Smart Connect Ecosystem Modernization through BERT Driven Deep Neural Models: A Comparative Analysis for Sustainable IT Operations and Data Governance
DOI:
https://doi.org/10.15680/IJCTECE.2024.0704004Keywords:
Smart Connect Ecosystem, BERT, Deep Neural Models, Sustainable IT Operations, Data Governance, Log Anomaly Detection, Domain Specific Models, InterpretabilityAbstract
Smart connect ecosystems—complex interlinked IT infrastructures combining applications, services, devices, and data flows—pose ever‑increasing challenges in sustainable operations, resilience, and governance. Modernizing these ecosystems often involves adopting advanced machine learning (ML) or deep neural models to enhance automation, scalability, and intelligence. This paper presents a comparative analysis of BERT‑driven deep neural models applied to ecosystem modernization, with special attention to sustainable IT operations and data governance. We explore how BERT (Bidirectional Encoder Representations from Transformers) can be used in tasks such as log anomaly detection, data flow classification, policy compliance, entity extraction for governance, and risk prediction. The study compares BERT variants, fine‑tuned BERT, domain‑specific BERT, and lightweight transformer models, in terms of accuracy, computational cost, interpretability, and governance compatibility. Using datasets drawn from real IT logs, data governance policies, configuration files, and user feedback, we evaluate model performance and sustainable operation metrics such as energy consumption, latency, robustness to drift, and transparency. Results show that domain‑adapted BERT fine‑tuned for operations tasks outperforms generic models in detection & classification tasks, but incurs higher computational overhead which may impact sustainability metrics unless optimized. Moreover, interpretable model variants or hybrid architectures help improve governance alignment, though often at modest cost in accuracy. We derive lessons for maximizing sustainability and governance compliance: model choice must balance performance with resource usage; data governance requirements (privacy, auditability, bias) must be built into training and deployment; and continuous monitoring is critical given operational drift. Overall, BERT‑driven modernization offers significant promise for smart connect ecosystems if carefully managed; this comparative study provides guidance for practitioners and researchers seeking sustainable IT operations under strong governance regimes.
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