Aug 2026· International Journal of Scientific Research in Computer Science Engineering and Information Technology· Vol 12, pp. 359-366· 0 citations
TL;DR
A five-layer AI-augmented pipeline operating model that replaces reactive recovery with proactive, adaptive operation and is grounded in operational observability as a prerequisite for automation, with governance controls embedded as non-optional cross-cutting elements.
Abstract
Deterministic ETL architectures - scheduled, fixed, and failure-reactive - cannot sustain the operational requirements of modern enterprise data environments, where volume growth, schema instability, and SLA pressure compound continuously. This paper presents a five-layer AI-augmented pipeline operating model that replaces reactive recovery with proactive, adaptive operation. The model integrates intelligent scheduling, continuous anomaly detection, and an operational copilot capability within a coherent Azure-native reference architecture anchored by a persistent feedback store. A structured implementation pathway and a three-dimensional evaluation framework - covering operational reliability, data quality, and delivery performance - are provided alongside the architectural specification. The model is grounded in operational observability as a prerequisite for automation, with governance controls embedded as non-optional cross-cutting elements.
AnyLog provides a cloud-like operating model for distributed SQL, real-time automation, Edge AI, federated learning, and resilient decision-making without a single point of failure or any dependence on centralized infrastructure.
Roy Shadmon, Mark Davidson, Eric Aquaronne et al.· arXiv.org· 0 citations
As enterprises transition toward increasingly distributed and cloud-native architectures, the need for seamless data interoperability has become paramount. Traditional data integration and governance approaches often fall short in dynamic, multi-cloud environments. Autonomous Data Products (ADPs) emerge as a transformative paradigm—self-contained, self-describing, and AI-enabled units that encapsulate data, metadata, policies, and processing logic. This paper explores the architecture, capabilities, and implementation strategies of ADPs to enhance data interoperability across cloud ecosystems. We discuss how AI enables adaptive schema evolution, smart data discovery, and automated quality checks within ADPs, and how they align with principles of data mesh and data fabric. Through technical frameworks and real-world use cases, we demonstrate how autonomous data products can drive scalability, agility, and intelligence in modern data architectures.
Arjun Malhotra· International Journal of Dat...· 0 citations
Real-time automation of financial activities, including approvals, controls, routing, and monitoring, can enable fast response to opportunities and threats, eliminating the rotation of capital through liquidity provider balances and streamlining interactions with sources of capital. Generative and agentic AI technologies, supported by real-time data streams of events, market feeds, and key risk indicators, can automate and govern these transactions provided that high standards of latency, throughput, security, and regulatory compliance are achieved. A cloud-native DevOps ecosystem equipped with serverless infrastructure-as-code patterns ensures scalable, cost-effective operations with minimum user interference, allowing a comprehensive evaluation of operational performance, change management, security controls, and regulatory oversight. Scenarios involving AI agents as the driving or supervisory part of automation workflows illustrate the architectural paradigm. The analysis identifies the expected challenges in operational responsibility and response reliability, gathering additional evidence from existing Cloud-Native/Serverless solutions of similar scope. Mitigation strategies address the main issues encountered in every-day AI adoption and propose supporting operations management and security controls. The examination of the full end-to-end process flow, including off-line and real-time phases, ensures cover for Governance, Risk, and Compliance (GRC) objectives by design and the definition of a robust plan for evaluation in on-line use.
E. Campbell· American International Journ...· 0 citations
Predictive maintenance leverages machine learning and real-time data analytics to anticipate equipment failures before they occur, thereby reducing downtime and optimizing operational efficiency. However, the deployment of such systems in edge computing environments introduces challenges related to latency, scalability, and resource constraints. This paper presents a scalable architecture for data pipelines that enables real-time predictive maintenance at the edge. We propose a modular pipeline design combining lightweight edge processing, efficient data streaming, and cloud-based model orchestration. The architecture is evaluated using industrial sensor data and edge devices in a simulated smart manufacturing environment. Our results demonstrate significant improvements in latency reduction, system scalability, and fault prediction accuracy, validating the effectiveness of the proposed approach for real-world edge deployments.
S. Rahman, Kenji Sato· International Journal of Dat...· 0 citations
Enterprise adoption of artificial intelligence is restructuring the discipline of infrastructure planning in ways that conventional capacity models cannot accommodate. Artificial intelligence workloads span a heterogeneous spectrum of training, fine-tuning, inference, and batch scoring operations, each imposing qualitatively distinct demands on accelerator compute, storage throughput, and network fabric. The proliferation of graphics processing unit-accelerated clusters, high-bandwidth interconnects, and multi-cloud execution environments has rendered traditional provisioning frameworks inadequate for governing the scale, velocity, and compliance complexity inherent to production artificial intelligence platforms. This article presents a practitioner-oriented engineering framework for provisioning artificial intelligence-ready infrastructure that remains architecturally stable across accelerator generations, managed service evolutions, and organizational growth trajectories. Drawing on operational patterns from large-scale cloud transformation programs, the framework addresses workload segmentation, layered platform architecture, accelerator cluster governance, data provenance, network engineering, security, reliability, and cost governance as interdependent engineering concerns. The central argument is that organizations achieving sustained operational excellence in artificial intelligence infrastructure do so through deliberate platform architecture governed by automation-first operational practices, not through hardware procurement alone. The article concludes by projecting the long-term strategic implications of multi-cloud artificial intelligence transformation as a governed maturity progression, offering forward-looking guidance for infrastructure architects navigating an accelerating and mission-critical technology landscape
Hemanth Kumar Gandavarapu· International Journal of Eng...· 0 citations
An artificial intelligence-enhanced middleware pattern that augments existing integration stacks with telemetry, stream processing, and a lightweight learning loop to predict failures, automatically tune policies, and direct traffic in real time is presented.
Tejas Gajjar· International Journal of Inf...· 0 citations
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