Azwa Abdul Aziz

DQAS implementation to support Base Analysis techniques in Data Quality Life Cycle

Good business decision making depends on how good information is provided. Due to this factor, the quality of data provided by transactions systems database is really important to organizations in order to produce the best solution for their company to move forward. Data Quality issues have become major problems in most enterprise systems where in some cases forced companies to stop their operations and in other, create chaos in life environment. Base Analysis technique is part of analyses which are introduced in our previous research in Data Quality Life Cycle. Base Analysis technique is used to profile heterogeneous data in a structured approach, with the intention to determine abnormal data. The technique contains three levels of analyses; Top Level Analysis, Middle Level Analysis and Low Level Analysis. On the other hand, Data Quality Analysis System is a tool developed using open source technologies that are connected to commercial databases to support Base Analysis and implemented in three-tier architecture. This paper describes the implementation of the system to perform data quality analysis using Human Resources data that reside inOracle database platform.

Online Analytical Processing Technique in Personalizing Student Academic Pattern Behavior for UniSZA Students’ Results

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.