The Use of Data Mining in Public Budgeting: A Systematic Literature Mapping

Abstract

Planning and allocating public resources is essential because resources are always limited and must be sufficient to meet a country's needs. Therefore, it is necessary to define how resources are distributed based on the amount collected, directly affecting society in the most diverse areas, such as education and health. Owing to advances in artificial intelligence in recent years, studies have been conducted to explore and propose intelligent solutions that enable the most diverse analyses in this critical area. Among these, data mining has emerged as a viable solution. Generally, data mining consists of three major steps: pre-processing, pattern extraction, and post-processing. Thus, to understand how data mining has been used in the most diverse subjects related to public planning and budgeting, this study presents systematic literature mapping. The aims were (i) to provide an overview of the aspects related to the data mining steps in the presented context and, (ii) to identify gaps that can be addressed and/or explored. The results are presented and discussed throughout this paper based on 30 papers selected over 10 years (from 2014 to 2023), with the potential to significantly impact future research and practice in public planning and data mining.