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Building DSS using knowledge discovery in database applied to admission and registration functionsEl-Ragal, Ahmed Abdel Hameed Hassan January 2001 (has links)
This research investigates the practical issues surrounding the development and implementation of Decision Support Systems (DSS). The research describes the traditional development approaches analyzing their drawbacks and introduces a new DSS development methodology. The proposed DSS methodology is based upon four modules; needs' analysis, data warehouse (DW), knowledge discovery in database (KDD), and a DSS module. The proposed DSS methodology is applied to and evaluated using the admission and registration functions in Egyptian Universities. The research investigates the organizational requirements that are required to underpin these functions in Egyptian Universities. These requirements have been identified following an in-depth survey of the recruitment process in the Egyptian Universities. This survey employed a multi-part admission and registration DSS questionnaire (ARDSSQ) to identify the required data sources together with the likely users and their information needs. The questionnaire was sent to senior managers within the Egyptian Universities (both private and government) with responsibility for student recruitment, in particular admission and registration. Further, access to a large database has allowed the evaluation of the practical suitability of using a data warehouse structure and knowledge management tools within the decision making framework. 1600 students' records have been analyzed to explore the KDD process, and another 2000 records have been used to build and test the data mining techniques within the KDD process. Moreover, the research has analyzed the key characteristics of data warehouses and explored the advantages and disadvantages of such data structures. This evaluation has been used to build a data warehouse for the Egyptian Universities that handle their admission and registration related archival data. The decision makers' potential benefits of the data warehouse within the student recruitment process will be explored. The design of the proposed admission and registration DSS (ARDSS) will be developed and tested using Cool: Gen (5.0) CASE tools by Computer Associates (CA), connected to a MSSQL Server (6.5), in a Windows NT (4.0) environment. Crystal Reports (4.6) by Seagate will be used as a report generation tool. CLUST AN Graphics (5.0) by CLUST AN software will also be used as a clustering package. Finally, the contribution of this research is found in the following areas: A new DSS development methodology; The development and validation of a new research questionnaire (i.e. ARDSSQ); The development of the admission and registration data warehouse; The evaluation and use of cluster analysis proximities and techniques in the KDD process to find knowledge in the students' records; And the development of the ARDSS software that encompasses the advantages of the KDD and DW and submitting these advantages to the senior admission and registration managers in the Egyptian Universities. The ARDSS software could be adjusted for usage in different countries for the same purpose, it is also scalable to handle new decision situations and can be integrated with other systems.
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Metodologia e uso de técnica de exploração e análise de dados na construção de Date WarehouseSANTOS, Roberto Ângelo Fernandes January 2002 (has links)
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Previous issue date: 2002 / O volume de informações a ser trabalhado na tomada das decisões gerenciais
supera largamente a capacidade do processamento humano, mecânico e dos sistemas
transacionais atuais, exigindo ferramentas de apoio à decisão mais adequadas aos novos
desafios gerenciais. Mesmo aplicando-se modelos de decisão tidos como adequados, uma
grande parte das implementações de Sistemas de Informação não atingem os resultados
esperados, o que levam muitos deles ao fracasso total ou parcial. Acredita-se que com
obtenção de resultados rápidos se possa conseguir um maior envolvimento do usuário final, o
que segundo os especialistas diminui bastante a possibilidade de fracasso.
Esse trabalho visa a utilizar técnicas de análise e exploração de dados na
construção de soluções de Sistemas de Apoio à Decisão, em especial na construção de Data
Warehouse(DW). Aproveita-se o conhecimento adquirido com a aplicação dessas técnicas,
mostrando a sua importância nas diversas fases de sua construção de um DW. Propõe-se e
implementa-se uma metodologia chamada FASTCUBE, que é baseada em um modelo de préprocessamento
de dados. Ela incorpora de maneira rápida os metadados extraídos diretamente
da massa de dados. Acelerar e sedimentar a compreensão do problema, sempre levando-se em
consideração a qualidade dos dados, durante todas as suas fases é um dos pontos forte dessa
metodologia. O seu objetivo final é acelerar o processo de visualização do modelo de decisão,
através de um protótipo de modelo dimensional, com dados operacionais amostrados no início
do processo e tratados durante o mesmo
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