RSAPP, um algoritmo baseado em rough sets para auxílio ao processo de descoberta de conhecimento em banco de dados

Abstract

Techniques of Business Intelligence (BI) became one of the main allies of organizations in tasks of transforming data into knowledge, supporting the middle and upper management levels in decision making. BI tools in their composition are based on techniques of knowledge management, such as Data Warehouse (DW), OLAP (Online Analytical Processing), Data Mining (DM), among others. In this context, it is observed that in many case, DM projects become unfeasible by some factors, such as project costs, duration and specially the uncertainty in obtaining results that return the investment spent on the project. This work seeks to minimize these factors through a diagnosis on data, by an algorithm based on Rough Sets Theory. The algorithm, named Rough Set App (RSAPP) aims to create a diagnosis on data persisted in DW, in order to map which attributes have the greatest potential for generating more accurate mining models and more interesting results. Thus, it is expected that the diagnosis generated by RSAPP can complement the KDD (Knowledge Discovery in Database) process, reducing the time spent on activities of understanding and reducing data dimensionality. This work presents a detailed description about the implemented algorithm, as well as the report of the executed tests. At the end there is an empirical analysis of the results, in order to estimate the effectiveness of the proposed algorithm.