Discovery of multivalued dependencies from relations

“…It would be interesting to develop algorithms that determine all FDs and MVDs on a nested attribute N that are satisfied by a particular instance r domðNÞ. For relational databases, [62,70,72,73] and [88,108] have developed algorithms for FDs and MVDs, respectively.…”

Section: Related and Future Workmentioning

confidence: 99%

Functional and multivalued dependencies in nested databases generated by record and list constructor

Hartmann

Link

Schewe

2006

Ann Math Artif Intell

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The impact of the list constructor on two important classes of relational dependencies is investigated. Lists represent an inevitable data structure whenever order matters and data is allowed to occur repeatedly. The list constructor is therefore supported by many advanced data models such as genomic sequence, deductive and object-oriented data models including XML. The article proposes finite axiomatisations of functional, multivalued and both functional and multivalued dependencies in nested databases supporting record and list constructor. In order to capture different data models at a time, an abstract algebraic approach based on nested attributes is taken. The presence of the list constructor calls for a new inference rule which allows to infer non-trivial functional dependencies from multivalued dependencies. Further differences to the relational theory become apparent when the independence of the inference rules is investigated. The extension of the relational theory to nested databases allows to specify more real-world constraints and increases therefore the number of application domains.

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“…Such approaches include the discovery of functional [24,15,28] and multi-valued [25] dependencies. Our work complements this work by providing a means of characterizing the redundancy captured by a dependency.…”

Section: Related Workmentioning

confidence: 99%

“…To understand the relationship, we turn to work on mining for constraints (dependencies). There have been several approaches towards discovery of functional [24,15,28] and multivalued [25] dependencies. However, none of the approaches presents a characeterizaton of the resulting dependencies.…”

Section: Ranking Dependenciesmentioning

confidence: 99%

Information-theoretic tools for mining database structure from large data sets

Andritsos

Miller

Tsaparas

2004

Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data

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Data design has been characterized as a process of arriving at a design that maximizes the information content of each piece of data (or equivalently, one that minimizes redundancy). Information content (or redundancy) is measured with respect to a prescribed model for the data, a model that is often expressed as a set of constraints. In this work, we consider the problem of doing data redesign in an environment where the prescribed model is unknown or incomplete. Specifically, we consider the problem of finding structural clues in an instance of data, an instance which may contain errors, missing values, and duplicate records. We propose a set of information-theoretic tools for finding structural summaries that are useful in characterizing the information content of the data, and ultimately useful in data design. We provide algorithms for creating these summaries over large, categorical data sets. We study the use of these summaries in one specific physical design task, that of ranking functional dependencies based on their data redundancy. We show how our ranking can be used by a physical data-design tool to find good vertical decompositions of a relation (decompositions that improve the information content of the design). We present an evaluation of the approach on real data sets.

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Discovery of multivalued dependencies from relations

Cited by 32 publications

References 6 publications

Degrees of conditional (in)dependence: A framework for approximate Bayesian networks and examples related to the rough set-based feature selection

Degrees of conditional (in)dependence: A framework for approximate Bayesian networks and examples related to the rough set-based feature selection

Functional and multivalued dependencies in nested databases generated by record and list constructor

Information-theoretic tools for mining database structure from large data sets

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