Towards Conceptual Multidimensional Design in Decision Support Systems

International audienceMultidimensional databases support efficiently on-line analytical processing (OLAP). In this paper, we depict a model dedicated to multidimensional databases. The approach we present designs decisional information through a constellation of facts and dimensions. Each dimension is possibly shared between several facts and it is organised according to multiple hierarchies. In addition, we define a comprehensive query algebra regrouping the more popular multidimensional operations in current commercial systems and research approaches. We introduce new operators dedicated to a constellation. Finally, we describe a prototype that allows managers to query constellations of facts, dimensions and multiple hierarchies

A functional approach for in line analytical processing

This paper describes an approach to On Line Analytical Processing (OLAP), expresed in the declarative programming paradigm. We define a collection of functions that capture some of the functionality currently provided by multidimensional database product. This is done by defining operations which allow for classifying and reducing relations (tables). Suitably combined, these operations will make possible to carry out the multidimensional analysis of a relational database, and make possible the declarative specification and optimization of multidimensional database queries. The library works over an abstract model of the relational database calculus as defined by Maier, written in the style of model-oriented formal specification in the functional language HaskellEste paper describe una aproximación al Procesamiento Analítico On Line (OLAP), desarrollado en el paradigma de la programación declarativa. Definimos un conjunto de funciones que capturan algunas de las funcionalidades actualmente provistas por las bases de datos multidimensionales existentes. Esto es realizado definiendo operaciones que permiten clasificar y reducir relaciones (tablas). Dichas operaciones, convenientemente combinadas, permitirían llevar a cabo el análisis multidimensional de una base de datos relacional, junto con la especificación declarativa y optimización de las correspondientes consultas multidimensionales sobre dicha base. La biblioteca provista trabaja sobre un modelo abstracto del cálculo de base de datos relacionales tal como ha sido definido por Maier, escrito en un estilo de especificación formal orientada al modelo en el lenguaje funcional HaskellIII Workshop de Ingeniería de Software y Bases de Datos (WISBD)Red de Universidades con Carreras en Informática (RedUNCI

A Flexible Approach to the Multidimensional Model: The Fuzzy Datacube

As a result of the use of OLAP technology in new fields of knowledge and the merge of data from different sources, it has become necessary for models to support this technology. In this paper, we propose a new multidimensional model that can manage imprecision both in dimensions and facts. Consequently, the multidimensional structure is able to model data imprecision resulting from the integration of data from different sources or even information from experts, which it does by means of fuzzy logic

Contraintes pour modèle et langage multidimensionnels

National audienceThis paper defines a constraint-based model dedicated to multidimensional databases. The model we define represents data through a constellation of facts (subjects of analyse) associated to dimensions (axis of analyse), which are possibly shared. Each dimension is organised according to several hierarchies (views of analyse) integrating several levels of data granularity. In order to insure data consistency, we introduce 5 semantic constraints (exclusion, inclusion, partition, simultaneity, totality) which can be intra-dimension or inter-dimensions; the intra-dimension constraints allow the expression of constraints between hierarchies within a same dimension whereas the inter-dimensions constraints focus on hierarchies of distinct dimensions. We also study repercussions of these constraints on multidimensional manipulations and we provide extensions of the multidimensional operators

Modeling Large Scale OLAP Scenarios

In the recent past, different multidimensional data models were introduced to model OLAP (‘Online Analytical Processing’) scenarios. Design problems arise, when the modeled OLAP scenarios become very large and the dimensionality increases, which greatly decreases the support for an efficient ad-hoc data analysis process. Therefore, we extend the classical multidimensional model by grouping functionally dependent attributes within single dimensions, yielding in real orthogonal dimensions, which are easy to create and to maintain on schema design level. During the multidimensional data analysis phase, this technique yields in nested data cubes reflecting an intuitive two-step navigation process: classification-oriented ‘drill-down’/ ‘roll-up’ and description-oriented‘split’/ ‘merge’ operators on data cubes. Thus, the proposed Nested Multidimensional Data Model provides great modeling flexibility during the schema design phase and application-oriented restrictiveness during the data analysis phase