Building a Data Warehouse for National Social Security Fund of the Republic of Tunisia

TL;DR

This paper describes the development of a comprehensive data warehouse for Tunisia's National Social Security Fund, enabling better decision-making through data cleaning, integration, and analysis using Oracle tools.

Contribution

It introduces a tailored data warehouse solution for NSSF, utilizing Oracle OLAP and KDD processes, with plans for advanced data mining techniques.

Findings

Successful implementation of data warehouse for NSSF

Enhanced decision-making capabilities through OLAP and reporting tools

Encouragement for future multidimensional data mining applications

Abstract

The amounts of data available to decision makers are increasingly important, given the network availability, low cost storage and diversity of applications. To maximize the potential of these data within the National Social Security Fund (NSSF) in Tunisia, we have built a data warehouse as a multidimensional database, cleaned, homogenized, historicized and consolidated. We used Oracle Warehouse Builder to extract, transform and load the source data into the Data Warehouse, by applying the KDD process. We have implemented the Data Warehouse as an Oracle OLAP. The knowledge extraction has been performed using the Oracle Discoverer tool. This allowed users to take maximum advantage of knowledge as a regular report or as ad hoc queries. We started by implementing the main topic for this public institution, accounting for the movements of insured persons. The great success that has followed the completion of this work has encouraged the NSSF to complete the achievement of other topics of interest within the NSSF. We suggest in the near future to use Multidimensional Data Mining to extract hidden knowledge and that are not predictable by the OLAP.