Article
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Proposing Machine Learning Models Suitable for Predicting Open Data Utilization
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Version 1
: Received: 19 June 2024 / Approved: 19 June 2024 / Online: 19 June 2024 (07:36:26 CEST)
A peer-reviewed article of this Preprint also exists.
Jeong, J.; Cho, K. Proposing Machine Learning Models Suitable for Predicting Open Data Utilization. Sustainability 2024, 16, 5880. Jeong, J.; Cho, K. Proposing Machine Learning Models Suitable for Predicting Open Data Utilization. Sustainability 2024, 16, 5880.
Abstract
As the digital transformation accelerates in our society, open data is being increasingly recognized as a key resource for digital innovation in the public sector. This study explores the following two research questions: 1) Can a machine learning approach be appropriately used for measuring and evaluating open data utilization? 2) Should different machine learning models be applied for measuring open data utilization depending on open data attributes (field and usage type)? This study used single-model (Random Forest, XGBoost, LightGBM, CatBoost) and multi-model (Stacking Ensemble) machine learning methods. A key finding is that the best-performing models differed depending on open data attributes (field and type of use). The applicability of the machine learning approach for measuring and evaluating open data utilization in advance was also confirmed. This study contributes to open data utilization and to the application of its intrinsic value to society.
Keywords
open data; open government data; open data utilization
Subject
Business, Economics and Management, Business and Management
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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