A Conceptual Model for Multidimensional Data (original) (raw)
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A Conceptual Modelling Perspective for Data Warehouses
Electronic Business Engineering, 1999
The volume of information of the most various types stored electronically in a company is increasing to an ever-greater extent. While in the field of operational systems everything is aimed at achieving the quickest possible throughput, in the dispositive field, questions regarding the total overview or detailed views are of interest. OLAP servers are multidimensionally structured. They are therefore suited for the analysis of multidimensional datastores. The functionality of the OLAP server, such as, creation of forms, drill down, roll up, slice and dice, analysis technology, multidimensional consolidation, etc. demonstrate the advantages of this tool. The analysis of the relevant features of the data warehouse and OLAP is based on both the mainstream literature and on our experience in a two-year project Data Warehouse for Tupperware Inc. The second problem addressed by this paper is the discussion of recent approaches for a proposition some formal definitions of basic constructs used in so called multidimensional modelling which seems to be an important technique for data warehousing and OLAP. It is different from E-R modelling and offers a number of important advantages that the E-R modelling lacks. We show a relationship of E-R modelling to the multidimensional modelling and describe a broad class of multidimensional databases based on so called constellation schemes with explicit hierarchies.
In the context of data warehouse design, a basic role is played by conceptual modeling, that provides a higher level of abstraction in describing the warehousing process and architecture in all its aspects, aimed at achieving independence of implementation issues. This chapter focuses on a conceptual model called the DFM that suits the variety of modeling situations that may be encountered in real projects of small to large complexity. The aim of the chapter is to propose a comprehensive set of solutions for conceptual modeling according to the DFM and to give the designer a practical guide for applying them in the context of a design methodology. Besides the basic concepts of multidimensional modeling, the other issues discussed are descriptive and cross-dimension attributes; convergences; shared, incomplete, recursive, and dynamic hierarchies; multiple and optional arcs; and additivity. Rizzi ments; usually, it relies on a graphical notation that facilitates writing, understanding, and managing conceptual schemata by both designers and users.
The GMD Data Model for Multidimensional Information: A Brief Introduction
2003
In this paper we introduce a novel data model for multidimensional information, GMD, generalising the MD data model first proposed in Cabibbo et al (EDBT-98). The aim of this work is not to propose yet another multidimensional data model, but to find the general precise formalism encompassing all the proposals for a logical data model in the data warehouse field. Our proposal is compatible with all these proposals, making therefore possible a formal comparison of the differences of the models in the literature, and to study formal properties or extensions of such data models. Starting with a logic-based definition of the semantics of the GMD data model and of the basic algebraic operations over it, we show how the most important approaches in DW modelling can be captured by it. The star and the snowflake schemas, Gray’s cube, Agrawal’s and Vassiliadis’ models, MD and other multidimensional conceptual data can be captured uniformly by GMD. In this way it is possible to formally understand the real differences in expressivity of the various models, their limits, and their potentials.
Data Warehouse Conceptual Modeling Approaches
Data Warehouse (DW) Systems enable managers in corporations to acquire and integrate information from heterogeneous sources and to query huge databases efficiently. Building a Data Warehouse requires focusing on the conceptual design phase due to the specific requirements found in the conceptual model used. Various approaches were presented by researchers to support the conceptual design of data warehouses as there is no generic and well formalized approach used by data warehouse designers synonymous to the Entity-Relationship (ER) Model used in the database environment. This paper will present the data warehouse requirements that are required to be present in the conceptual model. Then, a case study will be used to illustrate how the proposed conceptual models for data warehouses could be used. Finally, a comparison will be conducted to show the most significant model that is more suitable than others in supporting the conceptual design of data warehouses.
OO Approach for Developing Conceptual Model for A Data Warehouse
The conceptual model is considered as first tier in the architecture of data warehouse. To see the performance of any system we want that no of parameter should be displayed at the same time. Here no of parameters are the dimensions according to which we want to see the business. This approach is Multidimensional and we generally abbreviated as conceptual multidimensional model. In this paper we are going to propose a object oriented approach for developing the conceptual model in which main focus will be on the properties: 1.Aggregation 2.Generalization 3 Association which are explained by UML.
Towards A Generic Conceptual Model for Data Warehouses
During the last few years, developers had proposed various approaches to the conceptual design of Data Warehouse (DW). Due to the variation and differences between these proposed approaches, this paper will first show the multidimensional modeling properties that are required to be supported in the conceptual model of DW then it will compare and evaluate the proposed conceptual modeling approaches. After Evaluation, the model that was categorized to be the best model for DW conceptual Design will be taken through this paper for further extensions to address all multidimensional properties that it had missed and also extend it to support the conceptual design of Temporal DW.
Data warehouse is founded on modeling of multi-dimensional. By using (OLAP) Online Analytical Processing tool, and analyzes multi-dimensional data. In common users must analyzed data at a various overall standards, That why aggregated knowledges must be appropriate represented in the model of multi-dimensional, and planned in depended of physical and logical models. Any ways, existing " conceptual-multi-dimensional-models " weakly represented overall knowledges, that (1) is highly contextual and, (2) has a dynamics structure and complex. To account merits of this knowledge, we proposed to represent the merits of this knowledge with bases in the (PRR) " Production Rule Representation " and objects (UML class diagrams), represent static aggregate knowledges in the class diagram. However bases represented the dynamic, we prepare a typology and associated bases as examples and the class diagrams, We discussed that this aggregation knowledge representation help the requests of user in a data warehouses projects as an early modeling.
The GMD Data Model and Algebra for Multidimensional Information
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In this paper we introduce GMD, an abstract but rich data model for representing multidimensional information, equipped with logicbased semantics and seamlessly integrated with a fully compositional algebra also equipped with logic-based semantics. The aim of this work is to propose an homogeneous approach to formally represent all the aspects of multidimensional data, as proposed by the various data models presented in the literature.
On a Conceptual Data Model with Orientation to Data Integration
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In this paper a conceptual data model oriented to data integration is proposed. Formal definition of the considered conceptual data model is provided. To define the behavior of entities of the conceptual level, an algebra over such entities was developed. Formalization issues of data integration concept are discussed. Principles of mapping of source data models basic constructions into conceptual data model are considered. Mapping from data sources into conceptual schema is defined as an algebraic program.