Big Data Enables E-Government to Implement Sustainable Development
Keywords:
Artificial intelligence, Machine learning, Big data, Digital transformation, Sustainable development, E-government
Abstract
This study explores the theoretical foundations of artificial intelligence (AI) and big data, focusing on their role in the Fourth Industrial Revolution and sustainable development. Despite growing recognition of big data’s transformative potential, there is limited understanding of its specific impact on decision-making and societal transformation towards achieving sustainable development goals (SDGs). The research aims to fill this gap by analyzing how big data can support e-government initiatives and development objectives. Using a descriptive and analytical methodology, including a case study approach, the study examines the primary techniques and projects that facilitate large-scale data analysis in digital transformation. Results reveal that big data plays a critical role in monitoring progress, informing decisions, and driving social change aligned with SDGs. These findings contribute to a better understanding of big data’s value in modern governance and sustainable development efforts.
References
[1] M. A. Alqudah, L. Muradkhanli, and M. Al-Awasa, "Artificial Intelligence Applications That Support Business Organizations and E-Government in Administrative Decision," International Journal of Economics, Finance and Sustainable Development, vol. 3, no. 3, pp. 57–72, 2021.
[2] A. Korinek and J. E. Stiglitz, Artificial Intelligence and Its Implications for Income Distribution and Unemployment. Chicago, IL, USA: University of Chicago Press, 2019.
[3] M. A. Alqudah and L. Muradkhanli, "Electronic Management and Its Role in Developing the Performance of E-Government in Jordan," Electronic Research Journal of Engineering, Computer and Applied Sciences, vol. 3, pp. 65–82, 2021.
[4] B. W. Wirtz, J. C. Weyerer, and C. Geyer, "Artificial Intelligence and the Public Sector—Applications and Challenges," International Journal of Public Administration, vol. 42, no. 7, pp. 596–615, 2019.
[5] A. J. Guimarães, P. V. de Campos Souza, V. J. Silva Araújo, T. S. Rezende, and V. S. Araújo, "Pruning Fuzzy Neural Network Applied to the Construction of Expert Systems to Aid in the Diagnosis of the Treatment of Cryotherapy and Immunotherapy," Big Data and Cognitive Computing, vol. 3, no. 2, p. 22, 2019.
[6] J. Burrell, "How the Machine ‘Thinks’: Understanding Opacity in Machine Learning Algorithms," Big Data & Society, vol. 3, no. 1, p. 2053951715622512, 2016.
[7] A. A. Salamah et al., "Customer Retention Through Service Quality and Satisfaction: Using Hybrid SEM-Neural Network Analysis Approach," Heliyon, vol. 8, no. 9, p. e10570, 2022.
[8] M. A. Alqudah, "Towards the Governance of Government Data Using Artificial Intelligence," Social Science Research Network, 2021. [Online]. Available: https://ssrn.com/abstract=3992303
[9] M. A. Alqudah, L. Muradkhanli, Z. Muradkhanli, and A. A. Salameh, "Using Artificial Intelligence Applications for E-Government Services as Iris Recognition," in 2023 IEEE 17th International Conference on Application of Information and Communication Technologies (AICT), 2023, pp. 1–7. doi: 10.1109/AICT59525.2023.10313183.
[10] S. K. Punia, M. Kumar, T. Stephan, G. G. Deverajan, and R. Patan, "Performance Analysis of Machine Learning Algorithms for Big Data Classification: ML and AI-Based Algorithms for Big Data Analysis," International Journal of E-Health and Medical Communications, vol. 12, no. 4, pp. 60–75, 2021.
[11] P. Zerbino, D. Aloini, R. Dulmin, and V. Mininno, "Big Data-Enabled Customer Relationship Management: A Holistic Approach," Information Processing & Management, vol. 54, no. 5, pp. 818–846, 2018.
[12] C. Lin, P. Wang, H. Song, Y. Zhou, Q. Liu, and G. Wu, "A Differential Privacy Protection Scheme for Sensitive Big Data in Body Sensor Networks," Annales des Télécommunications, vol. 71, no. 9–10, pp. 465–475, 2016.
[13] B. D. Mittelstadt, P. Allo, M. Taddeo, S. Wachter, and L. Floridi, "The Ethics of Algorithms: Mapping the Debate," Big Data & Society, vol. 3, no. 2, p. 2053951716679679, 2016.
[14] Y. Zhuang, F. Wu, C. Chen, and Y. Pan, "Challenges and Opportunities: From Big Data to Knowledge in AI 2.0," Frontiers of Information Technology & Electronic Engineering, vol. 18, pp. 3–14, 2017.
[15] K. Veeramachaneni, I. Arnaldo, V. Korrapati, C. Bassias, and K. Li, "AI²: Training a Big Data Machine to Defend," in 2016 IEEE 2nd International Conference on Big Data Security on Cloud (BigDataSecurity), IEEE International Conference on High Performance and Smart Computing (HPSC), and IEEE International Conference on Intelligent Data and Security (IDS), 2016, pp. 49–54.
[16] M. E. Jennex, "Big Data, the Internet of Things, and the Revised Knowledge Pyramid," ACM SIGMIS Database, vol. 48, no. 4, pp. 69–79, 2017.
[17] J. Kaivo-Oja, P. Virtanen, H. Jalonen, and J. Stenvall, "The Effects of the Internet of Things and Big Data on Organizations and Their Knowledge Management Practices," in International Conference on Knowledge Management in Organizations, 2015, pp. 495–513.
[18] H. Felzmann, E. F. Villaronga, C. Lutz, and A. Tamò-Larrieux, "Transparency You Can Trust: Transparency Requirements for Artificial Intelligence Between Legal Norms and Contextual Concerns," Big Data & Society, vol. 6, no. 1, p. 2053951719860542, 2019.
[19] S. O. Salinas and A. C. N. Lemus, "Data Warehouse and Big Data Integration," International Journal of Computer Science and Information Technology, vol. 9, no. 2, pp. 1–17, 2017.
[20] M. do R. M. Bernardo, "Smart City Governance: From E-Government to Smart Governance," in Smart Cities and Smart Spaces: Concepts, Methodologies, Tools, and Applications, IGI Global, 2019, pp. 196–232.
[21] J. Snow, "This AI Traffic System in Pittsburgh Has Reduced Travel Time by 25%," Smart Cities Dive, 2017. [Online]. Available: https://www.smartcitiesdive.com
[22] A. H. Elsheikh, S. W. Sharshir, M. Abd Elaziz, A. E. Kabeel, W. Guilan, and Z. Haiou, "Modeling of Solar Energy Systems Using Artificial Neural Network: A Comprehensive Review," Solar Energy, vol. 180, pp. 622–639, 2019.
[23] N. Mohan, K. D. V. Prasad, K. Soujanya, D. C. Dobhal, M. Ali, and M. A. Tripathi, "An Adaptive Service-Oriented Business Management Pattern Based on Machine Learning Rule ML," in 2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), 2023, pp. 1672–1676.
[2] A. Korinek and J. E. Stiglitz, Artificial Intelligence and Its Implications for Income Distribution and Unemployment. Chicago, IL, USA: University of Chicago Press, 2019.
[3] M. A. Alqudah and L. Muradkhanli, "Electronic Management and Its Role in Developing the Performance of E-Government in Jordan," Electronic Research Journal of Engineering, Computer and Applied Sciences, vol. 3, pp. 65–82, 2021.
[4] B. W. Wirtz, J. C. Weyerer, and C. Geyer, "Artificial Intelligence and the Public Sector—Applications and Challenges," International Journal of Public Administration, vol. 42, no. 7, pp. 596–615, 2019.
[5] A. J. Guimarães, P. V. de Campos Souza, V. J. Silva Araújo, T. S. Rezende, and V. S. Araújo, "Pruning Fuzzy Neural Network Applied to the Construction of Expert Systems to Aid in the Diagnosis of the Treatment of Cryotherapy and Immunotherapy," Big Data and Cognitive Computing, vol. 3, no. 2, p. 22, 2019.
[6] J. Burrell, "How the Machine ‘Thinks’: Understanding Opacity in Machine Learning Algorithms," Big Data & Society, vol. 3, no. 1, p. 2053951715622512, 2016.
[7] A. A. Salamah et al., "Customer Retention Through Service Quality and Satisfaction: Using Hybrid SEM-Neural Network Analysis Approach," Heliyon, vol. 8, no. 9, p. e10570, 2022.
[8] M. A. Alqudah, "Towards the Governance of Government Data Using Artificial Intelligence," Social Science Research Network, 2021. [Online]. Available: https://ssrn.com/abstract=3992303
[9] M. A. Alqudah, L. Muradkhanli, Z. Muradkhanli, and A. A. Salameh, "Using Artificial Intelligence Applications for E-Government Services as Iris Recognition," in 2023 IEEE 17th International Conference on Application of Information and Communication Technologies (AICT), 2023, pp. 1–7. doi: 10.1109/AICT59525.2023.10313183.
[10] S. K. Punia, M. Kumar, T. Stephan, G. G. Deverajan, and R. Patan, "Performance Analysis of Machine Learning Algorithms for Big Data Classification: ML and AI-Based Algorithms for Big Data Analysis," International Journal of E-Health and Medical Communications, vol. 12, no. 4, pp. 60–75, 2021.
[11] P. Zerbino, D. Aloini, R. Dulmin, and V. Mininno, "Big Data-Enabled Customer Relationship Management: A Holistic Approach," Information Processing & Management, vol. 54, no. 5, pp. 818–846, 2018.
[12] C. Lin, P. Wang, H. Song, Y. Zhou, Q. Liu, and G. Wu, "A Differential Privacy Protection Scheme for Sensitive Big Data in Body Sensor Networks," Annales des Télécommunications, vol. 71, no. 9–10, pp. 465–475, 2016.
[13] B. D. Mittelstadt, P. Allo, M. Taddeo, S. Wachter, and L. Floridi, "The Ethics of Algorithms: Mapping the Debate," Big Data & Society, vol. 3, no. 2, p. 2053951716679679, 2016.
[14] Y. Zhuang, F. Wu, C. Chen, and Y. Pan, "Challenges and Opportunities: From Big Data to Knowledge in AI 2.0," Frontiers of Information Technology & Electronic Engineering, vol. 18, pp. 3–14, 2017.
[15] K. Veeramachaneni, I. Arnaldo, V. Korrapati, C. Bassias, and K. Li, "AI²: Training a Big Data Machine to Defend," in 2016 IEEE 2nd International Conference on Big Data Security on Cloud (BigDataSecurity), IEEE International Conference on High Performance and Smart Computing (HPSC), and IEEE International Conference on Intelligent Data and Security (IDS), 2016, pp. 49–54.
[16] M. E. Jennex, "Big Data, the Internet of Things, and the Revised Knowledge Pyramid," ACM SIGMIS Database, vol. 48, no. 4, pp. 69–79, 2017.
[17] J. Kaivo-Oja, P. Virtanen, H. Jalonen, and J. Stenvall, "The Effects of the Internet of Things and Big Data on Organizations and Their Knowledge Management Practices," in International Conference on Knowledge Management in Organizations, 2015, pp. 495–513.
[18] H. Felzmann, E. F. Villaronga, C. Lutz, and A. Tamò-Larrieux, "Transparency You Can Trust: Transparency Requirements for Artificial Intelligence Between Legal Norms and Contextual Concerns," Big Data & Society, vol. 6, no. 1, p. 2053951719860542, 2019.
[19] S. O. Salinas and A. C. N. Lemus, "Data Warehouse and Big Data Integration," International Journal of Computer Science and Information Technology, vol. 9, no. 2, pp. 1–17, 2017.
[20] M. do R. M. Bernardo, "Smart City Governance: From E-Government to Smart Governance," in Smart Cities and Smart Spaces: Concepts, Methodologies, Tools, and Applications, IGI Global, 2019, pp. 196–232.
[21] J. Snow, "This AI Traffic System in Pittsburgh Has Reduced Travel Time by 25%," Smart Cities Dive, 2017. [Online]. Available: https://www.smartcitiesdive.com
[22] A. H. Elsheikh, S. W. Sharshir, M. Abd Elaziz, A. E. Kabeel, W. Guilan, and Z. Haiou, "Modeling of Solar Energy Systems Using Artificial Neural Network: A Comprehensive Review," Solar Energy, vol. 180, pp. 622–639, 2019.
[23] N. Mohan, K. D. V. Prasad, K. Soujanya, D. C. Dobhal, M. Ali, and M. A. Tripathi, "An Adaptive Service-Oriented Business Management Pattern Based on Machine Learning Rule ML," in 2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), 2023, pp. 1672–1676.
Published
2024-09-26
How to Cite
Al Qudah, M. A., Muradkhanli, L., Weshah, H. A., Hamdan, H., & Abuhashish, M. M. (2024). Big Data Enables E-Government to Implement Sustainable Development. International Journal on Orange Technologies, 6(4), 40-52. https://doi.org/10.31149/ijot.v6i4.5323
Section
Articles
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