Integrating Multi-Modal Knowledge Sources: A Comprehensive Tool for AS/400 Legacy System Knowledge Transition and Business Process Documentation

Authors

  • Dr. Ranjith Gopalan Principal Consultant, Cognizant Technologies Corp, Charlotte, NC, United states. Author
  • Ganesh Viswanathan Department of Data science and Business Analytics, UNC Charlotte, Charlotte, NC, United states.0 Author
  • Debashish Roy Senior Director, Cognizant Technologies Corp, Charlotte, NC, United states. Author

DOI:

https://doi.org/10.63282/3050-9246/ICRTCSIT-129

Keywords:

AS/400, Legacy Systems, Knowledge Transition, COBOL, Business Process Documentation, Multi-modal Learning, Expert Knowledge Capture

Abstract

The paper discusses the critical challenge of knowledge transition in legacy AS/400 systems, particularly in insurance and financial services where business-critical processes rely on decades-old COBOL applications. This research presents a comprehensive tool that integrates multi-modal knowledge sources including expert documentation (PDF and DOC formats), video recordings, and COBOL source code to facilitate knowledge transfer for core business transactions such as policy creation, endorsement, renewal, billing, and claims processing. The paper explores the development of an intelligent knowledge management system that leverages natural language processing, video analysis, and code documentation techniques to create a unified knowledge repository [1]. The system addresses the urgent need for knowledge preservation as AS/400 experts approach retirement while ensuring business continuity for mission-critical legacy applications [7]

Downloads

Download data is not yet available.

References

[1] Anderson, M., & Thompson, R. (2023). Legacy System Knowledge Management: Challenges and Solutions in Enterprise Environments. Journal of Legacy Computing, 15(3), 45-62.

[2] Brown, K., Davis, L., & Wilson, P. (2022). Multi-Modal Learning Approaches for Technical Knowledge Transfer. International Conference on Knowledge Management, 234-248.

[3] Chen, S., Martinez, A., & Johnson, D. (2024). COBOL Code Analysis for Business Rule Extraction: A Systematic Approach. Software Maintenance and Evolution, 12(2), 78-95.

[4] Edwards, J., & Parker, M. (2023). Video-Based Knowledge Capture in Technical Training: Effectiveness and Best Practices. Educational Technology Research, 28(4), 112-129.

[5] Foster, T., Green, B., & Lee, C. (2022). Knowledge Graphs for Legacy System Documentation: A Case Study Approach. Enterprise Architecture Quarterly, 9(1), 23-39.

[6] Garcia, R., & Smith, H. (2024). AS/400 to IBM i Evolution: Maintaining Business Continuity Through Knowledge Preservation. Mainframe Computing Today, 31(2), 56-73.

[7] Hughes, L., Clark, N., & Adams, K. (2023). Natural Language Processing for Technical Documentation Analysis. Computational Linguistics Applications, 19(3), 145-162.

[8] IBM Corporation. (2023). IBM i Modernization Strategies: Preserving Investment While Enabling Growth. IBM Systems Technical Report, TR-2023-15.

[9] Jackson, P., & Taylor, S. (2022). Expert Knowledge Elicitation Techniques for Legacy System Maintenance. Software Engineering Practices, 14(4), 89-106.

[10] Kumar, V., & Roberts, A. (2024). Multi-Modal Information Integration: Challenges and Opportunities in Enterprise Knowledge Management. Information Systems Research, 35(1), 201-218.

[11] Bhagath Chandra Chowdari Marella, “From Silos to Synergy: Delivering Unified Data Insights across Disparate Business Units”, International Journal of Innovative Research in Computer and Communication Engineering, vol.12, no.11, pp. 11993-12003, 2024.

[12] Kommineni, M. "Explore Knowledge Representation, Reasoning, and Planning Techniques for Building Robust and Efficient Intelligent Systems." International Journal of Inventions in Engineering & Science Technology 7.2 (2021): 105- 114.

[13] Shrikaa Jadiga, "Understanding the Role of AI in Personalized Recommendation Systems, Applications, Concepts, and Algorithms," International Journal of Computer Trends and Technology (IJCTT), vol. 73, no. 1, pp. 106-118, 2025. Crossref, https://doi.org/10.14445/22312803/ IJCTT-V73I1P113

[14] Sharma, V. K. (2025). Cloud Computing & IoT: 5G Focused IoT with Cloud Solutions. International Journal of AI, BigData, Computational and Management Studies, 6(3), 21-25. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V6I3P103

[15] Kanji, R. K., & Subbiah, M. K. (2024). Developing Ethical and Compliant Data Governance Frameworks for AI-Driven Data Platforms. Available at SSRN 5507919.

[16] Arpit Garg, "How Natural Language Processing Framework Automate Business Requirement Elicitation," International Journal of Computer Trends and Technology (IJCTT), vol. 73, no. 5, pp. 47-50, 2025. Crossref, https://doi.org/10.14445/22312803/IJCTT-V73I5P107

[17] Reddy, R. P. (2025). Zero Trust Architectures in Modern Enterprises: Principles, Implementation Challenges, and Best Practices. International Journal of Computer Trends and Technology, 73(6), 48-57.

[18] Vijay Kumar Kasuba, (2025). Investigating the Issues and Challenges of Remote Working on Project Management: Case Studies from India. International Journal of Computer Trends and Technology(IJCTT), Volume 73 Issue 5, 64-69, May 2025

[19] Pugazhenthi, V. J., Pandy, G., Jeyarajan, B., & Murugan, A. (2025, March). AI-Driven Voice Inputs for Speech Engine Testing in Conversational Systems. In SoutheastCon 2025 (pp. 700-706). IEEE.

[20] Gunda, S. K., Yalamati, S., Gudi, S. R., Manga, I., & Aleti, A. K. (2025). Scalable and adaptive machine learning models for early software fault prediction in agile development: Enhancing software reliability and sprint planning efficiency. International Journal of Applied Mathematics, 38(2s). https://doi.org/10.12732/ijam.v38i2s.74

[21] Settibathini, V. S., Virmani, A., Kuppam, M., S., N., Manikandan, S., & C., E. (2024). Shedding Light on Dataset Influence for More Transparent Machine Learning. In P. Paramasivan, S. Rajest, K. Chinnusamy, R. Regin, & F. John Joseph (Eds.), Explainable AI Applications for Human Behavior Analysis (pp. 33-48). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-1355-8.ch003

[22] M. Kommineni, S. Panyaram, S. Banala, G. C. Vegineni, M. Hullurappa and S. K. Sehrawat, "Optimizing Processes and Insights: the Role of Ai Architecture in Corporate Data Management," 2025 International Conference on Data Science and Business Systems (ICDSBS), Chennai, India, 2025, pp. 1-7, doi: 10.1109/ICDSBS63635.2025.11031505.

[23] Kotte Kulasekhara Reddy. 2025. Smart Finance Harnessing AI and ERP for Real- Time Insights and Predictive Analytics. International Conference on AI in Daily Life Innovations and Applications.

[24] B. C. C. Marella, G. C. Vegineni, S. Addanki, E. Ellahi, A. K. K and R. Mandal, "A Comparative Analysis of Artificial Intelligence and Business Intelligence Using Big Data Analytics," 2025 First International Conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), Bhimtal, Nainital, India, 2025, pp. 1139-1144, doi: 10.1109/CE2CT64011.2025.10939850.

Published

2025-10-10

How to Cite

1.
Gopalan R, Viswanathan G, Roy D. Integrating Multi-Modal Knowledge Sources: A Comprehensive Tool for AS/400 Legacy System Knowledge Transition and Business Process Documentation. IJETCSIT [Internet]. 2025 Oct. 10 [cited 2025 Nov. 7];:209-1. Available from: https://www.ijetcsit.org/index.php/ijetcsit/article/view/449

Similar Articles

1-10 of 335

You may also start an advanced similarity search for this article.