Developing A New Decision Support System for University Student Recruitment

This paper investigates the practical issues surrounding the development and implementation of Decision Support Systems (DSS). The paper 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 paper 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 DW structure and knowledge management tools within the decision making framework. 2000 records have been used to build and test the data mining techniques within the KDD process. The records were drawn from the Arab Academy for Science and Technology and Maritime Transport (AASTMT) students’ database (DB). Moreover, the paper has analyzed the key characteristics of DW and explored the advantages and disadvantages of such data structures. This evaluation has been used to build a DW for the Egyptian Universities that handle their admission and registration related archival data. The decision makers’ potential benefits of the DW 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 MS-SQL Server (6.5), in a Windows NT (4.0) environment. Crystal Reports (4.6) by Seagate will be used as a report generation tool. CLUSTAN Graphics (5.0) by CLUSTAN software will also be used as a clustering package. 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

Investigating Business Intelligence (BI) Solution Adoption in Australian Companies: An ERP Perspective

The global Enterprise Resource Planning (ERP) systems industry blossomed in the 1990’s automating back office operations. Although these systems have become essential infrastructure for many companies, it is now critical that these transaction based systems are extended to support more strategic and complex decisions. The demand for more effective decision making tools has seen the move away from Decision Support Systems to more complex solutions. Accordingly ERP vendors have developed a range of solutions focussing on business intelligence (BI) in various functional areas. However the question must now be asked, “What is the status of BI solution integration in Australian companies and what is the nature of the adoption and use of these various solutions”? This paper adopts a two pronged research methodology. The first methodology involved a web based survey to identify BI implementation patterns. This was then expanded upon using a case study approach. The research identified an evolutionary maturity to the way in which BI solutions are adopted in Australian companies

BUILDING DSS USING KNOWLEDGE DISCOVERY IN DATABASE APPLIED TO ADMISSION & REGISTRATION FUNCTIONS

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

Critical Success Factors in Data Mining Projects.

The increasing awareness of data mining technology, along with the attendant increase in the capturing, warehousing, and utilization of historical data to support evidence-based decision making, is leading many organizations to recognize that the effective use of data is the key element in the next generation of client-server enterprise information technology. The concept of data mining is gaining acceptance in business as a means of seeking higher profits and lower costs. To deploy data mining projects successfully, organizations need to know the key factors for successful data mining. Implementing emerging information systems (IS) can be risky if the critical success factors (CSFs) have been researched insufficiently or documented inadequately. While numerous studies have listed the advantages and described the data mining process, there is little research on the success factors of data mining. This dissertation identifies CSFs in data mining projects. Chapter 1 introduces the history of the data mining process and states the problems, purposes, and significances of this dissertation. Chapter 2 reviews the literature, discusses general concepts of data mining and data mining project contexts, and reviews general concepts of CSF methodologies. It also describes the identification process for the various CSFs used to develop the research framework. Chapter 3 describes the research framework and methodology, detailing how the CSFs were identified and validated from more than 1,300 articles published on data mining and related topics. The validated CSFs, organized into a research framework using 7 factors, generate the research questions and hypotheses. Chapter 4 presents analysis and results, along with the chain of evidence for each research question, the quantitative instrument and survey results. In addition, it discusses how the data were collected and analyzed to answer the research questions. Chapter 5 concludes with a summary of the findings, describing assumptions and limitations and suggesting future research

The Impact Of Organisational Factors On Knowledge Sharing Performance

Facing global challenges in the knowledge economy, the competitiveness of business organisations has transformed dramatically in recent years. With the increase in the significance of knowledge sharing to organisational growth, a lot of resources have been invested to the management of knowledge via technological applications. In the same line of argument, a wide range of literature has argued for the contribution of employees in the sharing of knowledge. However, there are few literature that discussed the impact of organisational factors on the integration of business processes and knowledge sharing. Given the amount of research on the importance of knowledge management to improve business processes and organisational knowledge, it becomes imperative to develop a clear understanding of the impact of organisational factors on knowledge sharing performance. Therefore, the primary aim of this research is develop and validate a functional knowledge sharing model which can facilitate and enhance organisational performance considering the impact of organisational factors for business-knowledge implementation. A conceptual framework is built based on thorough literature review of knowledge management, organisational factors, performance and in-depth discussion with knowledge experts. The proposed conceptual framework is empirically tested adopting a quantitative method with survey data using over 300 responses from manufacturing and service industries in seven countries across three continents for a comprehensive and balanced view. The data from the survey are analysed by using integrated techniques of both Fuzzy Set Qualitative Comparative Analysis (fsQCA) and Data Envelopment Analysis (DEA). The fsQCA phase of this study discussed the comparative impact of organisational factors in the seven countries where survey data were collected and formulated the input and output variables for the measurement of knowledge sharing performance using DEA. With regard to the findings of the empirical research, three main constructs (knowledge sharing, organisational factors and performance) were successfully validated as dimensional constructs. The structural paths support conceptual framework that knowledge sharing has a positive influence on organisational competitive advantage, and organisational factors such as culture has a strong contribution to knowledge sharing performance. However, the direct impact of knowledge sharing on organisational performance is insignificant when key performance indicators are not identified. Various manufacturing and service organisations will potentially benefit from applying the results of this study to their knowledge sharing practices when seeking greater integration of multi business processes with accrued knowledge. The theoretical contribution of this study includes an integrated framework and model for knowledge transformation processes, knowledge sharing processes and knowledge sharing decision making for organisational performance.Sel