An MDA approach for the development of data warehouses

RESUMENUn data warehouse (DW) es un repositorio de datos que provienen de distintas fuentes. Es un sistema para el análisis de datos usado para el apoyo a la toma de decisiones. En el ámbito de análisis de datos es usual encontrar relaciones entre los datos de naturaleza difusa. Por ejemplo, en un contexto académico la respuesta a la consulta "qué alumnos obtuvieron buena nota final" no puede ser obtenida desde un DW tradicional pues no maneja información cualitativa. Debido a las limitaciones de los DW tradicionales es que se pueden extender usando lógica difusa a un DW Difusos (DWD). Por otro lado, en el ciclo de desarrollo de DW se han considerado diversas metodologías y modelos que tienen como desventaja que no integran los distintos niveles de abstracción ni el proceso de diseño de DW. Sin embargo, el enfoque MDA es una alternativa de integración que otorga un marco de desarrollo integrado basado en la arquitectura de modelos y transformaciones entre distintos niveles de abstracción, que la hace adecuada para este tema. En este artículo se presenta un enfoque MDA para el diseño de un DWD que considera medidas difusas, relaciones difusas y niveles difusos, para lo cual se proponen un metamodelo CWM OLAP Difuso, reglas QVT y un ejemplo. También se presenta la secuencia metodológica para la aplicación de las reglas QVT. Finalmente, se proporciona un caso de estudio de análisis en el ámbito educacional.Palabras clave: Data warehouse, data warehouse difuso, arquitectura dirigida por modelos, lógica difusa. ABSTRACT A Data Warehouse (DW) is a repository of data from different sources. Is a system for data analysis used to support decision making. In the area of data analysis is common to find relationships between data from fuzzy nature. In this context, the questions give me the marks quantity of good, average and bad cannot be obtained from the traditional DW because do not handle as qualitative information. Due to the limitations of traditional DW is that it can be extended to

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“…También se ha aplicado el enfoque MDA a DW que puede ser revisado en [17]. Operaciones de DW se pueden revisar en [18].…”

Section: Introductionunclassified

Enfoque MDA para el diseño de un data warehouse difuso

Zambrano

Contreras²,

Urrutia³

2012

Ingeniare. Rev. chil. ing.

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“…Afterwards, the data warehouse is built in order to support queries from the presented approach. Therefore, we can ensure that the query posed on the DW node will return the correct data required by the decision maker (Mazón & Trujillo, 2008;Mazón, Trujillo, & Lechtenbörger, 2007).…”

Section: Accepted M Manuscriptmentioning

confidence: 99%

Enrichment of the phenotypic and genotypic Data Warehouse analysis using Question Answering systems to facilitate the decision making process in cereal breeding programs

Peral

Ferrández

Gregorio

et al. 2015

Ecological Informatics

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“…These constraints have formal underlying formulae and contain the allowed operators, if any, along the dimension that the measure is not additive. Finally, in [25,26], this approach is integrated in a model driven framework in order to obtain a logical representation of the conceptual multidimensional model in an automatic way.…”

Section: General Approaches For Multidimensional Modelingmentioning

confidence: 99%

A survey on summarizability issues in multidimensional modeling

Mazón

Lechtenbörger

Trujillo

2009

Data & Knowledge Engineering

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The development of a data warehouse (DW) system is based on a conceptual multidimensional model, which provides a high level of abstraction in accurately and expressively describing real-world situations. Once this model is designed, the corresponding logical representation must be obtained as the basis of the implementation of the DW according to one specific technology. However, even though a good conceptual multidimensional model is designed underneath a DW, there is a semantic gap between this model and its logical representation. In particular, this gap complicates an adequate treatment of summarizability issues, which in turn may lead to erroneous results of data analysis tools. Research addressing this topic has produced only partial solutions, and individual terminology used by different parties hinders further progress. Consequently, based on a unifying vocabulary, this survey sheds light on (i) the weak and strong points of current approaches for modeling complex multidimensional structures that reflect real-world situations in a conceptual multidimensional model and (ii) existing mechanisms to avoid summarizability problems when conceptual multidimensional models are being implemented.

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An MDA approach for the development of data warehouses

Cited by 134 publications

References 12 publications

Enfoque MDA para el diseño de un data warehouse difuso

Enfoque MDA para el diseño de un data warehouse difuso

Enrichment of the phenotypic and genotypic Data Warehouse analysis using Question Answering systems to facilitate the decision making process in cereal breeding programs

A survey on summarizability issues in multidimensional modeling

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