Empirical evidence for the usefulness of Armstrong relations in the acquisition of meaningful functional dependencies

Langeveldt, Warren-Dean; Link, Sebastian

doi:10.1016/j.is.2009.11.002

Cited by 47 publications

(25 citation statements)

References 69 publications

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“…Intuitively, design teams find it more difficult to understand the interaction of FDs and MVDs in the presence of an arbitrary NFS. Hence, Armstrong databases [Fagin 1982a] may be of even greater value than for the special case of total relations [Langeveldt and Link 2010]. It is therefore desirable to extend the results about Armstrong relations from the class of FDs in the presence of an NFS [Hartmann et al 2012] to the combined class of FDs and MVDs in the presence of an NFS.…”

Section: Future Directionsmentioning

confidence: 99%

The implication problem of data dependencies over SQL table definitions

Hartmann

Link

2012

ACM Trans. Database Syst.

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We investigate the implication problem for classes of data dependencies over SQL table definitions. Under Zaniolo's "no information" interpretation of null markers we establish an axiomatization and algorithms to decide the implication problem for the combined class of functional and multivalued dependencies in the presence of NOT NULL constraints. The resulting theory subsumes three previously orthogonal frameworks. We further show that the implication problem of this class is equivalent to that in a propositional fragment of Schaerf and Cadoli's [1995] family of para-consistent S-3 logics. In particular, S is the set of variables that correspond to attributes declared NOT NULL. We also show how our equivalences for multivalued dependencies can be extended to Delobel's class of full first-order hierarchical decompositions, and the equivalences for functional dependencies can be extended to arbitrary Boolean dependencies. These dualities allow us to transfer several findings from the propositional fragments to the corresponding classes of data dependencies, and vice versa. We show that our results also apply to Codd's null interpretation "value unknown at present", but not to Imielinski's [1989] or-relations utilizing Levene and Loizou's weak possible world semantics [Levene and Loizou 1998]. Our findings establish NOT NULL constraints as an effective mechanism to balance not only the certainty in database relations but also the expressiveness with the efficiency of entailment relations. They also control the degree by which the implication of data dependencies over total relations is soundly approximated in SQL table definitions. ACM Reference Format:Hartmann, S., and Link, S. 2012. The implication problem of data dependencies over SQL table definitions: Axiomatic, algorithmic and logical characterizations.

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Section: Future Directionsmentioning

confidence: 99%

The implication problem of data dependencies over SQL table definitions

Hartmann

Link

2012

ACM Trans. Database Syst.

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“…It would also be interesting to investigate the properties of Armstrong databases for data dependencies under bag semantics. These databases are useful for the acquisition of meaningful integrity constraints [17].…”

Section: Discussionmentioning

confidence: 99%

Armstrong axioms and Boyce–Codd–Heath Normal Form under bag semantics

Köehler

Link

2010

Information Processing Letters

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“…The team then jointly inspects the sample with domain experts in order to discover flaws or shortcomings in the perception of the design team. Evidently, the Armstrong sample helps designers and domain experts communicate more effectively, thereby overcoming the mismatch in expertise [30]. This process repeats until all parties are happy.…”

Section: Armstrong Samples For Qualitative Cardinality Constraintsmentioning

confidence: 99%

“…They are widely regarded as an effective tool to visualize abstract sets of constraints in a user-friendly way [13,33,34]. As such data engineers exploit Armstrong databases as a communication tool in their interaction with domain experts in order to determine the set of constraints that are meaningful to the application at hand [22,30,33,34]. As Fig.…”

Section: Armstrong Samples For Qualitative Cardinality Constraintsmentioning

confidence: 99%

Cardinality constraints on qualitatively uncertain data

Hall

Köehler

Link

et al. 2015

Data & Knowledge Engineering

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Modern applications require advanced techniques and tools to process large volumes of uncertain data. For that purpose we introduce cardinality constraints as a principled tool to control the occurrences of uncertain data. Uncertainty is modeled qualitatively by assigning to each object a degree of possibility by which the object occurs in an uncertain instance. Cardinality constraints are assigned a degree of certainty that stipulates on which objects they hold. Our framework empowers users to model uncertainty in an intuitive way, without the requirement to put a precise value on it. Our class of cardinality constraints enjoys a natural possible world semantics, which is exploited to establish several tools to reason about them. We characterize the associated implication problem axiomatically and algorithmically in linear input time. Furthermore, we show how to visualize any given set of our cardinality constraints in the form of an Armstrong sketch. Even though the problem of finding an Armstrong sketch is precisely exponential, our algorithm computes a sketch with conservative use of time and space. Data engineers may therefore compute Armstrong sketches that they can jointly inspect with domain experts in order to consolidate the set of cardinality constraints meaningful for a given application domain.

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Empirical evidence for the usefulness of Armstrong relations in the acquisition of meaningful functional dependencies

Cited by 47 publications

References 69 publications

The implication problem of data dependencies over SQL table definitions

The implication problem of data dependencies over SQL table definitions

Armstrong axioms and Boyce–Codd–Heath Normal Form under bag semantics

Cardinality constraints on qualitatively uncertain data

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