencies (FDs) over SQL tables. Relations are special instances of SQL tables with no duplicate rows and no null value occurrences. While FDs do not enjoy Armstrong tables, the combined class of standard FDs and NOT NULL constraints does enjoy Armstrong tables. The problem of finding an Armstrong table is shown to be precisely exponential for this combined class. However, we establish an algorithm that computes Armstrong tables with a size at most quadratic in that of a minimum-sized Armstrong table. Our resulting toolbox of Armstrong tables can be applied by data engineers to concisely visualize constraints on SQL data. Such support can lead to designs that guarantee efficient data management in practice.