Cloud-Based Big Data Governance Frameworks for Digital Public Service Platforms
DOI:
https://doi.org/10.15680/IJCTECE.2022.0506024Keywords:
Big Data Governance, Digital Public Services, Cloud-Based Governance, Data Governance-as-a-Service, Government Data Platforms, Public Data Sharing, Smart City Data Governance, Data-Driven Policymaking, Public Sector Digital Transformation, Cloud Data Ecosystems, Data Security and Reuse, Government Data Exchange FrameworksAbstract
Governments are often viewed by citizens and businesses as the natural owners of public data that are generated or collected in their jurisdictions. It is expected that such data assets, if governed properly, can facilitate the provision of better services, enable data-driven policymaking, foster innovation, and drive new economic opportunities. However, big data present new and complicated challenges. Recent studies indicate data governance has become one of the most puzzling issues for governments pursuing digital transformation, which necessitates the establishment of a governance framework. Indeed, evidence-based guidance to inform the establishment of a big data governance framework for digital public service platforms on cloud infrastructure using a governance-as-a-service model is needed.
An analysis of the different stakeholders that contribute to the operation of digital public service platforms on a cloud infrastructure reveals that the primary goal is to establish the data governance framework. This framework aims to protect information security and ensure data can be reused and shared with low risk. Following a formal structure, four cloud-based big data governance frameworks adopted by governments are presented: the Cloud-Based Big Data Governance Framework, which targets big data ecosystems; the Cloud-Based Big Data Governance White Paper, which informs the operation of data exchanges; the Cloud-Based Smart City Data Governance Framework, which addresses the establishment of key data resources; and the Cloud-Based Data-Driven Economy Toolkit, which supports the proactive sharing of public data. Together, they form a roadmap for establishing a complete big data governance framework for digital public service platforms in a cloud environment.
References
[1] Singireddy, J. (2022). Leveraging Artificial Intelligence and Machine Learning for Enhancing Automated Financial Advisory Systems: A Study on AIDriven Personalized Financial Planning and Credit Monitoring. Mathematical Statistician and Engineering Applications, 71 (4), 16711–16728.
[2] Bores, J., Meyer, H., Underwood, E., Sirychenko, M., Langhout, W., von Döhren, P., et al. Review and synthesis of best practices in governance and land-use policies to implement TEN-N. ARPHA Preprints. https://doi.org/10.3897/arphapreprints.e139236
[3] Kolla, S. K. (2021). Architectural Frameworks for Large-Scale Electronic Health Record Data Platforms. Current Research in Public Health, 1(1), 1–19. Retrieved from https://www.scipublications.com/journal/index.php/crph/article/view/1372
[4] Akanfe, O. A. (2022). Advancing digital financial inclusion: Data privacy, regulatory compliance, and cross-country cultural values in digital payment systems use (Doctoral dissertation, The University of Texas at San Antonio).
[5] Li, H., Wei, H., Zhao, W., & Zheng, X. Research on geographic information data circulation supports the construction of digital China. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLVIII-1/W2-, 97–104.
[6] Adusupalli, B., Pandiri, L., & Singireddy, S. (2019). DevOps Enablement in Legacy Insurance Infrastructure for Agile Policy and Claims Deployment. risk, 7(12).
[7] Armbrust, M., Das, T., Davidson, A., Ghodsi, A., Or, A., Rosen, J., Stoica, I., Wendell, P., Xin, R., & Zaharia, M. (2021). Delta Lake: High-performance ACID table storage over cloud object stores. Proceedings of the VLDB Endowment, 13(12), 3411–3424.
[8] Avinash Reddy Segireddy. (2022). Terraform and Ansible in Building Resilient Cloud-Native Payment Architectures. International Journal of Intelligent Systems and Applications in Engineering, 10(3s), 444–455. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/7905.
[9] Kotlinski, M., & Calkowska, J. K. (2022). U-space and UTM deployment as an opportunity for more complex UAV operations including UAV medical transport. Journal of Intelligent & Robotic Systems, 106, 12. https://doi.org/10.1007/s10846-022-01681-6
[10] Chava, K., Chakilam, C., & Recharla, M. (2021). Machine Learning Models for Early Disease Detection: A Big Data Approach to Personalized Healthcare. International Journal of Engineering and Computer Science, 10(12), 25709–25730. https://doi.org/10.18535/ijecs.v10i12.4678
[11] Wei, H., & Zeng, Q. (2021). Research on sales forecast based on XGBoost–LSTM algorithm model. Journal of Physics: Conference Series, 1754(1). https://doi.org/10.1088/1742-6596/1754/1/012191.
[12] Sriram, H. K. (2022). Advancements in Credit Score Analytics using Deep Learning and Predictive Modeling Techniques. Available at SSRN 5255128.
[13] Bifet, A., & Gavaldà, R. (2007). Learning from time-changing data with adaptive windowing. Proceedings of the 2007 SIAM International Conference on Data Mining, 443–448.
[14] Kalisetty, S., Vankayalapati, R. K., Reddy, L., Sondinti, K., & Valiki, S. (2022). AI-Native Cloud Platforms: Redefining Scalability and Flexibility in Artificial Intelligence Workflows. Linguistic and Philosophical Investigations, 21(1), 1-15.
[15] Gondhi, P. K. FinTech cloud-based data lakes: Performance, governance, and scalability. Journal of Computer Science and Technology Studies, 7(2), 1–12.
[16] Vadisetty, R., Polamarasetti, A., Guntupalli, R., Raghunath, V., Jyothi, V. K., & Kudithipudi, K. (2021). Privacy-Preserving Gen AI in Multi-Tenant Cloud Environments. Sateesh kumar and Raghunath, Vedaprada and Jyothi, Vinaya Kumar and Kudithipudi, Karthik, Privacy-Preserving Gen AI in Multi-Tenant Cloud Environments (January 20, 2021).
[17] Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile Networks and Applications, 19(2), 171–209.
[18] Dwaraka Nath Kummari. (2022). Fiscal Policy Simulation Using AI And Big Data: Improving Government Financial Planning. Kurdish Studies, 10(2), 934–945. https://doi.org/10.53555/ks.v10i2.3855
[19] Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794.
[20] Gadi, A. L. The Role of Digital Twins in Automotive R&D for Rapid Prototyping and System Integration.
[21] Das T., Zhu, A., Li, S., Narayanamurthy, S., & Bhat, P. (2013). Distributed and fault-tolerant streaming computation in Spark. Proceedings of the ACM Symposium on Cloud Computing, 1–12.
[22] Siva Hemanth Kolla. (2022). Knowledge Retrieval Systems for Enterprise Service Environments. International Journal of Intelligent Systems and Applications in Engineering, 10(3s), 495–506. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/8037
[23] Dean, J., & Ghemawat, S. (2008). MapReduce: Simplified data processing on large clusters. Communications of the ACM, 51(1), 107–113.
[24] Paleti, S. (2022). Financial Innovation through AI and Data Engineering: Rethinking Risk and Compliance in the Banking Industry. Available at SSRN 5250726.
[25] DeCandia, G., Hastorun, D., Jampani, M., Kakulapati, G., Lakshman, A., Pilchin, A., Sivasubramanian, S., Vosshall, P., & Vogels, W. (2007). Dynamo: Amazon’s highly available key-value store. Proceedings of the 21st ACM Symposium on Operating Systems Principles, 205–220.
[26] Sriram, H. K., ADUSUPALLI, B., & Malempati, M. (2021). Revolutionizing Risk Assessment and Financial Ecosystems with Smart Automation, Secure Digital Solutions, and Advanced Analytical Frameworks.
[27] Dwork, C. (2008). Differential privacy: A survey of results. Proceedings of the 5th International Conference on Theory and Applications of Models of Computation, 1–19.
[28] Paleti, S., Singireddy, J., Dodda, A., Burugulla, J. K. R., & Challa, K. (2021). Innovative financial technologies: Strengthening compliance, secure transactions, and intelligent advisory systems through ai-driven automation and scalable data architectures. Secure Transactions, and Intelligent Advisory Systems Through AI-Driven Automation and Scalable Data Architectures (December 27, 2021).
[29] Elmagarmid, A. K., Ipeirotis, P. G., & Verykios, V. S. (2007). Duplicate record detection: A survey. IEEE Transactions on Knowledge and Data Engineering, 19(1), 1–16.
[30] Dwaraka Nath Kummari. (2022). Machine Learning Approaches to Real-Time Quality Control in Automotive Assembly Lines. Mathematical Statistician and Engineering Applications, 71(4), 16801–16820. Retrieved from https://philstat.org/index.php/MSEA/article/view/2972
[31] Fader, P. S., Hardie, B. G. S., & Lee, K. L. (2005). “Counting your customers” the easy way: An alternative to the Pareto/NBD model. Marketing Science, 24(2), 275–284.
[32] Inala, R. (2022). Engineering Data Products for Investment Analytics: The Role of Product Master Data and Scalable Big Data Solutions. International Journal of Scientific Research and Modern Technology, 155-171.
[33] Davuluri, P. N. (2020). Improving Data Quality and Lineage in Regulated Financial Data Platforms. Finance and Economics, 1(1), 1-14.
[34] Kalisetty, S., & Ganti, V. K. A. T. (2019). Transforming the Retail Landscape: Srinivas’s Vision for Integrating Advanced Technologies in Supply Chain Efficiency and Customer Experience. Online Journal of Materials Science, 1, 1254.
[35] Ghemawat, S., Gobioff, H., & Leung, S. T. (2003). The Google file system. Proceedings of the 19th ACM Symposium on Operating Systems Principles, 29–43.
[36] Pandiri, L., Singireddy, S., & Adusupalli, B. (2020). Digital Transformation of Underwriting Processes through Automation and Data Integration. Global Research Development (GRD) ISSN, 2455-5703.
[37] Yandamuri, U. S. (2021). A Comparative Study of Traditional Reporting Systems versus Real-Time Analytics Dashboards in Enterprise Operations. Universal Journal of Business and Management, 1(1), 1–13. Retrieved from https://www.scipublications.com/journal/index.php/ujbm/article/view/1357
[38] Gottimukkala, V. R. R. (2022). Licensing Innovation in the Financial Messaging Ecosystem: Business Models and Global Compliance Impact. International Journal of Scientific Research and Modern Technology, 1(12), 177-186.
[39] Berisha, B., Mëziu, E., & Shabani, I. (2022). Big data analytics in Cloud computing: an overview. Journal of Cloud Computing, 11(1). https://doi.org/10.1186/s13677-022-00301-w.
[40] Vadisetty, R., Polamarasetti, A., Guntupalli, R., Raghunath, V., Jyothi, V. K., & Kudithipudi, K. (2022). AI-Driven Cybersecurity: Enhancing Cloud Security with Machine Learning and AI Agents. Sateesh kumar and Raghunath, Vedaprada and Jyothi, Vinaya Kumar and Kudithipudi, Karthik, AI-Driven Cybersecurity: Enhancing Cloud Security with Machine Learning and AI Agents (February 07, 2022).
[41] Hellerstein, J. M., Haas, P. J., & Wang, H. J. (1997). Online aggregation. Proceedings of the 1997 ACM SIGMOD International Conference on Management of Data, 171–182.
[42] Segireddy, A. R. (2020). Cloud Migration Strategies for High-Volume Financial Messaging Systems.
[43] Hu, Y., Koren, Y., & Volinsky, C. (2008). Collaborative filtering for implicit feedback datasets. Proceedings of the 2008 IEEE International Conference on Data Mining, 263–272.
[44] Amistapuram, K. (2022). Fraud Detection and Risk Modeling in Insurance: Early Adoption of Machine Learning in Claims Processing. Available at SSRN 5741982.
[45] Davuluri, P. S. L. N. (2021). Event-Driven Compliance Systems: Modernizing Financial Crime Detection Without Machine Intelligence. Journal of International Crisis and Risk Communication Research, 339–354. https://doi.org/10.63278/jicrcr.vi.3636
[46] Meda, R. (2022). Integrating Edge AI in Smart Factories: A Case Study from the Paint Manufacturing Industry. International Journal of Science and Research (IJSR), 1473-1489.
[47] Jagadish, H. V., Gehrke, J., Labrinidis, A., Papakonstantinou, Y., Patel, J. M., Ramakrishnan, R., & Shahabi, C. (2014). Big data and its technical challenges. Communications of the ACM, 57(7), 86–94.
[48] Garapati, R. S. (2022). Web-Centric Cloud Framework for Real-Time Monitoring and Risk Prediction in Clinical Trials Using Machine Learning. Current Research in Public Health, 2, 1346.
[49] Meda, R. Enabling Sustainable Manufacturing Through AI-Optimized Supply Chains.
[50] Amistapuram, K. (2021). Digital Transformation in Insurance: Migrating Enterprise Policy Systems to .NET Core. Universal Journal of Computer Sciences and Communications, 1(1), 1–17.
[51] Kleppmann, M. (2017). Designing data-intensive applications. O’Reilly Media.
[52] Nagabhyru, K. C. (2022). Bridging Traditional ETL Pipelines with AI Enhanced Data Workflows: Foundations of Intelligent Automation in Data Engineering. Available at SSRN 5505199.
[53] Lahiri, M., & Venkatasubramanian, S. (2013). Robust record linkage. Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data, 101–112.
[54] Challa, K. (2021). Cloud Native Architecture for Scalable Fintech Applications with Real Time Payments. International Journal Of Engineering And Computer Science, 10(12).
[55] Leskovec, J., Rajaraman, A., & Ullman, J. D. (2014). Mining of massive datasets (2nd ed.). Cambridge University Press.
[56] Adusupalli, B., Singireddy, S., Sriram, H. K., Kaulwar, P. K., & Malempati, M. (2021). Revolutionizing Risk Assessment and Financial Ecosystems with Smart Automation, Secure Digital Solutions, and Advanced Analytical Frameworks. Universal Journal of Finance and Economics, 1(1), 101-122.
[57] Linden, G., Smith, B., & York, J. (2003). Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing, 7(1), 76–80.
[58] Choudhary, V., Kartik, & Bala, N. Cloud-based data lake. International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA), 1–5.
[59] Lin, J., Kolcz, A., & Szymanski, B. K. (2012). Large-scale machine learning at Twitter. Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data, 793–804.
[60] Sheelam, G. K. Power-Efficient Semiconductors for AI at the Edge: Enabling Scalable Intelligence in Wireless Systems. International Journal of Innovative Research in Electrical, Elec-tronics, Instrumentation and Control Engineering (IJIREEICE), DOI, 10.
[61] Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011). Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute.
[62] Segireddy, A. R. (2021). Containerization and Microservices in Payment Systems: A Study of Kubernetes and Docker in Financial Applications. Universal Journal of Business and Management, 1(1), 1–17.
[63] Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. Proceedings of the International Conference on Learning Representations, 1–12.
[64] Ramesh Inala. (2022). Cross-Domain MDM Integration Using AI-Driven Data Governance: A Case Study In Financial Technology Architecture. Migration Letters, 19(2), 280–304. Retrieved from https://migrationletters.com/index.php/ml/article/view/11982
[65] Montoya, D. Y., Neto, A. M., & da Silva, A. S. (2016). A survey of entity resolution in big data. Journal of Big Data, 3(1), 1–22.
[66] Annapareddy, V. N., Preethish Nandan, B., Kommaragiri, V. B., Gadi, A. L., & Kalisetty, S. (2022). Emerging Technologies in Smart Computing, Sustainable Energy, and Next-Generation Mobility: Enhancing Digital Infrastructure, Secure Networks, and Intelligent Manufacturing.
[67] Zaharia, M., Chowdhury, M., Franklin, M. J., Shenker, S., & Stoica, I. (2010). Spark: Cluster computing with working sets. Proceedings of the 2nd USENIX Conference on Hot Topics in Cloud Computing, 1–7.
[68] Challa, K. (2022). The Future of Cashless Economies Through Big Data Analytics in Payment Systems. International Journal of Scientific Research and Modern Technology, 60-70.
[69] Meda, R. (2021). Digital Infrastructure for Predictive Inventory Management in Retail Using Machine Learning. International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI, 10.
[70] Vadisetty, R., Polamarasetti, A., Guntupalli, R., Rongali, S. K., Raghunath, V., Jyothi, V. K., & Kudithipudi, K. (2021). Legal and Ethical Considerations for Hosting GenAI on the Cloud. International Journal of AI, BigData, Computational and Management Studies, 2(2), 28-34.
[71] Zhai, C., & Massung, S. (2016). Text data management and analysis: A practical introduction to information retrieval and text mining. ACM & Morgan Claypool.
[72] Davuluri, P. N. (2020). Event-Driven Architectures for Real-Time Regulatory Monitoring in Global Banking.
[73] Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T. (2017). Enriching word vectors with subword information. Transactions of the Association for Computational Linguistics, 5, 135–146.
[74] Keerthi Amistapuram , "Energy-Efficient System Design for High-Volume Insurance Applications in Cloud-Native Environments," International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering (IJIREEICE), DOI 10.17148/IJIREEICE.2020.81209
[75] Goutham Kumar Sheelam. (2022). Reconfigurable Semiconductor Architectures For AI-Enhanced Wireless Communication Networks. Kurdish Studies, 10(2), 1027–1040. https://doi.org/10.53555/ks.v10i2.3867
[76] Aitha, A. R. (2022). Cloud Native ETL Pipelines for Real Time Claims Processing in Large Scale Insurers. Available at SSRN 5532601.
[77] Gottimukkala, V. R. R. (2021). Digital Signal Processing Challenges in Financial Messaging Systems: Case Studies in High-Volume SWIFT Flows.
[78] Bhasin, H., & Bhatia, P. (2020). Clickstream data mining for web analytics and customer behavior modeling: A review. ACM Computing Surveys, 53(6), 1–34.
[79] Kolla, S. H. (2021). Rule-Based Automation for IT Service Management Workflows. Online Journal of Engineering Sciences, 1(1), 1–14. Retrieved from https://www.scipublications.com/journal/index.php/ojes/article/view/1360
[80] Davuluri, P. N. (2020). Improving Data Quality and Lineage in Regulated Financial Data Platforms. Finance and Economics, 1(1), 1-14.
[81] Abedjan, Z., Golab, L., & Naumann, F. (2016). Profiling relational data: A survey. The VLDB Journal, 24(4), 557–581.
[82] Yandamuri, U. S. (2022). Big Data Pipelines for Cross-Domain Decision Support: A Cloud-Centric Approach. International Journal of Scientific Research and Modern Technology, 1(12), 227–237. https://doi.org/10.38124/ijsrmt.v1i12.1111
[83] Nandan, B. P. (2022). AI-Powered Fault Detection In Semiconductor Fabrication: A Data-Centric Perspective.
[84] Davuluri, P. N. Event-Driven Compliance Systems: Modernizing Financial Crime Detection Without Machine Intelligence.
[85] Baesens, B., Van Vlasselaer, V., & Verbeke, W. (2021). Fraud analytics using descriptive, predictive, and social network techniques: A guide to data science for fraud detection (2nd ed.). Wiley.
[86] Avinash Reddy Aitha. (2022). Deep Neural Networks for Property Risk Prediction Leveraging Aerial and Satellite Imaging. International Journal of Communication Networks and Information Security (IJCNIS), 14(3), 1308–1318. Retrieved from https://www.ijcnis.org/index.php/ijcnis/article/view/8609
[87] Buccella, A., Cechich, A., Saurin, F., Montenegro, A., Rodríguez, A., & Muñoz, A. A context-based perspective on frost analysis in reuse-oriented big data-system developments. Information, 15(11), 661. https://doi.org/10.3390/info15110661
[88] Garapati, R. S. (2022). AI-Augmented Virtual Health Assistant: A Web-Based Solution for Personalized Medication Management and Patient Engagement. Available at SSRN 5639650.
[89] Gottimukkala, V. R. R. (2020). Energy-Efficient Design Patterns for Large-Scale Banking Applications Deployed on AWS Cloud. power, 9(12).
[90] Ahmad, M. A., Eckert, C., & Teredesai, A. (2018). Interpretable machine learning in healthcare. Proceedings of the ACM Conference on Health, Informatics, and Data Science, 1–10.
[91] Aitha, A. R. (2022). Cloud Native ETL Pipelines for Real Time Claims Processing in Large Scale Insurers. Available at SSRN 5532601.
[92] Bores, J., Meyer, H., Underwood, E., Sirychenko, M., Langhout, W., von Döhren, P., et al. Review and synthesis of best practices in governance and land-use policies to implement TEN-N. ARPHA Preprints. https://doi.org/10.3897/arphapreprints.e139236
[93] Inala, R. Advancing Group Insurance Solutions Through Ai-Enhanced Technology Architectures And Big Data Insights.
[94] Goutham Kumar Sheelam, "Semiconductor Innovation for Edge AI: Enabling Ultra-Low Latency in Next-Gen Wireless Networks," International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2022.111258
[95] Andry, J. F., Hartono, H., & Jo, J. Analysis and prediction of supermarket sales with data mining using RapidMiner. AIP Conference Proceedings, 2693(1). https://doi.org/10.1063/5.0118725.
[96] Kolla, S. K. (2021). Architectural Frameworks for Large-Scale Electronic Health Record Data Platforms. Current Research in Public Health, 1(1), 1–19. Retrieved from https://www.scipublications.com/journal/index.php/crph/article/view/1372.
[97] Kimball, R., & Ross, M. (2013). The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling. John Wiley & Sons.
[98] Kothapalli Sondinti, L. R., & Syed, S. (2022). The Impact of Instant Credit Card Issuance and Personalized Financial Solutions on Enhancing Customer Experience in the Digital Banking Era. Universal Journal of Finance and Economics, 1(1), 1223. Retrieved from https://www.scipublications.com/journal/index.php/ujfe/article/view/1223.
[99] Beyer, M. A., & Laney, D. (2012). The Importance of Big Data: A Definition. Gartner Research Report.
[100]

