Saturday , March 2 2024

Upgrading the Business Intelligence System by Implementing the Decision Tree Model
in the R Software Package

University of Kragujevac, Faculty of Tehnical Sciences Cacak,
Svetog Save 65, Cacak, Serbia (*Corresponding author),

Abstract: Business Intelligence is the key and basis of a modern understanding of management. The organizations that are able to manage their data resources, information and knowledge are more successful and competitive than the others. These organizations, as a rule, rely on modern strategic management concepts and develop business intelligence systems. Certain changes have taken place in the world of research in recent years, and open-source software packages are now most commonly used for statistical surveys. The R package in particular is gradually becoming the dominant platform for companies that are unable to spend too much on software. The R package has gathered a huge community of enthusiasts who are constantly building upon the latest developments in statistics and data mining at no cost to the end user. The main problem related to this approach lies in the data sources, because the R package is not able to store large amounts of data, as it was designed for data analysis and the respective data is mostly stored on other platforms. The aim of this paper is to find a common solution and correlation between BI, which is based on data warehouses and programs for statistical data processing, and the open-source R package on the other hand in order to obtain timely information in the shortest possible time, according to different criteria, by applying the decision tree model. Decision makers will be able to use the proposed solution to make decisions with confidence even if they don’t possess the pertinent IT knowledge.

Keywords: R, Business Intelligence, Decision tree, Machine learning.


Jordan ATANASIJEVIC, Danijela MILOSEVICUpgrading the Business Intelligence System by Implementing the Decision Tree Model in the R Software Package, Studies in Informatics and Control, ISSN 1220-1766, vol. 29(2), pp. 243-254, 2020.