Cognitive and Ethical AI Architecture for Business Rule Automation in SAP HANA: Enhancing Software Maintenance through Software Defined and Sensor Driven Networks
DOI:
https://doi.org/10.15680/IJCTECE.2023.0605007Keywords:
Cognitive AI, Ethical AI, Business Rule Automation, SAP HANA, Software Defined Networking (SDN), Sensor Driven Networks, Predictive Maintenance, Governance, Rule Engine, Human in the LoopAbstract
In contemporary enterprise IT environments, systems such as SAP HANA increasingly underpin mission‑critical business operations, demanding high levels of reliability, governance, and agility. This paper proposes a cognitive and ethical‑AI architecture for business‑rule automation within SAP HANA landscapes, augmented by sensor‑driven telemetry and software‑defined network infrastructures. The architecture comprises three key layers: (1) a sensor/telemetry ingestion layer, capturing infrastructure, network (via software‑defined networking) and application metrics; (2) a cognitive business‑rule automation layer, integrating rule‑engines and AI/ML components that analyse telemetry, suggest or enact rule changes, and initiate maintenance workflows in SAP HANA; and (3) an ethical governance layer, embedding transparency, human‑in‑the‑loop oversight, auditability, fairness, and accountability. We present a design‑science methodology: conceptual modelling, prototype implementation in a simulated SAP HANA cloud sandbox, and evaluation via key metrics (rule‑execution latency, mean‑time‑to‑repair, human‑override rate, governance overhead). Preliminary findings suggest that the integrated architecture can reduce maintenance latency and proactively maintain system health while preserving ethical oversight. Advantages include improved responsiveness, context‑aware automation and governance alignment; disadvantages include increased architectural complexity, reliance on sensor data quality, and additional governance latency. The contribution is a unified approach bridging business‑rule automation, network/sensor intelligence and ethical AI for SAP HANA maintenance scenarios. Future work will focus on large‑scale deployment across heterogeneous sensors/networks, rule‑learning automation, and standard‑ised ethical automation modules
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