Abstract. In this paper we present a method to validate user multidimensional requirements expressed in terms of SQL queries. Furthermore, our approach automatically generates and proposes the set of multidimensional schemas satisfying the user requirements, from the organizational operational schemas. If no multidimensional schema is generated for a query, we can state that requirement is not multidimensional. Keywords: Multidimensional Design, Design by Examples, DW. 1

Nowadays, it is widely accepted that the data warehouse design task should be largely automated. Furthermore, the data warehouse conceptual schema must be structured according to the multidimensional model and as a consequence, the most common way to automatically look for subjects and dimensions of analysis is by discovering functional dependencies (as dimensions functionally depend of the fact) over the data sources. Most advanced methods for automating the design of the data warehouse carry out this process from relational OLTP systems, assuming that a RDBMS is the most common kind of data source we may find, and taking as starting point a relational schema. In contrast, in our approach we propose to rely instead on a conceptual representation of the domain of interest formalized through a domain ontology expressed in the DL-Lite Description Logic. In our approach, we propose an algorithm to discover functional dependencies from the domain ontology that exploits the inference capabilities of DL-Lite, thus fully taking into account the semantics of the domain. We also provide an evaluation of our approach in a real-world scenario. 1.

Abstract not found

Abstract. Data Warehouse (DWH) systems represent a single source of information for analyzing the status, the development and the results of an organization. Today’s DWH systems provide many different services to different kinds of users. People involved in designing and managing DWH systems need to see the big picture of how the DWH is being used, to have an overview of the current situation, and to be able to visualize future scenarios. Currently, there is a lack of such general models in Data Warehousing. We introduce the UML Profile for Modeling DWH Usage for modeling the different kinds of DWH usage on a conceptual level. It uses features of UML intended for the purpose of creating abstract, general models. The profile distinguishes four perspectives of usage, and allows to model details of the users. The UML Profile is applied to examples illustrating some of the application scenarios. 1

Abstract—Data warehouse (DW) systems integrate data from heterogeneous sources and are used by decision makers to analyze the status and the development of an organization. Traditionally, requirement analysis approaches for DWs have focused purely on information needs of decision makers, without considering other kinds of requirements such as security or performance. But modeling these issues in the early stages of the development is a cornerstone for building a DW that satisfies user expectations. In this paper, we define the two kinds of requirements for data warehousing as information and quality-of-service requirements and combine them in a comprehensive approach based on MDA (Model Driven Architecture). This allows a separation of concerns to model requirements without losing the connection between information and quality-of-service, also in the following conceptual or logical design stages. Finally, in this paper, we introduce a security requirement model for data warehousing, and a three-step process for modeling security requirements, thus illustrating the applicability of our approach with an example. I.

Enterprise organizations use Data Warehouses (DWHs) to analyze their performance. Performance is judged regarding the achievement of goals.

Data Warehouse (DWH) systems are used by decision makers for performance measurement and decision support. Currently the main focus of the DWH research field is not as much on the interaction of the DWH with the organization, its context and the way it supports the organization's strategic goals, as on database issues. The aim of my thesis is to emphasize and describe the relationship between the DWH and the organization with conceptual models, and to use this knowledge to support data interpretation with business metadata.

Abstract. Data Warehouse (DWH) systems are used by decision makers for performance measurement and decision support. Currently the main focus of the DWH research field is not as much on the interaction of the DWH with the organization, its context and the way it supports the organization’s strategic goals, as on database issues. The aim of my thesis is to emphasize and describe the relationship between the DWH and the organization with conceptual models, and to use this knowledge to support data interpretation with business metadata. 1 Problem Statement and Research Question Data Warehouse (DWH) systems represent a single source of information to analyze the development and results of an organization[1]. Measures such as the number of transactions per customer or the increase of sales during a promotion are used to recognize warning signs and to decide on future investments with regard to the strategic goals of the organization. Currently, the main focus of the DWH research field is on database issues,

This paper presents a new approach to automate the multidimensional design of Data Warehouses. In our approach we propose a semi-automatable method aimed to find the business multidimensional concepts from a domain ontology representing different and potentially heterogeneous data sources of our business domain. In short, our method identifies business multidimensional concepts from heterogeneous data sources having nothing in common but that they are all described by an ontology.

journal homepage: www.elsevier.com/locate/datak A framework for multidimensional design of data warehouses from ontologies