Conceptual Modeling Of Data Warehouses Research Articles

Data Model Quality Metrics of Data Warehouse: A Survey

Quality of Data warehouse plays major role in managerial strategic decisions. Multidimensional data model acts as base for data warehouse, in this way quality of data model greatly affect data warehouse Quality. Metrics are used to measure the quality of data warehouse. Different authors have proposed various metrics to evaluate the quality factors of conceptual data models for data warehouse such as understandability, maintainability, Complexity, Efficiency etc. This paper summarizes the various metrics proposed for conceptual model of Data Warehouse and their formal and empirical validation to prove their correctness and practical utility.

Transforming of Construction Contract Administration: Data Warehouse Modeling

Construction stakeholders always use standard form of contract to regulate their contractual obligations and expectations during contract administration process. They need to refer a vast of contract provisions in the process. It always leads misunderstanding and interpretation errors as most of construction practitioners are without a proper legal background. Hence the research aims to apply data warehouse technology to assist end-users for better and/or right decision making. A conceptual model of data warehouse for contract administration is developed and verified by twelve reputed experts in the construction industry. The results show that the data warehouse modeling is feasible and practical to the construction practitioners. It would render a more effective and proactive approach in contract administration towards dispute resolution or prevention.

Building a secure star schema in data warehouses by an extension of the relational package from CWM

Data Warehouses (DWs) are widely accepted as the core of current decision support systems. Therefore, it is vital to incorporate security requirements from the early stages of the DWs projects and enforce them in the further design phases. Very few approaches specify security and audit measures in the conceptual modeling of DWs. Furthermore, these security measures are specified in the final implementation on top of commercial systems as there is not a standard relational representation of security measures for DWs (i.e. the well-known star schema does not allow us to specify security and audit measures on its multidimensional representation of data; instead, they must be specified on top of the implemented relational tables). On the other hand, the Common Warehouse Metamodel (CWM) has been accepted as the standard for the exchange and the interoperability of metadata. Nevertheless, it does not allow us to specify security measures for DWs. In this paper, we make use of the own extension mechanisms provided by the CWM to extend the relational package in order to build a star schema that represents the security and audit rules captured during the conceptual modeling phase of DWs. Finally, in order to show the benefits of our extension, we apply it to a case study related to the management of the pharmacy consortium business.

A UML 2.0 profile to design Association Rule mining models in the multidimensional conceptual modeling of data warehouses

By using data mining techniques, the data stored in a Data Warehouse (DW) can be analyzed for the purpose of uncovering and predicting hidden patterns within the data. So far, different approaches have been proposed to accomplish the conceptual design of DWs by following the multidimensional (MD) modeling paradigm. In previous work, we have proposed a UML profile for DWs enabling the specification of main MD properties at conceptual level. This paper presents a novel approach to integrating data mining models into multidimensional models in order to accomplish the conceptual design of DWs with Association Rules (AR). To this goal, we extend our previous work by providing another UML profile that allows us to specify Association Rules mining models for DW at conceptual level in a clear and expressive way. The main advantage of our proposal is that the Association Rules rely on the goals and user requirements of the Data Warehouse, instead of the traditional method of specifying Association Rules by considering only the final database implementation structures such as tables, rows or columns. In this way, ARs are specified in the early stages of a DW project, thus reducing the development time and cost. Finally, in order to show the benefits of our approach, we have implemented the specified Association Rules on a commercial database management server.