Abstract
Business Intelligence (BI) refers to skills, processes, technologies, applications and practices used to support decision making. BI technologies provide historical, current, and predictive views of business operations which are normally used to analyze business data. Online Analytical Processing (OLAP) is one of the common BI approaches in quickly answering multidimensional analytical queries for analytical purpose. In this paper, we have proposed OLAP technique to be implemented in Academic area. Through this technique, UniSZA students’ academic pattern behaviors can be analyzed. A set of data from students’ examination results in relational DB is extracted into multi-dimensional model to support OLAP query processing. The results are grouped into several subject areas. Then, the analysis to recognize students’ academic pattern behaviors is conducted. From the analysis, the groups of students who have the excellent skills or vice versa can be identified. It also optimizes the time dimension to perform current and historical data analysis. The weaknesses and strengths of the student can also be obtained. Finally, students’ future potential areas can be predicted for the next level of educations.
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Abdul Aziz, A., Wan Idris, W.M.R. (2011). Online Analytical Processing Technique in Personalizing Student Academic Pattern Behavior for UniSZA Students’ Results. In: Ariwa, E., El-Qawasmeh, E. (eds) Digital Enterprise and Information Systems. DEIS 2011. Communications in Computer and Information Science, vol 194. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22603-8_51
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DOI: https://doi.org/10.1007/978-3-642-22603-8_51
Publisher Name: Springer, Berlin, Heidelberg
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