A UML framework for OLAP conceptual modeling

Data warehouses are used by organizations around the world to store huge volumes of historical data. Ultimately, the purpose of the warehouse is to allow decision makers to assess both the history and, more importantly, the future of the organization. In practice, the capacity to make meaningful decisions is further supported through the use of Online Analytical Processing (OLAP) applications that provide more sophisticated representations of the warehouse data. In order to do this, OLAP systems rely on a multidimensional conceptual data model that represents the core elements of the data warehouse, as well as the relationships between them. Currently, there is no definitive conceptual model for this kind of environment. It is therefore quite difficult for data warehouse designers to express the kinds of complex analytical requirements which arise in real-world situations. In this thesis, we propose a robust and flexible conceptual model that can be used to represent multi-dimensional OLAP domains. Specifically, we present a profile extension of the Unified Modeling Language (UML) that consists of a set of stereotypes, constraints and tagged values that elegantly represent multi-dimensional properties at the conceptual level. We also make use of the Object Constraint Language (OCL) to ensure the correctness and completeness of the specification, thereby avoiding an arbitrary use of the basic components. Furthermore, we demonstrate how the new OLAP profile is utilized in MagicDraw, one of the leading UML development tools. The end result is an OLAP Modeling Environment (OME) that should significantly reduce development time, as well as improving the quality of the analytical interface for the end user

XWeB: the XML Warehouse Benchmark

With the emergence of XML as a standard for representing business data, new decision support applications are being developed. These XML data warehouses aim at supporting On-Line Analytical Processing (OLAP) operations that manipulate irregular XML data. To ensure feasibility of these new tools, important performance issues must be addressed. Performance is customarily assessed with the help of benchmarks. However, decision support benchmarks do not currently support XML features. In this paper, we introduce the XML Warehouse Benchmark (XWeB), which aims at filling this gap. XWeB derives from the relational decision support benchmark TPC-H. It is mainly composed of a test data warehouse that is based on a unified reference model for XML warehouses and that features XML-specific structures, and its associate XQuery decision support workload. XWeB's usage is illustrated by experiments on several XML database management systems

Unified Approach in the DSS Development Process

The structure of today's decision support environment become very complex due to new generation of Business Intelligence applications and technologies like Data Warehouse, OLAP (On Line Analytical Processing) and Data Mining. In this respect DSS development process are not simple and needs an adequate methodology or framework able to manage different tools and platforms to achieve manager's requirements. The DSS development process must be view like a unified and iterative set of activities and operations. The new techniques based on Unified Process (UP) methodology and UML (Unified Modeling Language) it seems to be appropriate for DSS development using prototyping and RAD (Rapid Application Development) techniques. In this paper we present a conceptual framework for development and integrate Decision Support Systems using Unified Process Methodology and UML.Decision Support Systems, Unified Process, UML, Prototyping, DSS Tools

Pattern tree-based XOLAP rollup operator for XML complex hierarchies

With the rise of XML as a standard for representing business data, XML data warehousing appears as a suitable solution for decision-support applications. In this context, it is necessary to allow OLAP analyses on XML data cubes. Thus, XQuery extensions are needed. To define a formal framework and allow much-needed performance optimizations on analytical queries expressed in XQuery, defining an algebra is desirable. However, XML-OLAP (XOLAP) algebras from the literature still largely rely on the relational model. Hence, we propose in this paper a rollup operator based on a pattern tree in order to handle multidimensional XML data expressed within complex hierarchies

Combining Objects with Rules to Represent Aggregation Knowledge in Data Warehouse and OLAP Systems

Les entrepôts de données reposent sur la modélisation multidimensionnelle. A l'aide d'outils OLAP, les décideurs analysent les données à différents niveaux d'agrégation. Il est donc nécessaire de représenter les connaissances d'agrégation dans les modèles conceptuels multidimensionnels, puis de les traduire dans les modèles logiques et physiques. Cependant, les modèles conceptuels multidimensionnels actuels représentent imparfaitement les connaissances d'agrégation, qui (1) ont une structure et une dynamique complexes et (2) sont fortement contextuelles. Afin de prendre en compte les caractéristiques de ces connaissances, nous proposons de les représenter avec des objets (diagrammes de classes UML) et des règles en langage PRR (Production Rule Representation). Les connaissances d'agrégation statiques sont représentées dans les digrammes de classes, tandis que les règles représentent la dynamique (c'est-à-dire comment l'agrégation peut être effectuée en fonction du contexte). Nous présentons les diagrammes de classes, ainsi qu'une typologie et des exemples de règles associées.Agrégation ; Entrepôt de données ; Modèle conceptuel multidimensionnel ; OLAP ; Règle de production ; UML