A Proposed Quality Assurance Intelligent Model for Higher Education Institutions in Saudi Arabia
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Abstract
Recent growth and demands for dealing with increasing complexity in management, evaluation, and accreditation of higher educational institutions have led keynote academic institutions and higher education authorities to adopt and try nonconventional solutions known to business firms to account for massive data management. The development in new practices and merging technology for analytics and information management have offered different solutions such as data warehousing, big data, and business intelligence. Such solutions are gradually being installed in a number of renown universities. Due to the difference between the two firms (higher education and business industry) in nature and aims, tailor-made solutions are needed.
This paper shares authors' experience in designing and implementing an educational information system in the College of Computers and Information systems at King Saud University, Saudi Arabia. The paper also highlights differences between educational intelligence and business intelligence systems. Higher education implementation aspects ensuring suitable data query service to ease the running of high educational institutions are discussed and recognized.
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References
2. Devlin, B.: Data warehouse: from architecture to implementation; Reading 1997.
3. Kimball, R.; Reeves, L.; Ross, M.; Thornthwaite, W.: The data warehouse lifecycle toolkit - Expert methods for designing, developing and deploying data warehouses; New York 1998.
4. Klesse, M.; Winter, R: Organizational Forms of Data Warehousing: An Explorative AnalysisProceedings of the 40th Hawaii International Conference on System Sciences "“ 2007.
5. Watson, H. J; Fullerb, C.; and Ariyachandraa, T.: Data warehouse governance: best practices at Blue Cross and Blue Shield of North Carolina. Decision Support Systems 38 (2004) 435"“ 450.
6. McKendrick, J.: Oracle IOUG group: A new dimension to data warehousing: 2011 IOUG data warehousing survey. Unisphere Research
7. Madsen, M: Technology White Paper. Cloud Computing Models for Data Warehousing Third Nature Inc., 2012.