Functional dependencies in relational databases: A lattice point of view

Demetrovics, János; Libkin, Leonid; Muchnik, Ilya

doi:10.1016/0166-218x(92)90028-9

Cited by 60 publications

(33 citation statements)

References 22 publications

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“…The set of all Moore families on a set Q is closely related to K n . Bernard Monjardet pointed out to me that results stronger than semimodularity are known for this set, e.g., Demetrovics, Libkin, and Muchnik (1992). Apparently, the most far-reaching result was recently obtained by Nathalie Caspard, drawing on the concept of dependence relations, cf.…”

Section: Discussion and Open Problemsmentioning

confidence: 93%

A Dimension-Related Metric on the Lattice of Knowledge Spaces

Suck

1999

Journal of Mathematical Psychology

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Section: Discussion and Open Problemsmentioning

confidence: 93%

A Dimension-Related Metric on the Lattice of Knowledge Spaces

Suck

1999

Journal of Mathematical Psychology

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“…The formal concept analysis approach discovers functional dependencies from the view of formal concept analysis. By considering the relationship between relational database theory and formal concept analysis (Demetrovics et al 1992), the functional dependencies that hold in a database can be extracted by using a predefined formal concept analysis closure operator. In Lopes et al (2002), the authors conclude that the qualitative comparison between DepMiner (or FastFDs) and TANE (or FUN) is difficult because the approaches widely differ.…”

Section: Related Workmentioning

confidence: 99%

Mining functional dependencies from data

Yao

Hamilton

2007

Data Min Knowl Disc

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In this paper, we propose an efficient rule discovery algorithm, called FD_Mine, for mining functional dependencies from data. By exploiting Armstrong's Axioms for functional dependencies, we identify equivalences among attributes, which can be used to reduce both the size of the dataset and the number of functional dependencies to be checked. We first describe four effective pruning rules that reduce the size of the search space. In particular, the number of functional dependencies to be checked is reduced by skipping the search for FDs that are logically implied by already discovered FDs. Then, we present the FD_Mine algorithm, which incorporates the four pruning rules into the mining process. We prove the correctness of FD_Mine, that is, we show that the pruning does not lead to the loss of useful information. We report the results of a series of experiments. These experiments show that the proposed algorithm is effective on 15 UCI datasets and synthetic data.

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“…The perhaps less known version but very important for its applications is the notion of (full) implicational system. As examples of implicational systems we can mention functional dependencies in relational databases (Maier 1983;Demetrovics et al 1992) or attribute implications in data mining. The standard example of attribute implications is usually taken from transactional databases that can be represented as a binary context (E, A, R) where E is a set of market baskets, A a set of attributes (items) and R the binary relation linking a market basket to the attributes it contains.…”

Section: Introductionmentioning

confidence: 99%