The words On-Line Analytical Processing bring together a set of tools, that use multidimensional modeling in the management of information to improve the decision making process. Lately, a lot of work has been devoted to modeling the multidimensional space. The aim of this paper is twofold. On one hand, it compiles and classifies some of that work, with regard to the design phase they are used in. On the other hand, it allows to compare the different terminology used by each author, by placing all the terms in a common framework. 1

The words On-Line Analytical Processing (OLAP) bring together a set of tools, that use multidimensional modeling in the extraction of information from the Data Warehouse. Lately, a lot of work has been devoted to modeling the multidimensional space. The aim of this paper is twofold. On one hand, it compiles and classifies most of that work. On the other hand, it allows to compare the different terminology used by each author, by placing all the terms in a common framework.

"On-Line Analytical Processing" tools are used to extract information from the "Data Warehouse" in order to help in the decision making process. These tools are based on multidimensional concepts, i.e. facts and dimensions. In this paper we study the meaning of facts, and the dependencies in multidimensional data. This study is used to find relationships between cubes (in an Object-Oriented framework) and explain navigation operations.

. As diverse demands are placed on On-Line Analytical Processing (OLAP) systems, distribution of the supporting multidimensional database (MDDB), and thus fragmentation, become an issue. We consider a recent multidimensional data model, and show that two basic strategies of fragmentation are possible: slice 'n dice and sever. The operators that realise these methods are composed from the basic set given in the data model, and the strategies are shown to satisfy the fragmentation correctness rules. 1 Introduction Multidimensional databases (MDDB) exploit the multidimensional nature of On-Line Analytical Processing (OLAP) [Codd et al., 1993]. As the popularity of OLAP grows, an increasingly diverse group of users are making demands of the systems, possibly with conflicting goals. In order to meet the performance requirements of the systems, implementation as distributed databases will become an important consideration. Fragmentation is an important part of distributed design [ Ozsu and ...

In the last years, lots of work have been devoted to multidimensional modeling, star shape schemas and OLAP operations. However, “drill-across ” has not captured as much attention as other operations. This operation allows to change the subject of analysis keeping the same analysis space we were using to analyze another subject. It is assumed that this can be done if both subjects share exactly the same analysis dimensions. In this paper, besides the implementation of an algebraic set of operations on a RDBMS, we are going to show when and how we can change the subject of analysis in the presence of semantic relationships, even if the analysis dimensions do not exactly coincide.

. In this paper, we try to outline the goodness of using an O-O model on designing multidimensional Data Marts. We argue that multidimensional modeling is lacking in semantics, which can be obtained by using the O-O paradigm. Some benefits that could be obtained by doing this are classified in six O-O-Dimensions (i.e. Classification/Instantiation, Generalization/Specialization, Aggregation/Decomposition, Caller/Called, Derivability, and Dynamicity), and exemplified with specific cases. Key Words: Object-Oriented Data Model, Semantics, Multidimensionality, Data Marts 1 Introduction Data warehousing is a relatively new research area. W. Inmon is considered the father of the "Data Warehouse" (DW), whose definition can be read in [Inm96]: "A data warehouse is a subject oriented, integrated, non-volatile, and time variant collection of data in support of management's decisions". Note that the definition does not refer to the size of the warehouse. However, if it is integrated and...

Abstract. Although multidimensionality has been widely accepted as the best solution to conceptual modeling, there is not such agreement about the set of operators to handle multidimensional data. This paper presents a comparative of the existing multidimensional algebras trying to find a common backbone, as well as it discusses about the necessity of a reference multidimensional algebra and the current state of the art. 1

This paper presents a multidimensional conceptual ObjectOriented model, its structures, integrity constraints and query operations.

Doctor en Informàtica per la Universitat Politècnica de CatalunyaTo those always there. This work is, somehow, also yours. Acknowledgements “Every thesis begins with a single keystroke.” PhD Comics, 2009 My foremost thank goes to Alberto Abelló. Without him, this dissertation would have not been possible. I thank him for his endless patience and encouragement, for his exigency and rigor, and for his insights and suggestions that helped to shape my research skills. Likewise, I really appreciate that he always had time for me, despite it was not easy with two little children around. Thanks Alberto! I would also like to thank Ernest Teniente and my colleagues in the Grup Facing On towards Logic database Rule Enforcement, and people in the Secció de Sistemes d’Informació, for their support. Thanks to Dr. Diego Calvanese, for the opportunity to stay in his group for five months, and people in the KRDB Centre for Knowledge and Data, who made me feel like at home. I also thank Joan Marc Montesó, for implementing the AMDO tool, and Antonio Montero,

Abstract. Recent efforts to support analytical tasks over relational sources have pointed out the necessity to come up with flexible, powerful means for analyzing the issued queries and exploit them in decisionoriented processes (such as query recommendation or physical tuning). Issued queries should be decomposed, stored and manipulated in a dedicated subsystem. With this aim, we present a novel approach for representing SQL analytical queries in terms of a multidimensional algebra, which better characterizes the analytical efforts of the user. In this paper we discuss how an SQL query can be formulated as a multidimensional algebraic characterization. Then, we discuss how to normalize them in order to bridge (i.e., collapse) several SQL queries into a single characterization (representing the analytical session), according to their logical connections. 1