Database sampling with functional dependencies

Bisbal, Jesús; Grimson, Jane

doi:10.1016/s0950-5849(01)00165-3

Cited by 6 publications

(18 citation statements)

References 16 publications

(25 reference statements)

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“…The formal semantics for Consistent Database Sampling presented here complements the informal developments of [9,5,6]. Briefly, these papers provided algorithmic descriptions of how to enforce the chain of insertions that take place when sampling a database according to, for example, a set of integrity constraints.…”

Section: Semantics Of Consistent Database Samplingmentioning

confidence: 99%

“…The same applies to the construction of a Sample Database. This section formalises the solutions that were adopted in [9,5,6]. It is believed this description will be very useful in the context of understanding and reasoning about database sampling.…”

Section: Semantics Of Consistent Database Samplingmentioning

confidence: 99%

“…Several criteria can be used to perform this initial insertion, e.g. randomly, or such that it leads to a small sample (refer to [9,6] for concrete examples). Another parameter that determines the concrete final Sample Database that results from this process is concerned with choosing between tuples when several are possible (which, once again, may refer to the size of the resulting database).…”

Section: This Definition Considers All Sets Of Tuples Not Yet In the mentioning

confidence: 99%

“…Examples include data mining [16] and approximate query evaluation [19]. Recently, database sampling has also been proposed [9,6] as a mechanism to populate a Prototype Database, that is, any database used to support the development of data-intensive applications (e.g. requirements elicitation, software testing, experimentation with design alternatives).…”

Section: Introductionmentioning

confidence: 99%

“…inclusion dependencies, cardinality constraints, subset dependencies) was presented in [9]. The particular case of sampling with functional dependencies was analysed in [6]. Database sampling was described in [5] abstracting the details of particular integrity constraint types.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A formal framework for database sampling

Bisbal

Grimson

Bell

2005

Information and Software Technology

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Section: Semantics Of Consistent Database Samplingmentioning

confidence: 99%

Section: Semantics Of Consistent Database Samplingmentioning

confidence: 99%

Section: This Definition Considers All Sets Of Tuples Not Yet In the mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A formal framework for database sampling

Bisbal

Grimson

Bell

2005

Information and Software Technology

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Consistent database sampling as a database prototyping approach

Bisbal

Grimson

2002

J. Softw. Maint. Evol.: Res. Pract.

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Requirements elicitation has been reported to be the stage of software development when errors have the most expensive consequences. Users usually find it difficult to articulate a consistent and complete set of requirements at the beginning of a development project. Prototyping is considered a powerful technique to ease this problem by exposing a partial implementation of the software system to the user, who can then identify required modifications. When prototyping data-intensive applications a so-called prototype database is needed. This paper investigates how a prototype database can be built. Two different approaches are analysed, namely test databases and sample databases; the former populates the resulting database with synthetic values, while the latter uses data values from an existing database. The application areas that require prototype databases, in addition to requirements analysis, are also identified. The paper reports on existing research into the construction of both types of prototype databases, and indicates to which type of application area each is best suited. This paper advocates for the use of sample databases when an operational database is available, as is commonly the case in software maintenance and evolution. Domainrelevant data values and integrity constraints will produce a prototype database which will support the information system development process better than synthetic data. The process of extracting a sample database is also investigated. ‡ In general, a prototype is a model of the software to be built which exhibits the desired properties of the final product [1]. Numerous techniques have been developed to create such models (e.g. simulations, reusable components, high-level languages, breadboards), not all of which result in an implementation of the system.

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Design by example for SQL table definitions with functional dependencies

Hartmann

Kirchberg²,

Link

2011

The VLDB Journal

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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 1 data. Such support can lead to designs that guarantee efficient data management in practice.

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Database sampling with functional dependencies

Cited by 6 publications

References 16 publications

A formal framework for database sampling

A formal framework for database sampling

Consistent database sampling as a database prototyping approach

Design by example for SQL table definitions with functional dependencies

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