Citation:

Amit Kumar, 2022. "Advanced AI Algorithms for Data Stewardship: Implementing Java/J2EE in Modern MDM Systems", ESP Journal of Engineering & Technology Advancements 2(3): 102-115.

Abstract:

MDM is crucial for companies to achieve and sustain standard and reliable data quality in multiple domains. The bonding of AI algorithms in MDM boosts the execution of data stewardship and governance. This paper will briefly provide insight into the applicability of some advanced AI techniques likely to be integrated into the current MDM solutions and give specific details on Java/J2EE as a highly reliable backend environment. The paper surveys the literature and provides approaches to developing AI-aided MDM systems using Java/J2EE, focusing on experimental findings and the benefits of integrating AI and enterprise approaches. The findings of this research provide benefits such as improving the quality of data, making better decisions, and enhancing compliance.

References:

[1] Abrams, M., Abrams, J., Cullen, P., & Goldstein, L. (2019). Artificial intelligence, ethics, and enhanced data stewardship. IEEE Security & Privacy, 17(2), 17-30.

[2] Spruit, M., & Pietzka, K. (2015). MD3M: The master data management maturity model. Computers in Human Behavior, 51, 1068-1076.

[3] Hikmawati, S., Santosa, P. I., & Hidayah, I. (2021). Improving Data Quality and Data Governance Using Master Data Management: A Review. IJITEE (International Journal of Information Technology and Electrical Engineering), 5(3), 90-95.

[4] Hechler, E., Oberhofer, M., Schaeck, T., Hechler, E., Oberhofer, M., & Schaeck, T. (2020). Applying AI to master data management. Deploying AI in the Enterprise: IT Approaches for Design, DevOps, Governance, Change Management, Blockchain, and Quantum Computing, 213-234.

[5] Tadi, V. (2020). Optimizing Data Governance: Enhancing Quality through AI-Integrated Master Data Management Across Industries. North American Journal of Engineering Research, 1(3).

[6] Pansara, R. R. (2022). Edge Computing in Master Data Management: Enhancing Data Processing at the Source. International Transactions in Artificial Intelligence, 6(6), 1-11.

[7] Ge, Z. (2022). Artificial Intelligence and Machine Learning in Data Management. Future And Fintech, The: Abcdi And Beyond, 281.

[8] Choudhuri, K. B. R., & Mangrulkar, R. S. (2021). Data Acquisition and Preparation for Artificial Intelligence and Machine Learning Applications. In Design of Intelligent Applications Using Machine Learning and Deep Learning Techniques (pp. 1-11). Chapman and Hall/CRC.

[9] Fontana, V., Blasco, J. M. D., Cavallini, A., Lorusso, N., Scremin, A., & Romeo, A. (2020, June). Artificial intelligence technologies for Maritime Surveillance applications. In 2020 21st IEEE International Conference on Mobile Data Management (MDM) (pp. 299-303). IEEE.

[10] Giavina-Bianchi, M., de Sousa, R. M., Paciello, V. Z. D. A., Vitor, W. G., Okita, A. L., Prôa, R., ... & Machado, B. S. (2021). Implementation of artificial intelligence algorithms for melanoma screening in a primary care setting. PLoS One, 16(9), e0257006.

[11] Shaw, J., Rudzicz, F., Jamieson, T., & Goldfarb, A. (2019). Artificial intelligence and the implementation challenge. Journal of medical Internet research, 21(7), e13659.

[12] Kallem, S. R. (2012). Artificial intelligence algorithms. IOSR Journal of Computer Engineering, 6(3), 1-8.

[13] Shaykhian, G. A., Khairi, M. A., & Ziade, J. (2016, June). Architectural Evaluation of Master Data Management (MDM): Literature Review. In 2016 ASEE Annual Conference & Exposition.

[14] Broemmer, D. (2003). J2EE best practices: Java design patterns, automation, and performance (Vol. 8). John Wiley & Sons.

[15] Gupta, A. Machine Learning Applications in Mobile Device Management (MDM). J Artif Intell Mach Learn & Data Sci 2022, 1(1), 648-654.

[16] Lai, R. (2004). J2EE platform web services. Prentice Hall Professional.

[17] Sharma, R., Stearns, B., & Ng, T. (2001). J2EE Connector architecture and enterprise application integration. Addison-Wesley Professional.

[18] Rana, K. J. (2010). Migration Of J2EE/Java Application to SAP NetWeaver Java Development Infrastructure (SAP NWDI).

[19] Avhad, A. R. (2016). Investigation of IoT (Internet of Things) approach to data exchange and visualization in context of MES & ERP integration (Doctoral dissertation, Kauno technologijos universitetas.).

Keywords:

AI Algorithms, Data Stewardship, MDM Systems, Java/J2EE, Data governance, Machine learning, Data integration.