Examining Quality Factors Influencing the Success of Data Warehouse

Increased organizational dependence on data warehouse (DW) systems has drived the management attention towards improving data warehouse systems to a success. However, the successful implementation rate of the data warehouse systems is low and many firms do not achieve intended goals. A recent study shows that improves and evaluates data warehouse success is one of the top concerns facing IT/DW executives. Nevertheless, there is a lack of research that addresses the issue of the data warehouse systems success. In addition, it is important for organizations to learn about quality needs to be emphasized before the actual data warehouse is built. It is also important to determine what aspects of data warehouse systems success are critical to organizations to help IT/DW executives to devise effective data warehouse success improvement strategies. Therefore, the purpose of this study is to further the understanding of the factors which are critical to evaluate the success of data warehouse systems. The study attempted to develop a comprehensive model for the success of data warehouse systems by adapting the updated DeLone and McLean IS Success Model. Researcher models the relationship between the quality factors on the one side and the net benefits of data warehouse on the other side. This study used quantitative method to test the research hypotheses by survey data. The data were collected by using a web-based survey. The sample consisted of 244 members of The Data Warehouse Institution (TDWI) working in variety industries around the world. The questionnaire measured six independent variables and one dependent variable. The independent variables were meant to measure system quality, information quality, service quality, relationship quality, user quality, and business quality. The dependent variable was meant to measure the net benefits of data warehouse systems. Analysis using descriptive analysis, factor analysis, correlation analysis and regression analysis resulted in the support of all hypotheses. The research results indicated that there are statistically positive causal relationship between each quality factors and the net benefits of the data warehouse systems. These results imply that the net benefits of the data warehouse systems increases when the overall qualities were increased. Yet, little thought seems to have been given to what the data warehouse success is, what is necessary to achieve the success of data warehouse, and what benefits can be realistically expected. Therefore, it appears nearly certain and plausible that the way data warehouse systems success is implemented in the future could be changed

An overview of data warehouse design approaches and tecbniques

A Data Warehouse (DW) is a database that stores information oriented to satisfy decision-making requests. It ia a database with some particular features concerning the data it contains and its utilisation. The features of DWs cause the DW design process and strategies to be different frome the ones for OLTP Systems. This work presents a brief description of different approaches and techniques that address the DW design problem

INTEGRATION OF DATA FROM HETEROGENEOUS SOURCES USING ETL TECHNOLOGY.

Data integration is a crucial issue in environments of heterogeneous data sources. At present mentioned heterogeneity is becoming widespread. Whenever, based on various data sources, we want to gain useful information and knowledge we must solve data integration problem in order to apply appropriate analytical methods on comprehensive and uniform data. Such activity is known as knowledge discovery from data process. Therefore approaches to data integration problem are very interesting and bring us closer to the "age of information". The paper presents an architecture, which implements knowledge discovery from data process. The solution combines ETL technology and wrapper layer known from mediated systems. It also provides semantic integration through connections mechanism between data elements. The solution allows for integration of any data sources and implementation of analytical methods in one environment. The proposed environment is verified by applying it to data sources on the foundry industry

Making International Refugee Law Relevant Again: A Proposal for Collectivized and Solution-Oriented Protection

International refugee law is in crisis. Even as armed conflict and human rights abuse continue to force individuals and groups to flee their home countries, many governments are withdrawing from the legal duty to provide refugees with the protection they require. While governments proclaim a willingness to assist refugees as a matter of political discretion or humanitarian goodwill, they appear committed to a pattern of defensive strategies designed to avoid international legal responsibility toward involuntary migrants. Some see this shift away from a legal paradigm of refugee protection as a source for enhanced operational flexibility in the face of changed political circumstances. For refugees themselves, however, the increasingly marginal relevance of international refugee law has in practice signalled a shift to inferior or illusory protection. It has also imposed intolerable costs on many of the poorest countries, and has involved developed states in practices antithetical to their basic political values

MONIL Language, an Alternative for Data Integration El Lenguaje MONIL, una Alternativa para la Integración de Datos

Abstract Data integration is a process of retrieving, merging and storing of data originated in heterogeneous sources of data. The main problem facing the data integration is the structural and semantic heterogeneity of participating data. A concern of research communities in computer sciences is the development of semi-automatic tools to assist the user in an effective way in the data integration processes. This paper introduces a programming language called MONIL, as an alternative to integrate data by means of design, storage and program execution. MONIL is based on the use of meta-data, conversion functions, a meta-model of integration and a scheme of integration suggestions. MONIL offers to the user a dedicated work environment with built-in semi-automatic tools supporting the integration process in three stages. Keywords: data integration, integration language, databases, metadata. Resumen La integración de datos es el proceso de extracción, mezcla y almacenamiento de datos provenientes de fuentes de datos heterogéneas. El problema principal que enfrenta la integración de datos es la heterogeneidad estructural y semántica de los datos que participan. Una preocupación en las comunidades de investigación de las ciencias computacionales, es el desarrollo de herramientas semiautomáticas que asistan a los usuarios de forma efectiva en los procesos de integración de datos. Este artículo presenta un lenguaje de programación llamado MONIL, como una alternativa para integrar datos mediante el diseño, almacenamiento y ejecución de programas. MONIL está basado en el uso de metadatos, funciones de conversión, un metamodelo de integración y un esquema de sugerencias de integración. MONIL ofrece al usuario un ambiente de trabajo dedicado con herramientas semiautomáticas integradas y que soportan un proceso de integración en tres etapas. Palabras claves: integración de datos, lenguaje de integración, bases de datos, bodegas de datos, metadatos

O processo de refrescamento nos sistemas de data warehouse: guião de modelação conceptual da tarefa de extracção de dados

Nos últimos anos, os Sistemas de Data Warehouse (SDW) têm sido os sistemas de apoio à decisão mais utilizados nas organizações, integrando dados de diferentes fontes nos Repositórios de Data Warehouse (RDW). Com o decorrer do tempo de funcionamento do sistema, coloca-se o problema do refrescamento, entendido como o problema de assegurar que os conteúdos dos RDW são periodicamente refrescados, de modo a reflectirem as alterações que ocorrem nos dados das fontes que lhes servem de base. Esta dissertação propõe uma abordagem que tem como objectivos principais tornar explícito e documentar o problema do refrescamento e apresentar um guião de modelação conceptual da tarefa de extracção de dados que possa enriquecer as fases subsequentes de desenho para a especificação formal do processo de refrescamento. São dois os contributos desta dissertação. Primeiro, providencia um quadro detalhado sobre o problema do refrescamento que inclui os conceitos e questões fundamentais que permitem caracterizar os SDW, na perspectiva das funcionalidades no apoio à decisão, das abordagens de integração de fontes de dados e dos componentes da arquitectura, os constrangimentos e tarefas que compreendem o processo de refrescamento, as principais abordagens disponíveis na literatura. Segundo, propõe um guião de apoio à modelação conceptual da tarefa de extracção de dados, com base na UML, apresentando os passos que devem ser seguidos pelo designer e disponibilizando as construções que permitem representar os dados que se extraem das fontes, de acordo com as regras que permitem isolar e extrair os dados relevantes para a tomada de decisão.Data Warehouse Systems (DWS) have become very popular in the last years for decision making, by integrating data from internal and external sources into data warehouse stores. As times advances and the sources from which warehouse data is integrated change, the data warehouse contents must be regularly refreshed, such that warehouse data reflect the state of the underlying data sources. This dissertation proposes an approach which main goals are to explicit and document the data warehouse refreshment problem and to present a guidelines for the conceptual modelling of data extraction in order to enrich the subsequent design steps for the formal specification of the refreshment process. The contributions of our approach are twofold. First, it provides a detailed outline of data warehouse refreshment problem, including the main concepts and issues that characterise the general domain of the DWS, such as decision making functionalities, data sources integration approaches and architecture and, the refreshment tasks and constraints as well as the main approaches. Second, it proposes a guidelines for an UML conceptual modelling of data extraction, by giving the sequence of steps for a designer to follow, the modelling constructs for the definition of extracting data, according to the rules that must be accomplished for extracting relevant data