OO Approach for Developing Conceptual Model for A Data Warehouse (original) (raw)
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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.
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.
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.
Research in data warehouse modeling and design
Proceedings of the 9th ACM international workshop on Data warehousing and OLAP - DOLAP '06, 2006
Multidimensional modeling requires specialized design techniques. Though a lot has been written about how a data warehouse should be designed, there is no consensus on a design method yet. This paper follows from a wide discussion that took place in Dagstuhl, during the Perspectives Workshop "Data Warehousing at the Crossroads", and is aimed at outlining some open issues in modeling and design of data warehouses. More precisely, issues regarding conceptual models, logical models, methods for design, interoperability, and design for new architectures and applications are considered.
A UML-based data warehouse design method
Decision Support Systems, 2006
Data warehouses are a major component of data-driven decision support systems (DSS). They rely on multidimensional models. The latter provide decision makers with a business-oriented view to data, thereby easing data navigation and analysis via On-Line Analytical Processing (OLAP) tools. They also determine how the data are stored in the data warehouse for subsequent use, not only by OLAP tools, but also by other decision support tools. Data warehouse design is a complex task, which requires a systematic method. Few such methods have been proposed to date. This paper presents a UML-based data warehouse design method that spans the three design phases (conceptual, logical and physical). Our method comprises a set of metamodels used at each phase, as well as a set of transformations that can be semi-automated. Following our object orientation, we represent all the metamodels using UML, and illustrate the formal specification of the transformations based on OMG's Object Constraint Language (OCL). Throughout the paper, we illustrate the application of our method to a case study.
Conceptual Design of Data Warehouse using Hybrid Methodology
International Journal of Advanced Trends in Computer Science and Engineering, 2020
In recent times, data warehousing achieved tremendous attention in various organizations including universities to analyze important aspects of their academic environment. In this paper, we present an automatic design system to integrate the functionality of both the requirement-driven and data-driven approaches. In addition, it is established on the basis of i* framework and Dimensional Fact Model (DFM) which is used to design the actions of actors, and the relationship existing among the agents in DW (Data Warehouse) environment. Furthermore, the proposed system introduces and illustrates an automatic design technique based on a logical programming to perform the integration of different data sources using UML multidimensional schemas that are reconciled with data sources to improvise the conceptual quality.