KDD Processes in Non-Relational Data: The case of the MineraMongo Tool

Abstract

The process of Knowledge Discovery in Databases, or KDD for short, have been intensively used in tasks focused on searching useful information based on data. The reason is that such data is generated in significant volume, high speed and with a large variety, which makes it require accurate, efficient and scalable methods to handle them. Due to this scenario, several tools and methodologies have been developed to enable data analysis and mining processes. However, there is still a lack of KDD methods based on non-relational data. Considering this scenario, this work aims to present a tool capable of providing selection, preprocessing, transformation, mining and data analysis through databases. Our results consist in a case study, which is used to demonstrate the potential of the MineraMongo tool. This research contributes in a framework of analytical and computational techniques.