International Journal of Emerging Trends in Computer Science and Information Technology
Latency Optimization Techniques for Near Real‑Time Market Data
High-frequency trading (HFT) and algorithmic market-making have reduced the latency between sub-micro second and now challenged near real-time market data infrastructure with one of the most challenging engineering problems in distributed-systems engineering in the modern finance sector. The paper includes a detailed scientific study of methods of minimizing latency of an entire market data consumer system, including both the hardware interface (network) and the consumer software applications at the top. A cumulative sum of all the optimization layers resulted in a 57.9 times decrease in median end-to-end message latency, a 45.2 µs median Latency on a standard Linux stack to 0.78 u Latency with an FPGA feed handler, and a 99th-percentile Latency of 1.38 u Latency on a standard Linux stack to that of 1.38 u Latency on an FPGA feed handler. Regulatory considerations and implications of MiFID II and Reg NMS are addressed and nine practical recommendations are given to practitioners at various levels of the latency-optimization investment spectrum. CXL memory pooling, P4-programmable in-network normalization, and quantum key-distribution of low-latency secure feed channels are the future directions.
Scalable Revenue Platform Design: Architectural Principles and Best Practices
In the constantly emerging new world of e-business, the requirements for robust revenue solutions are manifest. These platforms need to be scalable and relatively stable, and this should also cover different payment methods and business models. The following article aims to reveal the theory and practices of scaling revenue platforms and explains the fundamental principles of architecture. The focus is on discussing the design of the system, its growth pattern, and the use of up-to-date trends, including microservices, cloud computing, and machine learning. Furthermore, the paper looks at successful case studies of cloud computing implementation with references made to lessons learnt. In the current paper, based on the literature review, the common issues are identified, and the corresponding methodologies are suggested. The results and discussions section gives evidence about realistic implementation and advantages of the proposed design principles. Last but not least, we are going to discuss the prospects, as well as future research directions in the constantly evolving branch of knowledge.
Enterprise Integration for Manufacturing Using IoT: A Framework for Real-Time Data Exchange, Predictive Maintenance, and Smart Factory Operations
Manufacturing enterprises increasingly rely on interconnected sensors, controllers, and software platforms that convert physical operations into continuous streams of actionable information. Enterprise integration for manufacturing links machine-level Internet of Things (IoT) devices with resource planning, execution, and quality systems, creating a unified environment where production data moves in real time across organizational boundaries. This convergence transforms isolated production lines into responsive, self-monitoring ecosystems capable of forecasting equipment failures before they occur and adjusting operations dynamically. Core outcomes include reduced unplanned downtime, extended equipment life, improved asset utilization, and tighter alignment between shop-floor execution and enterprise-level decision-making. Predictive maintenance emerges as a central capability within this framework, using sensor-derived condition data and machine learning models to anticipate failures and schedule interventions before costly breakdowns occur. Digital twins extend these capabilities further, offering virtual replicas of physical assets that mirror real-world behaviour and support simulation, optimization, and remote diagnostics. Middleware platforms and standardized communication protocols such as MQTT and OPC UA enable interoperability between heterogeneous devices and enterprise resource planning, manufacturing execution, and supply chain management systems, addressing long-standing barriers of data silos and fragmented visibility. Practical significance extends beyond operational efficiency: organizations that integrate IoT with enterprise systems gain measurable improvements in energy consumption, resource allocation, and product quality, while building the foundation for adaptive, self-optimizing smart factories. Persistent barriers, including cybersecurity exposure, legacy-system compatibility, and the cost of large-scale sensor deployment, continue to shape adoption decisions across industries of varying scale. Manufacturers that align technical architecture with organizational processes convert raw sensor output into a strategic asset, positioning smart factory operations as a decisive factor in industrial competitiveness.
Event-Based Microservices Architectures for High-Throughput Sanctions Compliance
As financial institutions engage in more and more cross-border transactions, as well as changing regulatory requirements and constantly growing global watchlists, the volume and complexity of the demand for sanctions screening is growing to unprecedented levels. Traditional monolithic compliance solutions may not offer the scalability, resilience, low-latency and operational flexibility needed to effectively support real time sanctions screening, leading to processing bottlenecks, delayed decision making and higher operational costs. The study introduces a new microservices-based architecture that allows for high throughput for sanctions compliance based on principles of asynchronous event streaming, decoupled services and using cloud-native deployments. In the proposed architecture the data ingestion service, event publishing service, sanctions screening service, watchlist management service, alert generation service, audit logging service, notification service and reporting service are all first class pieces of microservices that can be deployed independently, meaning they can be detached from any concerns when trouble arises.The proposed architecture breaks down the compliance workflow into independently deployable microservices each responsible for their own set of actions, such as data ingestion service, event publishing service, sanctions screening service, watchlist management service, alert generation service, audit logging service, notification service, and reporting service, and an event broker is used for reliable, scalable and fault-tolerant communications between these services. The research methodology involves architectural design, technology integration and performance evaluation on representative high volume sanction-screening data sets with the aim of measuring throughput, response latency, scalability, fault tolerance and resource usage across different transaction rates. Experimental results show that the proposed architecture achieves significantly higher processing throughput, lower end-to-end response time, and horizontal scalability, and high continuous services availability when there is a component failure compared to the traditional monolithic implementations. Moreover, the architecture enhances regulatory requirements by offering robust audit logs, secure event processing, as well as durable and distributed execution. This work makes several key contributions, including the creation of a scalable event-driven model for financial compliance, by embedding cloud-native microservices into the distributed event streaming infrastructure of financial sanctions screening in real-time; and a detailed performance assessment to show that the proposed architecture was viable, enabling the implementation of future enterprise financial compliance engines handling large volumes of financial transactions in a regulatory environment.
A Digital Twin Framework for Enterprise IT Operations: Bridging Application Development, Infrastructure Management, Product Migration, and Operational Support
Enterprise IT environments have become increasingly complex due to the rapid growth of cloud computing, microservices architectures, DevOps practices, hybrid infrastructures, and continuous product evolution. Traditional management approaches often operate in silos, creating challenges in application development, infrastructure management, product migration, and operational support. This paper proposes a comprehensive Digital Twin Framework for Enterprise IT Operations that establishes a dynamic virtual representation of enterprise technology ecosystems. The framework integrates real-time operational data, application lifecycle information, infrastructure telemetry, migration intelligence, and support analytics into a unified digital environment. By leveraging technologies such as artificial intelligence, machine learning, observability platforms, predictive analytics, and automation, the proposed framework enables continuous monitoring, simulation, impact analysis, capacity planning, risk assessment, and proactive decision-making across the IT lifecycle. The framework bridges the gap between development and operations by providing end-to-end visibility of applications, infrastructure resources, business services, and migration dependencies. Furthermore, it supports scenario-based testing, root-cause analysis, performance optimization, and predictive maintenance, reducing operational risks and improving service reliability. A conceptual architecture is presented to demonstrate how digital twin capabilities can enhance collaboration among development, infrastructure, migration, and support teams while increasing organizational agility and operational resilience. The study contributes a unified approach to enterprise IT management and highlights the potential of digital twins as a strategic enabler for intelligent, data-driven IT operations.
JupyterOps: Version-Controlled, Automated, and Scalable Notebooks for Enterprise ML Collaboration
In the present day's data-centric corporations, the necessity for data science workflows that are scalable, cooperative & more replicable has reached an all-time high. Although traditional Jupyter notebooks are great for searching & testing, they are not enough for team-based work, which needs version control, automation & orchestration on the enterprise level. A strong framework called JupyterOps completely redefines the collaboration of data science teams by applying DevOps concepts directly to the notebook lifecycle. JupyterOps not only incorporates versioning via Git but also executes notebook automation through CI/CD pipelines, orchestrates workflows using Kubeflow or Airflow, and ensures scalability by employing a cloud-native containerization approach, thus bridging the gap between experimentation and production. The system allows seamless transitions from research to deployment, thus enabling teams to keep a record of changes, reproduce results, schedule executions, and scale compute on demand. This article describes the key parts and overall layout of JupyterOps, besides giving hands-on direction for enterprises on the way they can install it in their ML workflows. Several important pieces of information are outlined, such as a drastic decrease in deployment time, better model reproducibility, and increased cross-functional collaboration between data engineers, scientists, and DevOps teams.
Lakehouse Architecture and the Semantic Revolution: Bridging Analytics and Governance with AI
In the present day's digital world, businesses are moving beyond the limits of traditional data warehouses and lakes and using data lakehouse architecture, which is more flexible and more scalable. This hybrid architecture combines the reliability of data warehouses with the flexibility & low price of the data lakes to provide a single platform that makes analytics easier and makes data more accessible. But as data grows in size and complexity, merely having good storage isn't enough. The semantic layer is a strong abstraction that makes it possible for all data consumers to have the same business-oriented definitions. This makes sure that data is easy to find, understand, trust & use again. The semantic layer makes raw data relevant and links the technological information infrastructure to these strategic decisions. When you add AI to this bridge, it becomes wiser. AI makes it easy to automate things like keeping track of information, watching provenance, identifying more abnormalities, and making their own findings. This improves data governance and makes predictive and prescriptive analytics possible. A convincing case study shows how a big company uses lakehouse architecture, together with a strong semantic layer and AI, to speed up time-to-insight, data compliance, and communication across more departments by a lot. This change not only improved their technology foundation, but it also changed how their company thought about data-driven innovation. The combination of lakehouse frameworks, semantic intelligence, and AI marks the beginning of the latest way of doing business analytics: one that is well-organized, more smart & planned to be that way. This convergence not only solves problems that already exist, but it also gives us a chance to rethink how data supports business at all levels. Not only will we need to save information in the future, but we will also need to understand it, manage it ethically & use it wisely to get the all actual outcomes.
Governed LLM Interfaces for Enterprise Data Access: Chat-Based Querying with Policy Constraints
Large Language Models (LLMs) are turning into conversational interfaces for accessing the data of the organization while the enterprises work to make data more accessible across roles and departments. Chat-based LLM systems facilitate natural language conversations that open data access to all, eliminate reliance on technical staff, and speed up decision-making. Still, it also brings about serious issues like data security, regulatory compliance, and internal governance. The problems are especially acute if the information is sensitive or is in a silo. Enterprises are going to have to deal with risks resulting from unregulated queries, potential leakage of data, and the violation of access rights. Our research, to solve this problem, investigates the concept of a governed interface model, where LLMs are wrapped with policy-aware restrictions that provide the implementation of data usage rules, user permissions, and compliance boundaries in real time. The system not only directly incorporates access policies and governance logic into the LLM pipeline, but also allows secure, context-aware conversations with enterprise data. As a case in point, we illustrate a financial services firm that rolled out an actively regulated LLM interface that allowed the business users to access customer analytics and operational metrics freely. The implementation did not only keep the strict role-based access controls and the ability to be audited, but it also maintained the flexibility and usability of a conversation-style interface. The key results show that there have been significant gains in adoption and productivity while there was a drastic drop in the number of unauthorized query attempts. The case also points out some practical challenges in policy enforcement, latency management, and user training. Broadly speaking, the study demonstrates the capability of LLMs to serve as enterprise-grade data interfaces when combined with the governance schemes that ensure that the flow of users is controlled while still maintaining access. This approach stirs up a practical pathway from here on for organizations that want to safely draw upon the power of AI-driven, chat-based data access.
Explainable Big Data Pipelines: Trust and Transparency in AI-Augmented ETL
In the highly data-centric world of today, ETL (Extract, Transform, Load) pipelines are the basic components of enterprise analytics and decision-making. As companies are employing AI more and more to automate and maximize ETL pipelines, the explainability challenge is emerging alongside. The point is that AI systems are gaining more and more autonomy and the whole process of transformation is not any longer visible to the analysts who are left with just the end results. Simply put, the rationale of those decisions seems to be vague or hard to uncover at times. This raises questions. Knowing which action the AI took is not enough for stakeholders; they also want the reasons for this action. The data scientists may be able to vouch for the outcomes, but the compliance teams, business users, and regulators all require that the results they get are clear. In the absence of explainability, trust fades and there is an increased likelihood of biased or incorrect data handling. This is exactly where Explainable AI (XAI) finds its place. .The article is suggesting a framework for integrating XAI into large data pipelines, thus presenting each AI-powered change as easily understandable. Through the use of interpretable models, embedding of audit trails, and provision of real-time justifications for AI decisions, an organization can have the best of two worlds: a smart pipeline and one that is trustworthy. Apart from meeting regulatory demands, the use of these pipelines can also help cross-functional collaboration and the maintenance of organizational governance standards. The main point is quite straightforward: transparency is not only a compliance requirement but also a benefit in terms of business. The presence of explainable pipelines enables teams to debug quicker, audit more efficiently, and gain trust to a greater extent. In a world where data is a form of currency, being aware of the handling process is of utmost importance. Explainability is the link that connects innovation and trust in the AI-augmented ETL era.
The Future of Site Reliability Engineering: AI-Driven Observability and Autonomous Operations in Multi-Cloud Environments
SRE has come a long way since its start, from monitoring infrastructure health to reactive events. Modern SRE is an AI-driven observability platform providing real-time visibility into complex distributed systems. As multi-cloud use grows, operational complexity increases and it becomes increasingly complicated to provide dependability, performance and security across a broad range of cloud platforms. Artificial Intelligence (AI), Machine Learning (ML) and AIOps technologies are changing Service Availability and Incident Response (SRE) with intelligent anomaly detection, predictive analytics, automated root-cause investigation and autonomous remediation in response to such. The effort aims at investigating the future of service-oriented architecture (SRE) in the age of AI-based observability and autonomous operations in multi-cloud environments. Through a review of current technologies, industry practice and upcoming trends in research. The objective of this research is to investigate the viability of the application of AI-based solutions to enhance system dependability, minimize operational overhead and optimize incident response efficiency. The study will also address issues of scalability, interoperability and governance. The results indicate that AI and ML in observability systems can deliver automated operational workflows that help enable proactive reliability management, minimize downtime and accelerate decision making. This makes AIOps powered autonomous operations a rapidly growing important enabler for cloud native infrastructures with self repairing systems. This paper describes the convergence of AI with software defined networking (SRE) and strategic implications for enterprises seeking strong, scalable and efficient cloud operations. The results show intelligent automation is becoming more important in shaping the future of cloud-native reliability management and operational excellence.
DevOps Beyond Software: Establishing CI/CD Frameworks across Semiconductor and Cloud Engineering Lifecycles
DevOps has changed the way we build and deploy software, focusing on collaboration, automation, continuous integration and continuous delivery (CI/CD). This allows enterprises to provide software faster, with higher quality and greater operational efficiency. While these ideas are frequently employed in traditional software engineering, their use in other technological fields is still limited, despite increasing complexity and increasing demands of speed and dependability. In this post we discuss how CI/CD approaches may be used outside of software development to semiconductor and cloud engineering life-cycles. Disconnected workflows, lengthy validation cycles, varied toolchains and segregated teams can hinder productivity and creativity. Semiconductor creation entails complex procedures of design, simulation, verification and fabrication, while cloud engineering demands constant provisioning of infrastructure, managing configurations, ensuring security and deploying services in ever-changing settings. Such issues point to the necessity for a unified methodology that can integrate automation, traceability and constant feedback across many engineering disciplines. In this work, we propose a comprehensive CI/CD solution, which merges software, semiconductor and cloud engineering processes in one DevOps-driven approach. This framework brings together test automation, versioning, artifact management, infrastructure orchestration, design validation and continuous monitoring into a single scalable engineering environment. This paper presents a realistic case study showing how the implementation in cross-functional teams, with the help of the standardized pipelines and integrated automation, facilitates cooperation, reduces cycle times, enhances quality assurance and accelerates delivery outcomes. The results suggest that the use of DevOps principles in non-traditional domains can yield significant benefits in terms of operational consistency, reduction of human work, and end-to-end visibility along the product life cycle. This study adds to the knowledge of enterprise-wide DevOps adoption by providing a viable roadmap to organizations that aspire to integrate engineering processes, break down domain silos and realize continuous innovation across physical and digital technology ecosystems.
Building Connected Enterprises: A Comparative Framework for API, Ipaas, and Serverless Integration Using Webmethods, Apigee, Boomi, and AWS Lambda in Digital Transformation
Modern businesses operate across many separate applications, databases, and external systems, creating obstacles that slow decision-making and hold back growth. Multi-platform integration helps overcome these obstacles by combining technologies such as hybrid middleware, API management, cloud-based integration platforms, and event-driven computing into a unified system that connects different parts of the business. Four key technologies show the variety of choices available: webMethods offers hybrid integration and business-to-business connections, helping companies link old systems with new cloud-based ones; Apigee manages the entire lifecycle of APIs, from design to security and monetisation; Dell Boomi provides a low-code integration platform as a service that quickly connects software-as-a-service applications with on-premises systems; and AWS Lambda allows for event-driven, serverless execution, removing the need to manage infrastructure for time-sensitive tasks. Together, these platforms form different layers of a composable integration stack that supports various digital transformation efforts, including improving customer experiences and enhancing supply chain visibility. Companies that choose the right platforms based on specific integration needs see clear benefits: faster integration of newly acquired business units, lower operational costs due to automatic scaling, and better control over shared data and services. The real impact becomes clear when organisations treat integration architecture as a key strategic asset rather than just a minor technical task, as scattered connectivity directly limits how quickly new digital products can reach customers. Choosing the right mix of hybrid middleware, API governance, low-code integration platforms, and serverless computing allows large-scale transformation projects to move forward with predictable costs, consistent security, and the flexibility to adjust as software systems change. This approach helps organisations smoothly scale their digital efforts compared to those held back by outdated, point-to-point integration methods.
AI-Powered Precision Public Health: A National Framework for Targeting Chronic Disease Prevention and Early Intervention
Chronic diseases remain among the leading causes of preventable illness, disability, mortality, and healthcare expenditure in many national health systems. Traditional public health approaches have contributed significantly to disease prevention, but they often rely on broad population-level strategies, delayed surveillance, and reactive intervention models that may fail to identify high-risk individuals and communities early enough. This paper proposes an AI-powered precision public health framework for targeting chronic disease prevention and early intervention at national level. The framework integrates electronic health records, public health surveillance data, social determinants of health, genomic risk information, and predictive analytics to support earlier risk detection, population segmentation, and tailored prevention strategies. It emphasizes the use of artificial intelligence not as a replacement for public health expertise, but as a decision-support mechanism for improving resource allocation, screening, care coordination, and intervention timing. The paper also highlights the ethical, governance, and equity challenges associated with AI implementation, including algorithmic bias, privacy risks, data quality limitations, transparency, and public trust. It argues that AI-powered precision public health can strengthen chronic disease prevention when implemented through responsible governance, human oversight, equity-focused design, and continuous learning health systems. The proposed framework provides a national policy direction for improving early detection, reducing preventable complications, and promoting more targeted and equitable health outcomes.
AI-Driven Test Automation Frameworks for the Modern Software Quality Engineering
The emergence of Artificial Intelligence (AI) technology in software testing and quality engineering has played a significant role in intelligent automation and analysis. Current test automation models may face limitations in terms of flexibility and ability to predict defects and allocate test resources effectively. This paper proposes a novel adaptive test-based test automation model that leverages the capabilities of AI technology to improve the quality engineering of software systems. The proposed framework is a combination of predictive analysis and test execution process to avoid redundancy of test cases and maximize the effectiveness of test cases. Moreover, it also contains learning algorithms to maximize the accuracy of test cases through the analysis of past test results and software behavior models. The experimental evaluation confirms that the proposed framework can provide better test coverage, lower test execution time, and enhanced software reliability compared to existing software automation techniques. Additionally, the framework is highly scalable and adaptable, enabling its incorporation into agile and DevOps development methodologies to ensure ongoing Quality Assurance and intelligent testing. The findings prove that AI-assisted automation models have tremendous potential.
Cloud-Native Multi-Factor Authentication Framework for Digital Banking Systems: An AI-Driven Adaptive Security Architecture
The digital banking ecosystem faces unprecedented security challenges with the imminent advent of quantum computing, which threatens to compromise existing cryptographic infrastructures. Traditional multi-factor authentication (MFA) systems in banking rely on classical cryptographic primitives vulnerable to quantum attacks, creating critical security gaps in financial transactions and customer data protection. This paper introduces a novel Quantum-Enhanced Multi-Factor Authentication Framework (QE-MFAF) specifically designed for digital banking systems, integrating lattice-based post-quantum cryptography with biometric fuzzy commitment schemes and behavioral analytics. The proposed framework incorporates a hybrid authentication mechanism combining quantum-resistant cryptographic protocols, continuous behavioral pattern recognition using deep learning models, and secure multi-party computation for transaction verification. Experimental validation on a synthetic banking dataset demonstrates superior performance with 99.7% authentication accuracy, 0.02% false positive rate, and 15ms average authentication latency while maintaining quantum resistance. The framework successfully mitigates advanced persistent threats, insider attacks, and quantum-based cryptographic vulnerabilities while ensuring seamless user experience in high-transaction banking environments. Performance comparisons with existing banking authentication systems show 34% improvement in security metrics and 28% reduction in computational overhead.
Risk-Adaptive Cloud-to-Edge Application and Data Update Architecture for Android-Based Embedded Systems Using Lightweight Edge AI Validation
Android-based embedded systems increasingly receive application packages, configuration data, policy files, machine-learning models, and security metadata from cloud services. These cloud-to-edge updates improve product maintainability, but they also expand the attack surface: a device can receive a stale manifest, a mismatched split package, a corrupted artifact, an unsafe policy revision, or a model update that is technically valid but operationally risky for the current device state. This paper proposes a risk-adaptive cloud-to-edge application and data update architecture for Android-based embedded systems using lightweight edge AI validation. The architecture combines signed update manifests, artifact-level integrity checks, application data-security controls, persisted update-state management, package compatibility verification, rollback-safe orchestration, and a quantized on-device risk classifier. The edge AI component does not replace deterministic security gates; instead, it prioritizes, defers, quarantines, or escalates updates based on contextual risk features such as signature status, manifest freshness, package version drift, retry history, rollback frequency, network trust, power state, storage pressure, and prior installation failures. The proposed architecture is designed for industry environments where Android applications run on embedded, automotive-style, kiosk, retail, industrial, or managed-device platforms and where cloud-delivered updates must be secure, observable, recoverable, and operationally safe. A reproducible evaluation framework is provided using metrics for false acceptance, false rejection, validation latency, memory overhead, rollback detection time, manifest replay resistance, and update completion reliability.
Enterprise AI Governance Architecture for Salesforce-Based Healthcare CRM Platforms: A Pattern-Oriented Framework for Regulated AI
Artificial intelligence is moving rapidly from experimentation to day-to-day operations in healthcare and insurance platforms, especially in customer relationship management systems that support service workflows, care coordination, claims guidance, and member engagement. This shift creates a difficult but important question for regulated enterprises: how can AI be introduced into cloud CRM environments without weakening compliance, explainability, operational trust, or human accountability? This paper proposes a practical governance pattern library for regulated cloud CRM platforms, with a focus on healthcare and insurance environments using modern cloud services, workflow orchestration, and data activation layers. The article develops a design-oriented reference framework that brings together policy controls, human-in-the-loop review, consent-aware data usage, prompt and model governance, auditability, and release discipline. Rather than treating AI governance as a legal checklist alone, the paper presents it as an enterprise architecture concern that must be embedded into platform design, delivery governance, and operating workflows. The main contribution is a practitioner-oriented framework that helps organizations scale AI-enabled CRM functions while preserving trust, resilience, and regulatory readiness.
Counterfactual Deployment Event Graphs for Explainable Cost Attribution and Resource Efficiency Optimization in Kubernetes Workloads
Kubernetes has become the dominant substrate for cloud-native workload orchestration, yet its cost behavior remains difficult to explain because infrastructure expenditure is produced by a dynamic interaction among deployment events, scheduler decisions, autoscaling loops, resource requests, workload dependencies, and shared-node allocation policies. Existing cost observability systems often report expenditure retrospectively at namespace, service, or cluster levels, but they rarely explain why a cost increase occurred, which deployment event produced it, or what alternative configuration would have reduced waste without violating service-level objectives. This paper proposes Counterfactual Deployment Event Graphs (CDEGs), a conceptual and methodological framework for explainable cost attribution and resource efficiency optimization in Kubernetes workloads. CDEGs model Kubernetes operational history as a temporally indexed, causally annotated graph connecting deployments, pods, nodes, autoscalers, telemetry streams, billing records, configuration changes, and service dependencies. The framework integrates causal counterfactual reasoning, graph-based provenance, workload telemetry, and FinOps-oriented cost allocation to estimate how observed costs would have changed under alternative deployment decisions. Unlike purely correlational dashboards, CDEGs support path-specific cost explanations, actionable recourse recommendations, and guarded optimization policies for rightsizing, autoscaling, scheduling, and consolidation. The paper defines the problem, presents the graph model and counterfactual attribution procedure, describes a reference architecture, and proposes evaluation criteria for attribution fidelity, counterfactual validity, optimization effectiveness, operational overhead, and explanation usability. Analytical discussion demonstrates how CDEGs can distinguish legitimate elasticity from avoidable overprovisioning, identify deployment-induced waste, and bridge engineering and financial accountability. The study contributes a research agenda for explainable Kubernetes cost intelligence that is auditable, causally grounded, and practically aligned with production reliability constraints.
AI-Ready Enterprise CRM Organizations: A Governance, Transformation, and Agent-Orchestration Architecture for Intelligent Business Operations
Enterprise customer relationship management (CRM) systems are transitioning from transactional platforms into intelligent operating environments that integrate data, decision intelligence, workflow automation, and autonomous software agents. However, most enterprise CRM organizations remain insufficiently prepared for artificial intelligence (AI) because their governance structures, data foundations, process architectures, and accountability mechanisms were designed for deterministic systems rather than adaptive, learning-enabled business operations. This paper develops a research-oriented framework for AI-ready enterprise CRM organizations by synthesizing CRM theory, data governance, IT governance, digital transformation, business process management, MLOps, AI risk management, and multi-agent systems. The proposed Governed Agent-Orchestrated CRM Transformation framework conceptualizes AI readiness as a socio-technical capability composed of strategic governance, data and knowledge stewardship, process modularity, human-AI collaboration, agent orchestration, model lifecycle assurance, and value realization. The paper contributes a layered conceptual architecture that links enterprise governance with operational agent coordination, allowing sales, service, marketing, customer success, compliance, and analytics functions to operate through controlled intelligent workflows. Evaluation criteria are proposed across eight dimensions: strategic alignment, data readiness, process adaptability, agent reliability, model governance, human oversight, operational performance, and customer value. The analytical discussion shows that AI-ready CRM transformation requires more than embedding generative AI or predictive models into existing platforms; it requires a redesign of CRM operating models around governed autonomy, auditable decisions, role-aware agent collaboration, and continuous learning. The paper concludes by outlining practical implications, limitations, and future research directions for empirical validation, maturity modeling, and sector-specific deployment.
Cloud-Native Architectures for Enterprise Financial Data Management, Analytics, and Regulatory Reporting Compliance
Organizations using traditional financial data management infrastructures are facing many difficulties due to the explosive growth of enterprise financial data, the growing regulatory requirements and the need for real-time enterprise data analysis. For legacy systems, common challenges include data silos, inconsistencies in scalability, long reporting periods and challenges in keeping with changing regulatory needs. The study delves into how cloud-native architectures are reshaping financial data management, analytics, and regulatory reporting in enterprise environments with their scalability capabilities, resilience features, and governance-driven platforms. The goal of the research is to create an architectural vision that encompasses data ingestion, storage, processing, analytics, security and compliance capabilities for creating a unified architecture that is cloud-native. It proposes to implement a cloud-based data lakehouse architecture using containerized microservices, automated data pipelines, realtime streaming platforms and centralized management solutions to enable efficient finance operations and financial reporting. The effectiveness of the framework in solving the problems of data integration, scalability, compliance and performance is analyzed using design science and architectural analysis methodology. The results show that these cloud-native financial platforms can have a massive impact on data quality, processing efficiency, reporting accuracy, operational agility and regulatory transparency, while minimizing infrastructure complexity and lowering operating expenses. The paper offers a practical reference architecture, implementation guidelines and governance framework for modern financial enterprises. These results offer organizations profound insights into the modernisation of financial data ecosystems, advanced data analytics solutions and the acceleration towards sustainable regulatory compliance, particularly in today's data-driven business world.
Enterprise AI-Driven Data Engineering: Building Intelligent, Secure, and Scalable Data Platforms for Modern Organizations
The rapid proliferation of digital technologies, cloud computing, Internet of Things (IoT), big data ecosystems, and artificial intelligence (AI) has fundamentally transformed how organizations manage, process, and derive value from data. Traditional data engineering frameworks, designed primarily for structured and moderate-volume datasets, are increasingly incapable of addressing the complexity, velocity, variety, and scalability requirements of modern enterprises. Consequently, organizations are transitioning toward Enterprise AI-Driven Data Engineering (EAIDE), an advanced paradigm that integrates artificial intelligence, machine learning, automation, and intelligent orchestration into data platform architectures. This study investigates the role of AI-driven data engineering in building intelligent, secure, and scalable enterprise data platforms. The research examines key architectural components, including automated data ingestion, intelligent data pipelines, metadata management, data governance, cybersecurity integration, cloud-native infrastructure, and AI-powered analytics. Furthermore, the study evaluates the operational benefits, security implications, and organizational challenges associated with implementing AI-enabled data engineering frameworks. A conceptual research methodology based on comparative analysis of existing enterprise architectures, cloud-based platforms, and AI-driven automation techniques is adopted. Findings indicate that AI-enhanced data engineering significantly improves data quality, operational efficiency, decision-making capabilities, predictive analytics performance, and platform scalability. However, challenges related to governance, model transparency, ethical AI deployment, and cybersecurity remain critical considerations. The study concludes that enterprise AI-driven data engineering represents a foundational element of next-generation digital transformation strategies. Organizations adopting intelligent data platforms are better positioned to leverage data assets, support real-time analytics, and achieve sustainable competitive advantage in increasingly data-centric business environments.
Runtime Observability in Micro-Frontend Architectures: Distributed Tracing, Error Boundary Instrumentation, and Performance Budget Monitoring at Scale
Micro-frontend architectures distribute UI ownership across independently deployed modules, each maintained by a separate team. While this model improves deployment autonomy and team scalability, it creates monitoring gaps that conventional application performance tools do not address. When a remotely loaded module fails silently, when an LCP regression originates in one of several independently deployed remote modules, or when an error caught by a React error boundary lacks context about which module produced it, standard monitoring instrumentation yields incomplete signal. This paper presents a three-layer runtime observability framework for Module Federation-based micro-frontend systems. The first layer propagates distributed trace context across module load boundaries using the W3C Trace Context standard and the Open Telemetry JavaScript SDK. The second layer instruments React error boundaries in both host and remote modules to capture error reports that include the module identifier and the active trace ID, routing all errors to a shared collection endpoint regardless of which team deployed the faulting module. The third layer attaches per-module attribution tags to Core Web Vitals measurements collected through the Performance Observer API, making it possible to identify which remote module contributed to a page-level metric regression. We describe the architecture of each layer, discuss their interaction, and evaluate the framework against a set of six observable failure scenarios common in production micro-frontend deployments. Results show that the combined three-layer approach achieves root cause isolation for five of the six failure scenarios, compared to zero for single-application APM tools applied to the same scenarios.
Zero-Trust Wireless Architectures in Healthcare: Integrating Aruba ClearPass for Granular Policy Enforcement
Healthcare wireless networks present a uniquely challenging security environment characterized by high device density, strict uptime requirements, and a substantial population of Internet of Medical Things (IoMT) devices incapable of supporting traditional endpoint protection. Perimeter-based and endpoint-centric security models have proven inadequate in this context. This paper proposes and evaluates a Zero Trust Architecture (ZTA) framework for enterprise healthcare wireless local area networks (WLANs), implemented through Aruba Networks ClearPass as the centralized identity and policy enforcement plane. The framework integrates Network Access Control (NAC) with wireless Intrusion Detection and Prevention Systems (IDPS) to achieve continuous identity verification, dynamic micro-segmentation, and automated closed-loop threat enforcement. Deployed across a multi-campus healthcare environment encompassing more than 7,000 access points, 120 controllers, and 20,000 users across 60+ clinical sites, the architecture reduced detection-to-enforcement latency to under 200 milliseconds, decreased unauthorized access incidents by 73–78%, and reduced manual remediation requirements by 60–85%, with zero clinical downtime during security enforcement events. Results demonstrate that network-centric Zero Trust enforcement, anchored by ClearPass-based policy orchestration, constitutes a viable and scalable security model for critical healthcare wireless environments.
Unifying Customer Intelligence: An Enterprise Architecture for Real-Time Decisioning Using Microsoft Dynamics 365 and Power Platform
The contemporary enterprise landscape is defined by an unprecedented velocity of data generation and an equally intense demand for personalized customer engagement. Traditional Customer Relationship Management (CRM) systems, predicated on batch processing and historical data analysis, are increasingly inadequate for meeting the expectations of the modern consumer. This research proposes a comprehensive enterprise architecture for unifying customer intelligence through the integration of Microsoft Dynamics 365 and the Microsoft Power Platform. By transitioning from reactive data repositories to a "closed-loop" intelligence fabric, organizations can leverage real-time decisioning to influence customer journeys as they occur. The architecture centers on Microsoft Dataverse as a unified data hub, Dynamics 365 Customer Insights for identity resolution, and Power Automate for intelligent workflow orchestration. The study analyzes the technical mechanisms of event-driven architectures, the role of AI-driven inference layers, and the operational outcomes of multi-cloud hybrid deployments. Findings indicate that this unified approach yields significant improvements in operational efficiency, conversion rates, and strategic agility, effectively bridging the "diffusion gap" between technological potential and business value.
Strategic Leadership in the Age of Agentic AI: Redefining Executive Decision-Making and Organizational Control
Agentic artificial intelligence is reshaping how organizations plan, coordinate activities, allocate resources, monitor risks, and make strategic decisions. Unlike conventional AI systems that primarily provide predictions or recommendations, agentic AI can interpret objectives, plan actions, use organizational tools, coordinate workflows, and execute tasks with varying levels of autonomy. This shift creates important implications for strategic leadership, particularly regarding executive authority, decision rights, accountability, and organizational control. This study examines how strategic leaders can redefine executive decision-making and governance structures in organizations adopting agentic AI systems. Drawing on dynamic capability’s theory, agency theory, organizational control theory, and socio-technical systems theory, the study develops a framework that links strategic leadership capability, AI governance maturity, executive oversight, agentic AI autonomy, accountability clarity, and organizational performance. A mixed-methods approach is proposed, combining survey evidence from executives and AI governance professionals with qualitative interviews involving senior leaders, digital transformation managers, and risk professionals. The study investigates how leadership capabilities, governance arrangements, and control mechanisms influence decision quality, trust in AI-enabled processes, and organizational resilience. The proposed framework emphasizes the importance of clearly defined decision boundaries, human approval thresholds, explainability, auditability, escalation procedures, and continuous governance review. The study contributes to strategic leadership and AI governance research by positioning executives not simply as final decision-makers, but as architects of human-AI decision systems. It offers practical guidance for organizations seeking to balance agentic AI autonomy with responsible executive control, ethical accountability, and long-term strategic value.
Simulation-Based Validation of Machine Learning Optimization Models in Warehouse Operations
Deploying machine learning optimization models in warehouse operations carries significant operational risk, as suboptimal decisions can cascade into fulfillment failures, labor inefficiencies, and customer experience degradation at scale. Traditional offline evaluation metrics such as accuracy, RMSE, or reward convergence are insufficient to capture real-world operational constraints such as inventory bottlenecks, stochastic order arrivals, picking congestion, labor shift variability, and equipment limitations. This paper presents a simulation-based validation methodology for machine learning (ML) optimization models prior to production deployment in warehouse environments. The proposed framework integrates discrete event simulation (DES) with ML-based decision policies to evaluate system-level performance under realistic operational conditions. Unlike conventional validation pipelines that rely solely on historical datasets, the proposed approach embeds ML models into a virtual warehouse environment where order fulfillment processes, storage allocation, and picking strategies are dynamically simulated. The study emphasizes risk quantification through scenario-based stress testing, where variations in order volume, SKU distribution, labor availability, and equipment downtime are introduced systematically. Key performance indicators (KPIs) such as order cycle time, throughput rate, picker utilization, and SLA violation rate are measured across multiple simulation runs to assess robustness. A hybrid architecture combining reinforcement learning-based optimization and stochastic simulation is introduced. The model evaluates policy decisions at each decision epoch, while the simulation environment provides feedback in the form of system state transitions. This enables iterative validation before real-world deployment. The results demonstrate that simulation-based validation significantly reduces deployment risk by identifying performance degradation scenarios that are not observable in offline training metrics. Furthermore, the framework enables early detection of policy instability under peak load conditions, improving operational resilience. The contribution of this work lies in bridging the gap between ML optimization theory and warehouse operational realities by introducing a structured validation pipeline that ensures safer deployment of intelligent systems in supply chain environments. The approach provides a scalable methodology for enterprise-grade warehouse automation systems where operational errors carry high financial and service-level costs.
Machine Learning-Based Prediction of Technical Debt in Continuous Software Development Using Repository Mining and Developer Activity Analytics
Technical debt is no longer an occasional by-product of rushed implementation; it has become a persistent socio-technical risk in continuous software development environments where code, build scripts, configuration artifacts, tests, issue tickets, pull requests, and deployment workflows evolve at high velocity. Conventional technical debt management methods often depend on manual code reviews, static analysis alerts, or retrospective refactoring decisions, which are insufficient for detecting emerging debt before it becomes costly to repay. This paper proposes a machine learning-based framework for predicting technical debt in continuous software development by integrating repository mining, developer activity analytics, change-process metrics, self-admitted technical debt signals, and software lifecycle governance indicators. The proposed framework models technical debt as an evolving risk state rather than a static code smell, combining structural code metrics, commit-level churn, ownership dispersion, pull-request review behavior, issue-tracker semantics, CI/CD instability, and developer workload signals. The study introduces a multi-source feature architecture, a temporal labeling strategy, an explainable ensemble learning pipeline, and a debt-prioritization layer designed for continuous integration environments. Unlike review-oriented approaches that summarize technical debt literature, this manuscript develops a research-oriented predictive design that can be operationalized in modern software engineering pipelines. The expected contribution is a decision-support mechanism that helps engineering teams identify debt-prone modules, anticipate repayment urgency, improve sprint planning, and align refactoring decisions with delivery risk, developer capacity, and long-term maintainability.
MVVM Architectural Patterns for Enterprise Android Kiosk Applications: State Management and Lifecycle Handling
Conditions that enterprise Android kiosks have to handle differ dramatically from typical mobile apps, as they're used in commercial settings, e.g., in retail applications, warehouses, health care and industrial applications. Such systems are on expected to operate around the clock with much reliability, performance and access security to enterprise resources. These requirements create problems related to long-running sessions, memory management, process recovery, network interruptions, offline operation and smooth integration with enterprise identity and device management platforms. Common Android development strategies face challenges in meeting these requirements and there is a need for architectures to ensure predictable application behaviour, maintainable code bases and strong lifecycle management capabilities. This research explores the suitability of a Model-View-ViewModel (MVVM) architectural pattern as a building block design for enterprise Android kiosk applications emphasizing on state management, lifecycle resilience, and application operability. The research suggests the development of a cross-platform MVVM pattern framework which utilizes the contemporary development technologies of Android: Jetpack Compose, ViewModel, StateFlow, repository structure for data access and enterprise authentication systems. The framework has structured ways of handling the state of UI, preserving application context on change of configuration and restart of processes, authentication session by Single Sign-On (SSO) and biometric verification, and fault-tolerant recovery strategies in a kiosk context. Analysis of the proposed architecture shows that it achieves improvements over these traditional architectural approaches with regards to maintainability, state consistency, lifecycle recovery and integration with security. The rest of the study offers a practical architectural approach to enterprise Android-based kiosk systems and design guidelines to help organizations produce scalable, secure and resilient mobile architectures enabling enterprise mission critical operations.
Indoor Positioning and Location Intelligence SDK Design for Enterprise Mobile Applications
Indoor positioning systems (IPS) have emerged as a critical technological component for enterprise mobile applications operating within complex indoor environments where conventional Global Positioning System (GPS) technologies fail to provide reliable location information. Enterprises across retail, hospitality, healthcare, logistics, airports, and convention centers increasingly depend on location-aware applications to deliver contextual services, optimize customer engagement, improve operational efficiency, and enable intelligent analytics-driven decision-making. However, the implementation of indoor positioning solutions presents significant technical challenges due to heterogeneous positioning technologies, varying environmental conditions, infrastructure dependencies, and accuracy requirements. Enterprise developers require software development kits (SDKs) capable of abstracting diverse localization technologies while maintaining consistent performance, scalability, and ease of integration. Indoor positioning systems for enterprise mobile applications require SDK architectures that abstract heterogeneous positioning technologies including BLE beacon triangulation, Wi-Fi fingerprinting, and sensor fusion approaches, while providing consistent location accuracy and developer accessibility across diverse deployment environments. This paper presents the design and implementation of an indoor positioning and location intelligence SDK for enterprise Android applications, examining positioning technology abstraction layers, accuracy-latency tradeoff management, geofencing event delivery reliability, and analytics pipeline integration for location-based marketing use cases. The proposed SDK architecture incorporates modular positioning services, context-aware event processing, cloud analytics integration, and enterprise-grade security mechanisms. The architecture enables seamless switching between multiple localization technologies depending on environmental conditions and infrastructure availability. The study investigates SDK performance across large-scale indoor environments including retail stores, hospitality facilities, and convention centers. Experimental evaluation demonstrates improvements in positioning accuracy, event detection reliability, and deployment flexibility compared with conventional single-technology localization frameworks. Furthermore, the location intelligence layer enables real-time customer journey analysis, proximity marketing campaigns, heatmap generation, and behavioral analytics. Results indicate that the proposed architecture achieves significant improvements in operational efficiency while reducing integration complexity for enterprise application developers. The research contributes a comprehensive enterprise-focused SDK design framework that balances positioning accuracy, battery consumption, scalability, and developer usability. The findings provide practical guidance for organizations seeking to deploy scalable indoor positioning infrastructures and support future advancements in intelligent location-aware enterprise ecosystems.
Android AppLink Integration for Connected Vehicle Infotainment Systems: Architecture and Implementation Patterns
Connected vehicle infotainment systems present a unique integration domain for Android mobile applications, requiring reliable bidirectional communication between smartphone applications and in-vehicle head units under constraints of driver safety, low-latency interaction, and heterogeneous vehicle hardware environments. The rapid evolution of connected vehicles, automotive Internet of Things (IoT), and intelligent transportation systems has significantly increased the demand for seamless interaction between mobile devices and vehicle infotainment platforms. Android AppLink has emerged as a promising middleware framework that enables smartphone applications to extend their functionalities to vehicle head units while maintaining a safe and standardized human–machine interaction model. This paper examines Android AppLink integration architecture for connected vehicle infotainment systems by analyzing the AppLink protocol stack, proxy layer architecture, communication workflow, voice interaction mechanisms, and user interface adaptation strategies designed specifically for automotive environments. The study investigates how AppLink facilitates communication between Android applications and vehicle infotainment systems through transport-independent protocols that support Bluetooth, USB, and Wi-Fi connectivity. Furthermore, the research explores architectural design patterns that abstract vehicle-specific communication complexities through proxy components, enabling application developers to build scalable and reusable automotive applications. The proposed architecture focuses on four key dimensions: communication reliability, user experience optimization, driver safety compliance, and system scalability. Particular emphasis is placed on voice-command integration, heads-up display compatibility, application lifecycle management, and context-aware interface adaptation. The paper also evaluates protocol efficiency, latency optimization methods, and synchronization mechanisms that support real-time vehicle interactions while ensuring minimal driver distraction. A layered architectural framework is presented to demonstrate the interaction among Android mobile applications, AppLink proxy services, transport interfaces, and vehicle head units. Methodological analysis includes architecture modeling, protocol evaluation, implementation pattern assessment, and performance measurement using representative infotainment use cases such as navigation, media streaming, voice assistance, and vehicle status monitoring. The results indicate that Android AppLink significantly improves application interoperability, reduces integration complexity, and enhances user engagement while maintaining automotive safety standards. Additionally, the proxy abstraction layer demonstrates improved maintainability and portability across heterogeneous vehicle ecosystems. The findings suggest that Android AppLink serves as an effective foundation for future connected vehicle applications, particularly when integrated with emerging technologies such as cloud-based automotive services, edge computing, artificial intelligence-driven assistants, and Vehicle-to-Everything (V2X) communication frameworks. The study contributes practical implementation guidelines and architectural recommendations for researchers, automotive software engineers, and infotainment platform developers seeking to design robust connected vehicle ecosystems.
Copyright (c) 2023 International Journal of Emerging Trends in Computer Science and Information Technology