IPHO-Journal of Advance Research in Science And Engineering

WANT TO BECOME A SPLUNK ENGINEER? HERE'S WHAT I WISH I KNEW STARTING OUT

RAHUL BHATIA — 2026-05-07

Observability engineering turns diverse machine data into understanding of systems for operations, reliability, and security. Does this mean anything? Platforms like Splunk can ingest, index, correlate, and visualize almost any kind of heterogeneous data, but their scope is so broad they easily overwhelm beginners and create silos. The roadmap in this article is based on years of experience designing and building Splunk applications and consists of the steps necessary to evolve from a casual user to an experienced Splunk engineer. To get there, you must learn how the data pipeline works and gain expertise in SPL, native log formats, and an increasingly complex personal Splunk lab. It also explores the modular architecture, packaging of extensions, and knowledge objects to enable scalable analytics and reusable operational intelligence. It concludes by showing engineers how using structured preparation for certifications and scenario-based interviews can convert their private technical knowledge into evidence of engineering judgment in the workplace.

THE ECONOMICS OF ENTERPRISE AI: NAVIGATING THE AI VALUE CHAIN, RETURN ON INVESTMENT, AND STRATEGIC CUSTOMIZATION

RAJAN SETH — 2026-05-15

Artificial intelligence has moved from experimentation to production for organizations competing in fast-moving markets. To invest effectively, enterprise leaders need a grounded view of the AI ecosystem how hardware concentration, compute-access models, and application layers combine to turn computation into measurable outcomes. Upstream, semiconductor manufacturing and critical tooling remain highly consolidated, creating structural chokepoints shaped by physics limits, capital intensity, and specialized expertise. Midstream, infrastructure consumption spans hyperscaler cloud, specialized AI clouds, and hybrid or on-prem deployments, with tradeoffs across cost, speed, sovereignty, and compliance. Downstream, applications translate compute into productivity improvements and, increasingly, new revenue streams across functions and industries. This article presents a practical ROI lens for enterprise transformation: value typically appears first through internal efficiency engineering velocity, marketing operations, analytics, and customer support before scaling into external, customer-facing products and services. It also outlines two complementary customization paths that drive differentiation without full retraining: retrieval-augmented generation (RAG) to ground outputs in proprietary knowledge with traceability, and parameter-efficient fine-tuning (PEFT) to adapt behavior, style, and format with lower training cost. Finally, it emphasizes governance as the control layer evaluation, monitoring, guardrails, and auditability needed to reduce unsupported outputs, protect brand and data, and scale adoption responsibly. The central conclusion is simple: token generation is a means, not the end; durable value creation is the success criterion for enterprise AI adoption.

DESIGNING AI-NATIVE FINANCIAL SYSTEMS: ARCHITECTURE PATTERNS FOR INTELLIGENT ENTERPRISE PLATFORMS

AJAY MIRANI — 2026-05-16

Enterprise financial platforms have historically been designed as systems of record augmented post-hoc with analytical and AI capabilities — an approach that imposes a compounding integration tax that grows superlinearly with platform complexity. This paper argues that AI must be treated as a foundational architectural concern from platform inception, not a layered addition, and introduces five architecture patterns for AI-native financial system design: Event-Driven Intelligence, Federated AI Governance, Compliance-by-Design, Adaptive Schema Fabric, and Explainability-First. These patterns, derived from practitioner experience designing and operating enterprise-scale financial platforms at a high-volume global technology manufacturer, collectively define a reference architecture for AI-native financial platforms. The paper further presents a simulated case study of an AI-native order-to-cash replatforming initiative and a comparative evaluation against traditional layered and bolt-on AI architectures. The Compliance-by-Design pattern explicitly integrates the Adaptive Compliance Intelligence and Governance (ACIG) framework as its implementation blueprint, establishing a formal connection between platform-level AI-native design and domain-specific compliance intelligence. Together, the two papers constitute a cohesive architectural framework for intelligent enterprise financial governance.

EVENT-DRIVEN MICROSERVICES FOR SYNCHRONIZATION FIDELITY IN HOSPITAL DIGITAL TWIN SYSTEMS

KRISHNA MATTAM — 2026-05-23

INTRODUCTION: Hospital operations face critical inefficiencies in emergency department flow, ICU capacity planning, and patient admissions management. Legacy monolithic IT architectures are fundamentally ill-equipped to support the high-frequency, real-time data synchronization that operationally actionable digital twins require.

OBJECTIVES: This article investigates how event-driven architectures (EDA) and cloud-native microservices enable the synchronization fidelity necessary to deploy hospital digital twins as dynamic, real-time operational tools rather than static descriptive simulations.

METHODS: A structured narrative synthesis of peer-reviewed literature was conducted, drawing from PubMed, IEEE Xplore, Scopus, and the ACM Digital Library, using a systematic search protocol with defined inclusion and exclusion criteria, reported in accordance with the PRISMA framework.

RESULTS: Microservices provide essential architectural modularity by decomposing monolithic systems into independently scalable services. EDA, via asynchronous event streaming through platforms such as Apache Kafka and Azure Event Grid, acts as the definitive enabling layer coupling physical hospital environments with their digital replicas. Key findings confirm latency reductions enabling real-time predictive simulation across trauma, triage, and capacity planning contexts. Significant tensions between scalability and data privacy, alongside interoperability barriers, remain unresolved.

CONCLUSION: A tri-layer conceptual framework — comprising the physical context layer, the event-driven microservices layer, and the agent-based digital twin layer — is proposed to guide future hospital digital twin deployments and inform both IT governance and security policy.