A UML-based data warehouse design method

Prat, Nicolas; Akoka, Jacky; Comyn-Wattiau, Isabelle

doi:10.1016/j.dss.2005.12.001

Cited by 116 publications

(85 citation statements)

References 18 publications

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“…Afterwards, we consider the recent approaches [1,19,33], which exhibit more expressiveness based on an object-oriented perspective but which lack in a detailed proposal for ensuring summarizability.…”

Section: General Approaches For Multidimensional Modelingmentioning

confidence: 99%

“…It is instructive to note, however, that this approach does not allow generalizations as modeling constructs. Moreover, although several multidimensional design proposals are based on object-oriented modeling and, in particular, the UML [1,19,33] none of them explicitly suggests the use of generalization to avoid roll-up incompleteness. In line with [3] we argue that optional constructs such multiplicities of "0" should be avoided in understandable conceptual models whenever possible.…”

Section: Generalizationmentioning

confidence: 99%

“…Concerning semantically richer proposals, in [33], Prat et al first build a general UML (Unified Modeling Language) diagram which is enriched with multidimensional concepts that facilitate the derivation of a logical representation. Although, the authors describe in another paper [2] how to model different kinds of dimension hierarchies, the transformations of the hierarchies into a logical level are not as rich, since they are only defined from many-to-one and one-to-one associations of the conceptual model to fulfill summarizability conditions.…”

Section: General Approaches For Multidimensional Modelingmentioning

confidence: 99%

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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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Section: General Approaches For Multidimensional Modelingmentioning

confidence: 99%

Section: Generalizationmentioning

confidence: 99%

Section: General Approaches For Multidimensional Modelingmentioning

confidence: 99%

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A survey on summarizability issues in multidimensional modeling

Mazón

Lechtenbörger

Trujillo

2009

Data & Knowledge Engineering

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“…Para Codd, OLAP es un tipo de procesamiento de datos que se caracteriza, entre otras cosas, por permitir el análisis multidimensional [1]. Dependiendo de la base de datos subyacente, OLAP se subdivide dentro de dos grandes categorías: OLAP multidimensional (MOLAP) que son herramientas de almacenamiento de datos en un sistema de base de datos multidimensional propio y OLAP relacional (ROLAP), las cuales son herramientas que simulan un modelo multidimensional con una base de datos relacional basada en un esquema estrella o copo de nieve [16,6], siendo ROLAP la solución indicada para el desarrollo de esta solución (dada la infraestructura tecnológica existente dentro de la Universidad y la adopción de los procesos requeridos).…”

Section: Introductionunclassified

Incorporación De Elementos De Inteligencia De Negocios en El Proceso De Admisión Y Matrícula De Una Universidad Chilena

Tapia

Pinto

2010

Ingeniare. Rev. chil. ing.

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RESUMENEste artículo describe un proceso orientado a la incorporación de elementos de inteligencia de negocios (business intelligence-BI) en la Universidad de Tarapacá (UTA), Arica, Chile. Con este fin, se implementó un data mart (DM) centrado en el área de Admisión y Matrícula de la Vicerrectoría Académica. Su desarrollo requirió de la realización de actividades tales como la obtención de los requerimientos del negocio, la investigación del indicador clave de rendimiento (KPI) del área, el análisis de las distintas fuentes de información interna y el desarrollo de un modelado dimensional basado en el esquema estrella de Kimball. Para la correcta implementación e integración de este repositorio de datos se debió realizar un proceso de extracción, transformación y carga (ETL) a partir de dos fuentes de datos. La creación de este DM permitió que los usuarios de la Vicerrectoría Académica pudieran visualizar la información que requerían a través de herramientas de procesamiento analítico en línea (OLAP). Complementándose, además, con herramientas para la generación de reportes y herramientas para la creación de dashboards. La integración de estos elementos conformó una plataforma de inteligencia de negocios, que permite dar soporte a los requerimientos de información y análisis asociados al proceso de admisión y matrícula.Palabras clave: Inteligencia de negocios, data warehouse, data mart, KPI, ETL, OLAP, dashboard, Pentaho. ABSTRACT This paper describes a process oriented to the addition of business intelligence (BI) elements at Universidad de Tarapacá (UTA), Arica, Chile. For the purpose, a data mart (DM) was implemented, focused on the Admission and Registration area of Academic Vice-Rectory. Its development required carrying out activities such as to obtain business requirements, to investigate the area key performance indicator (KPI), to analyze several internal information sources and to develop a dimensional model based on the Kimball star schema. For proper implementation and integration of these data repositories, extraction, transformation and loading (ETL) processes were carried out from two data sources. The creation of this DM, allowed users of the Academic Vice-Rectory to visualize the information they required through online analytical processing (OLAP) tools. The implementation was complemented with reporting and dashboards tools. The integration of these elements formed a business intelligence platform, which allows supporting of the information and analysis requirements associated to the admission and registration process.Keywords: Business intelligence, data warehouse, data mart, KPI, ETL, OLAP, dashboard, Pentaho. INTRODUCCIÓNHoy en día, las organizaciones están comprendiendo la importancia de la gestión de la información y las ventajas competitivas que implica su uso. Este proceso de gestión consiste en lograr de una manera eficiente el análisis de distintos tipos de datos de la empresa y su entorno, a través de la explotación de la información por medio de las tecnologías de la información (T...

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“…Now, we present a study of the best available conceptual and logical models for data warehousing. Existing conceptual modeling approaches can be broadly classified into Extensions of Entity Relationship (E/R) models ( [6][7][8][9][10]), Extensions of Unified Modeling Language (UML) ( [11][12][13]) and Ad-hoc ( [14][15][16][17]) design models. Several different logical models have also been proposed to model multidimensional data in the past few years.…”

Section: Related Workmentioning

confidence: 99%

On the Requirements for User-Centric Spatial Data Warehousing and SOLAP

Viswanathan

Schneider

2011

Database Systems for Adanced Applications

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Abstract. Data warehouses and OLAP systems help to analyze complex multidimensional data and provide decision support. With the availability of large amounts of spatial data in recent years, several new models have been proposed to enable the integration of spatial data in data warehouses and to help analyze such data. This is often achieved by a combination of GIS and spatial analysis tools with OLAP and database systems, with the primary goal of supporting spatial analysis dimensions, spatial measures and spatial aggregation operations. However, this poses several new challenges related to spatial data modeling in a multidimensional context, such as the need for new spatial aggregation operations and ensuring consistent and valid results. In this paper, we review the existing modeling strategies for spatial data warehouses and SOLAP in all three levels: conceptual, logical and implementation. While studying these models, we gather the most essential requirements for handling spatial data in data warehouses and use insights from spatial databases to provide a "meta-framework" for modeling spatial data warehouses. This strategy keeps the user as the focal point and achieves a clear abstraction of the data for all stakeholders in the system. Our goal is to make analysis more user-friendly and pave the way for a clear conceptual model that defines new multidimensional abstract data types (ADTs) and operations to support spatial data in data warehouses.

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A UML-based data warehouse design method

Cited by 116 publications

References 18 publications

A survey on summarizability issues in multidimensional modeling

A survey on summarizability issues in multidimensional modeling

Incorporación De Elementos De Inteligencia De Negocios en El Proceso De Admisión Y Matrícula De Una Universidad Chilena

On the Requirements for User-Centric Spatial Data Warehousing and SOLAP

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