International Journal of AI, BigData, Computational and Management Studies
AI-Driven Business Intelligence Automation: Integrating Data Engineering, Auto-BI, and Large Language Models
Business intelligence (BI) systems are important as they aid in assisting organizations to convert huge amounts of unprocessed data into insights that can be used to make strategic and operational decisions. Nevertheless, the conventional BI systems tend to employ manual data preparation, inert dashboards, and a set of analytical models, which restrict their capability to be adapted easily to the extensive and ever-evolving business contexts. With the continuous growth of enterprise data ecosystems in terms of volume, velocity, and variety, the need to process smart and automated analytics solutions has grown dramatically. The traditional BI systems also demand specialized technical skills in fields like data engineering, SQL queries and dashboards development which pose a barrier to the business users who require at the right time and in an accessible format the information to make the needed decisions. This work suggests an artificial intelligence-based Business Intelligence automation system that combines the recently developed data engineering pipelines, automated Business Intelligence (Auto-BI) functionalities and Large Language Models (LLMs) to facilitate scalable and intelligent analytics. The suggested architecture will bring a multi-layers system of data ingestion and transformation, automated analytics, which would detect KPI and generate dashboard, and insight generation component powered by the LLM which provides the ability to query it in natural language and generate analytics. The diagram is tested with a billion scale analytical workloads to determine the enhanced efficiency of queries, automation, and insight generation accuracy compared to the traditional BI systems. The experimental results suggest that the suggested strategy decreases the effort of developing manual BI systems, speeds up the process of making insights, and improves accessibility with the help of a natural language interface. Altogether, the framework shows that the combination of AI, machine learning, and the LLM technologies can transform conventional BI infrastructures to become intelligent, automated decision-support systems in the modern ventures.
Multi-Cloud Data Federation Models for Unified Business Intelligence Insights
Multi-cloud data federation has emerged as a pivotal approach for enterprises seeking unified business intelligence (BI) across distributed cloud data sources. This paper presents a comprehensive study of architectures and techniques that enable data virtualization, real-time data access, and interoperability across cloud providers. We describe an academic-grade architecture for federated query engines and semantic abstraction layers that integrate heterogeneous cloud databases and data lakes into a unified virtual data layer. Implementation details of federated query processing, data integration platforms, and semantic metadata management are discussed, with emphasis on query decomposition, source connectors, and global schema mapping. We evaluate the design through analysis of query optimization strategies and a case study of a federated query engine spanning multiple public clouds. The results demonstrate that intelligent federation (e.g., push-down of operations to sources, caching, and distributed execution) can significantly reduce cross-cloud data movement and query latency, improving performance for real-time analytics. Key challenges – including network latency, distributed query optimization, security and access control, schema heterogeneity, and compliance – are examined in depth. We discuss practical solutions and best practices to mitigate these issues, such as caching strategies, learned federated optimizers, and unified identity management. The paper’s contributions provide a foundation for building multi-cloud data federation models that deliver unified BI insights with the flexibility of data virtualization and the rigor of enterprise data governance.
Unified Identity And Access Management (IAM)
Designing Financial Instrument Control Mechanisms for Pre-Shipment Order Execution
In export‑oriented and regulated business environments, organizations are required to mitigate financial exposure by securing customer obligations through pre‑shipment financial instruments such as Bank Guarantees (BG) or Security Deposits (SD). Although Oracle E-Business Suite (EBS) R12 Order Management (OM) provides comprehensive capabilities for order processing and credit management [1], it does not natively support instrument-level governance of externally issued financial securities across the sales order lifecycle. Enterprise systems literature emphasizes the importance of embedding preventive controls within transactional workflows [3][6], yet practical implementations often rely on downstream or manual reconciliation mechanisms. This paper proposes a novel functional design pattern for integrating pre‑shipment financial instrument control directly into Oracle EBS R12 Order Management. The contribution lies in the introduction of a centralized BG/SD repository tightly coupled with the sales order booking process, enabling deterministic validation, automated balance consumption, systematic release upon order cancellation, and end‑to‑end audit traceability. The framework was designed to operate within seeded OM workflows, thereby minimizing disruption while significantly enhancing preventive financial control. From an enterprise systems perspective, this work addresses a recurring and largely undocumented control gap between financial risk instruments and operational order processing in Oracle E-Business Suite implementations. The proposed design pattern is applicable across industries and geographies and has relevance for organizations operating regulated, high-value order fulfillment models. By formalizing instrument-level financial sufficiency control within the order booking lifecycle, this contribution advances applied ERP practice beyond standard credit-based mechanisms.
Blockchain is an incredible invention, the brainchild of an individual known by the pen name, Satoshi Nakamoto. But since then, it has evolved into a technology which holds a vision greater and better than the greatest of creations in the field of technology. A Blockchain is an incorruptible and non-tamperable public digital ledger of data collected through a network. It is how this information is recorded that provides Blockchain its ground-breaking potential. Thus, Blockchain technology provides integrity, authentication, confidentiality and security to everyone on the chain. The Blockchain network has no central authority or governance which is the very definition of a decentralized system. Since it is a distributed and an immutable ledger, the information in it is open for everyone to view. The legal industry has ample of features to gain from the Blockchain by integrating their existing software with Blockchain. One of the main issues of judicial systems today is the management and handling of the evidences. From the time of collection by the court it undergoes a series of ownership and scrutinizing and may lead to manipulations that may be deceitful. Also, the current process is entirely paper based processes and acting on single database system which makes it a single- server model. All the data being stored on to a single server makes the system vulnerable on being prone to attacks and can lead to permanent data loss and not to mention making the process slow. For that purpose, this project deals with successfully embedding Blockchain into the judicial systems for making it even faster, secure and making the people possessing the evidence accountable for their deeds which ultimately leads to provide equal justice to all. Our prototype is entirely developed on the Ethereum private network.
Energy-Efficient AI Inference at the Edge: Optimizing Semiconductor Hardware for Small Language Models
The rapid expansion of artificial intelligence applications across mobile devices, Internet of Things (IoT) platforms, and embedded systems has intensified the demand for efficient on-device inference. While large language models have demonstrated remarkable performance in natural language processing tasks, their computational and energy requirements make them impractical for deployment in resource-constrained edge environments. Small Language Models (SLMs) have therefore emerged as a promising alternative for enabling localized intelligence while maintaining manageable computational footprints. However, achieving efficient inference for these models remains dependent on the capabilities of underlying semiconductor hardware and the effectiveness of hardware-aware optimization strategies. This study examines the design considerations necessary for enabling energy-efficient inference of small language models on edge computing platforms. The paper analyzes how semiconductor-level architectural features such as neural processing units, specialized tensor accelerators, and optimized memory hierarchies influence inference latency and energy consumption. In addition, the work investigates model optimization techniques including low-precision quantization, parameter pruning, and hardware-aware scheduling that allow language models to operate efficiently on embedded processors and dedicated AI accelerators. A system-level framework is proposed that integrates semiconductor hardware capabilities with model compression techniques to improve inference efficiency without significantly degrading predictive performance. The study further evaluates performance characteristics across representative edge hardware platforms using metrics such as energy consumption per inference, inference latency, and throughput. The findings indicate that coordinated optimization across model architecture and semiconductor hardware design can significantly reduce energy requirements while sustaining real-time processing capabilities. These results highlight the importance of hardware–software co-design in enabling scalable and sustainable deployment of language models in edge environments. The proposed framework provides practical guidance for the development of next-generation edge AI systems capable of supporting language-based applications with improved energy efficiency and operational autonomy.
Edge–Cloud Continuums for Latency-Sensitive Tasks
Latency-sensitive applications such as autonomous driving, augmented reality, and real-time industrial control increasingly exceed the performance limits of traditional cloud infrastructures. To address these constraints, recent research highlights the emergence of edge–cloud continuums that distribute computation across heterogeneous layers to reduce communication overhead, improve responsiveness, and enhance reliability [16, 19]. Edge computing brings processing closer to data sources, while cloud resources provide large-scale computation and centralized intelligence, forming a hybrid architecture capable of supporting strict end-to-end latency requirements [15, 1]. However, ensuring optimal task offloading, dynamic resource allocation, secure data transmission, and QoS guarantees across distributed nodes remains an open challenge. Existing studies demonstrate that effective orchestration requires jointly modeling network conditions, workload characteristics, and application-level latency budgets [6, 12]. This paper surveys architectural models, scheduling strategies, optimization frameworks, and emerging trends enabling robust edge–cloud integrations for latency-sensitive tasks. Furthermore, it identifies research gaps in real-time prediction, cross-layer optimization, and scalable multi-tenant orchestration that must be addressed to support the next generation of ultra-low-latency systems.
Optimizing Continuous Integration and Continuous Deployment (CI/CD) Pipelines: Strategies, Tools, and Performance Metrics
Continuous Integration and Continuous Deployment (CI/CD) have become central practices in modern software engineering, enhancing development velocity, reliability, and scalability. However, optimizing CI/CD pipelines to minimize latency, reduce resource usage, and improve deployment stability remains a critical research challenge. This study examines optimization techniques, tools, and architectural patterns for CI/CD systems, drawing upon literature from 2015–2021. Through comparative analysis of major CI/CD tools (Jenkins, GitLab CI, Travis CI, CircleCI), the paper explores methods to improve build efficiency, testing automation, and deployment workflows. Results suggest that pipeline optimization depends on three core factors: automation maturity, infrastructure scalability, and feedback loop efficiency. The paper concludes with recommendations for performance tuning and integrating machine learning-based optimization within CI/CD environments.
Addressing Technology Challenges in Banking and Financial Institutions Using AWS-Native Architectural Patterns
Banking and financial institutions operate within highly regulated, security sensitive, and complex operational environments. The convergence of legacy system constraints, cybersecurity threats, regulatory mandates, and customer demand for real-time digital services requires a strategic approach to cloud transformation strategies. This paper presents an implementation-driven analysis of enterprise banking challenges and maps them to AWS-native architectural patterns aligned with industry frameworks such as the AWS Well-Architected Framework, and NIST Zero Trust Architecture. The paper uses examples from multi-account setups in regulated industries to create a model that links challenge-to-pattern in areas like modernization, governance, identity, observability, FinOps, disaster recovery, and AI governance. Observational insights and architectural trade-offs are assessed to provide a balanced, practitioner-oriented yet academically grounded contribution.
Real-Time Data Engineering for Large-Scale Supply Chain Network Optimization: A Framework for Petabyte-Scale Analytics
Modern supply chain networks generate unprecedented volumes of operational data that, when properly analyzed, can reveal significant optimization opportunities across diverse industry sectors. This paper presents a comprehensive framework for implementing real-time data engineering solutions that enable large-scale supply chain network optimization, capable of processing petabyte-scale datasets within operational time constraints. The proposed three-layer architecture integrates data processing, candidate analysis, and aggregation components to enable data-driven network design decisions across retail, manufacturing, healthcare, and logistics industries. Through systematic implementation of distributed computing architectures, real-time processing paradigms, and advanced analytics methodologies, the framework demonstrates measurable improvements including 15-35% reduction in transportation costs, 20-40% improvement in service levels, and achievement of 90% automation in previously manual processes. The solution addresses critical challenges in supply chain visibility, real-time decision-making, and network reassignment analysis while maintaining scalability across diverse operational environments. This research contributes to the growing body of knowledge in supply chain analytics by bridging the gap between theoretical optimization models and practical implementation at enterprise scale, establishing design principles for scalable supply chain optimization systems that transform traditional reactive approaches into proactive, evidence-driven strategies.
Chatbots vs AI Agents: The Shift toward Multi-Step Automation in Sales Support Workflows
Sales organizations rely on automation tools to handle repetitive customer interactions, lead qualification, and support tasks. Traditional chatbots execute predefined scripts effectively for straightforward queries but struggle with complex, multi-step workflows requiring contextual understanding and adaptive decision-making. AI agents represent an evolution beyond chatbot capabilities, employing large language models and reasoning frameworks to autonomously manage multi-stage processes including deal coaching, opportunity analysis, and cross-system task orchestration. This paper examines fundamental differences between chatbots and AI agents in sales support contexts, describing when each approach delivers value and how organizations can transition from scripted automation to agentic workflows. The analysis covers implementation considerations, use case selection criteria, and practical frameworks for deploying AI agents that handle complex sales workflows while maintaining appropriate human oversight.
Multi-Cloud Governance with Azure Arc and Lighthouse
Enterprises are increasingly adopting multi-cloud strategies to leverage the strengths of multiple providers while maintaining agility, resilience, and compliance. However, managing identities, policies, and operations across diverse environments poses significant challenges. Microsoft’s Azure Arc and Azure Lighthouse address these challenges by extending Azure’s governance, security, and management capabilities across hybrid and multi-cloud infrastructures. This paper explores the architecture, automation frameworks, and governance models enabled by Azure Arc and Lighthouse. It demonstrates how organizations can achieve unified policy enforcement, centralized monitoring, and operational consistency across clouds using Azure-native tools. Through experimental modeling and real-world use cases, it highlights measurable improvements in compliance efficiency, operational overhead reduction, and scalability.
Data Quality Contracts for Multi-Cloud Healthcare: Semantic SLAs with Automated Remediation
Multi-cloud healthcare platforms distribute ingestion, transformation, and analytics across Amazon Web Services, Microsoft Azure, and Google Cloud Platform to improve elasticity, regional resilience, and regulatory alignment. In practice, these benefits are frequently undermined by silent data-quality failures schema drift, terminology misalignment, freshness regressions, and broken references that propagate across heterogeneous pipeline components and destabilize clinical analytics and downstream AI systems. Existing approaches remain largely reactive: static checks and ad hoc expectations detect issues after propagation, while cloud provider SLAs emphasize infrastructure availability rather than semantic correctness or temporal fitness-for-use. This paper proposes Data Quality Contracts (DQCs) as semantic SLAs operationalized as SLOs with error budgets for multi-cloud healthcare data products. A DQC is a versioned, machine-readable agreement between producers and consumers specifying: (i) FHIR-aware HL7 semantic constraints (profiles, cardinalities, terminology binding), (ii) temporal objectives (freshness and end-to-end latency), (iii) completeness and referential integrity targets, and (iv) error budgets that bound acceptable quality loss and trigger escalation. We introduce a cloud-agnostic Quality-as-a-Service (QaaS) control plane that manages contract lifecycle and error-budget accounting while enforcing contracts federatedly at ingestion boundaries to minimize cross-cloud protected health information movement. When violations occur, the control plane orchestrates policy-driven remediation workflows schema reconciliation, terminology synchronization, prioritized replay, cache invalidation, and referential repair with approval gates for high-severity or low-confidence actions. We evaluate a prototype using a synthetic FHIR-compatible workload of 750,000 patients generated with Synthea across three clouds over 45 simulated days, with data-quality chaos injections spanning schema drift, vocabulary misalignment, freshness degradation, completeness regressions, and referential integrity faults. Relative to three baselines (reactive monitoring, expectations-based validation, and FHIR validator workflows), DQCs reduce mean time to remediation from 168.6 to 10.2 minutes (p<0.001), reduce false-positive alert rate from 26.6% to 14.3% (p<0.001), maintain 93.8% aggregate contract compliance during chaos, and automatically resolve 82% of violations. A 12-month operational cost model (assumptions in Section VIII) indicates reduced incident-driven toil and downtime exposure versus reactive approaches. These results suggest that contract-driven, semantics-aware governance with error budgets and automated remediation provides a practical foundation for trustworthy multi-cloud healthcare analytics and AI.
Machine Learning for Smart Cities and Urban Analytics
The rapid urbanization of the global population has placed unprecedented demands on city infrastructure, resource management, and service delivery. Smart city initiatives aim to leverage technology and data to optimize urban living, enhance sustainability, and improve the quality of life for residents. Machine learning (ML), as a core component of data-driven intelligence, has emerged as a transformative tool for analyzing complex urban systems and enabling predictive, adaptive, and autonomous decision-making. By processing vast volumes of heterogeneous urban data—including traffic flows, energy consumption, environmental sensors, social media activity, and public services usage—ML models can uncover patterns, forecast trends, and support real-time interventions. This article provides a detailed exploration of machine learning applications in smart cities, covering urban analytics, transportation and mobility management, energy and resource optimization, environmental monitoring, public safety, and citizen engagement. It also addresses technical challenges, data privacy concerns, ethical implications, and emerging trends such as federated learning and edge intelligence. The integration of machine learning into urban systems promises to make cities more efficient, resilient, and responsive while fostering sustainable and equitable urban development.
Edge AI and On-Device Machine Learning Optimization
The proliferation of connected devices, sensors, and intelligent applications has led to an unprecedented growth in data generation at the network edge. Traditional cloud-centric artificial intelligence architectures, while powerful, face limitations related to latency, bandwidth consumption, privacy concerns, and operational costs. Edge AI has emerged as a transformative paradigm that shifts computation and machine learning inference from centralized data centers to local devices such as smartphones, IoT sensors, embedded systems, autonomous vehicles, and industrial controllers. By enabling on-device machine learning optimization, Edge AI systems process data closer to its source, delivering real-time intelligence, enhanced privacy, and improved energy efficiency. This article presents a comprehensive and detailed exploration of Edge AI and on-device machine learning optimization, examining architectural foundations, model compression techniques, hardware-software co-design, privacy-preserving mechanisms, deployment strategies, and real-world applications. It further discusses scalability challenges, security considerations, energy constraints, and future research directions shaping decentralized intelligent systems. Through in-depth analysis, this work highlights how Edge AI is redefining scalable artificial intelligence by enabling efficient, secure, and responsive machine learning directly on resource-constrained devices.
Multi-Agent AI Architectures for Automated Customer Service Management Systems
An increasing number of companies have recently adopted automated systems to manage their Customer Service Department. Such systems typically consist of software applications capable of supporting operations such as online booking or client support. More recently, systems also include human-like conversational agents designed to manage tasks with users through spoken or written natural languages. More sophisticated systems support complex and multi-turn conversations thanks to the cooperation of several intelligent modules working together. These systems are usually designed as Multi-Agent Systems, where Artificial Intelligent modules called agents work together to achieve a specific objective. The growing complexity of dialogue-based tasks requires dialogue management modules to consider the agents’ speech, non-speech, and contextual behaviour to better infer users’ intents and to adopt appropriate strategies during human-agent interactions. Recent papers have proposed a complete architecture for this kind of system, putting special attention on the interaction among the agents during the task execution phase. The focus has been on defining the communication language between the agent and a set of modules that perceive the agent environment and recognize the user’s intent in natural language interactions. In these new architectures, the complexities in the processing of speech and contextual behaviour properties are delegated to these specific agents.
Cloud-Based Big Data Observability Frameworks for Healthcare Analytics Platforms
Cloud computing platforms for big data analytics, such as Amazon Web Services, Google Cloud Platform, or Microsoft Azure, have entered a mainstream growth phase, yet despite the enormous market demand, their effective operation remains a challenging task. Monitoring and observability of big data workloads needing support for dynamic and complex infrastructure represent an important area of research. Healthcare analytics platforms are particularly demanding in this respect, as workloads are commonly performing computations over patient data, which introduces additional requirements. During the last few years, several frameworks have been proposed to address various observability needs for cloud-based platforms, yet information on those approaches remains scattered. This survey work provides a structured overview of definitions, architectural patterns, frameworks, and tools, along with special focus on healthcare observability requirements. Furthermore, the discussion identifies potential areas for future investigation. The cloud observability area is still evolving, making it necessary to review the state-of-the-art, identify gaps, and propose a research agenda. Foundation work covers an analysis of core concepts and architectural patterns for big data observability in the cloud, followed by the examination of requirements driven by healthcare workloads. These aspects have laid the groundwork for a survey of existing observability frameworks and tools tailored to cloud platforms, with special emphasis placed on monitoring telemetry collection, distributed tracing, and performance management. Finally, additional data management considerations are discussed before the identification of architectural patterns that address the specific observability demands from healthcare data processing workloads.
Metric-Driven Automatic Software Vulnerability Detection Using Feature Extraction Pipelines and Interpretable ML
Software vulnerability detection has progressed from signature-driven static analysis toward learning-based detection over code, build, and operational signals. However, many machine-learning (ML) vulnerability detectors remain difficult to operationalize in real enterprise pipelines because they (i) rely on brittle or opaque feature representations, (ii) provide limited traceability from predictions to security-relevant metrics and engineering controls, and (iii) under-support governance requirements such as auditable decision pathways, risk thresholds, and continuous integration and delivery (CI/CD) gating. This manuscript proposes a metric-driven framework for automatic vulnerability detection that explicitly couples (a) a modular feature-extraction pipeline spanning program semantics and software delivery telemetry, (b) a unified risk metric schema grounded in vulnerability taxonomies and operational signals, and (c) interpretable ML for actionable explanations at multiple levels of granularity. The core idea is to treat vulnerability detection as a metric-aware learning problem: the detector outputs a probability of vulnerability and an aligned, human-auditable rationale mapped to metrics such as weakness category, severity tier, change-risk indicators, and runtime observability signals. The framework integrates transformer-based code representations and graph-based program semantics with explanation methods to produce transparent triage artifacts suitable for DevSecOps and compliance-constrained domains. A deployment-oriented methodology is presented, including feature engineering, model training, explanation generation, CI/CD integration, and evaluation protocols that emphasize decision utility in addition to predictive accuracy.
Artificial Intelligence in Human Capital Management: A Comprehensive Framework for Intelligent Workforce Systems
Human Capital Management (HCM) systems have historically evolved from administrative record-keeping platforms into enterprise decision systems. By 2023, advances in artificial intelligence (AI), machine learning, and conversational computing enabled a paradigm shift toward intelligent workforce ecosystems capable of autonomous decision support, predictive workforce planning, and adaptive employee experiences. This manuscript proposes a comprehensive framework for integrating AI into HCM environments through intelligent agents, data-centric architectures, and enterprise automation models. Unlike traditional HR automation, AI-enabled HCM systems operate as cognitive platforms capable of continuous learning from organizational behavior patterns. The study analyzes contemporary enterprise implementations, market trends, system architectures, algorithmic approaches, governance considerations, and deployment strategies relevant to 2023 technology maturity levels. A reference architecture for AI-native HCM platforms is introduced, followed by implementation models addressing employee lifecycle management, managerial decision augmentation, workforce analytics, and organizational intelligence. The work contributes a unified perspective bridging HR domain knowledge with artificial intelligence engineering practices, enabling scalable, ethical, and enterprise-ready intelligent workforce management systems.
From Linear Logistics to Neural Supply Chains: Predictive Machine Learning and the Rise of Autonomous Supply Chain Intelligence
Global supply chains are increasingly exposed to systemic volatility arising from geopolitical disruptions, demand uncertainty, and structural fragilities embedded in traditional linear logistics models. Conventional enterprise planning systems, grounded in deterministic forecasting and historical data extrapolation, have proven inadequate in environments characterized by rapid change and high complexity. This article reconceptualizes the contemporary supply chain as an intelligent, networked system driven by Predictive Machine Learning, Generative Artificial Intelligence, and autonomous decision architectures. Adopting a conceptual and industry-anchored analytical approach, the study synthesizes advances in feature-based forecasting, real-time data sensing, software-defined operations, and AI-enabled automation to explain the transition from reactive supply chain management toward anticipatory and self-healing logistics networks. Drawing on evidence from automotive and manufacturing ecosystems, the article illustrates how predictive analytics, generative models for unstructured data, and embodied AI systems jointly enhance end-to-end visibility, demand accuracy, inventory optimization, and operational resilience. The findings contribute to supply chain theory by reframing logistics as an adaptive intelligence system rather than a sequential flow of activities, while also offering practical insights for enterprises seeking strategic value from AI adoption. The study further highlights implications for emerging economies, particularly in the context of digital infrastructure and talent development. Overall, the article positions AI-driven supply chains as a foundational mechanism for achieving autonomous, resilient, and scalable global commerce.
Cloud-Enabled AI Contact Centers in Oncology Care
This article describes the design and deployment of cloud-enabled AI contact centers to support oncology practice. It also describes the unique technology-enabled engagement needs of oncology practices, such as supporting chemotherapy cycles, radiation therapy, symptom monitoring, and polypharmacy, and why temporally aware and risk-stratified patient engagement systems are not merely desirable but are, in fact, essential to the practice of oncology. Technical considerations include AI-based triage algorithms optimized to the stage of treatment, toxicity, and laboratory thresholds, balancing the need for early intervention with minimizing unnecessary use of emergency resources. Bidirectional integration with electronic health records (EHR) is discussed as a foundational strategy for the predictive routing of immunocompromised and higher-acuity patients for appropriate clinical escalations. Human-AI collaborative and augmented approaches to maintaining the human aspects of patient interaction and human judgment in oncology decisions for emergencies and other high-risk situations are also reviewed as an alternative to replacing high-risk oncology decision-making with automation. Considerations for scaling these approaches to regional cancer networks and to underserved and rural communities with high cancer mortality disparities are also discussed. The next step in synthesis crystallizes cloud-enabled AI oncology contact centers as transformative infrastructure for value-based care objectives and pathways to digital transformation of healthcare systems.
Oncology Contact Center Artificial Intelligence
Telehealth Oncology Patient Engagement Opus 4.5
Designing Data and Analytics Ecosystems for High Volume Transaction Processing Applications
Increasingly high-volume transaction processing systems in digital commerce, financial services, healthcare systems and cloud-native enterprise need unified architectures that can provide ultra-low-latency operations and also near-real-time analytics. Separating the OLTP and OLAP environment traditionally creates issues related to synchronization delays, multiple data replications, and schema inconsistencies as well as creating performance contention during mixed workloads. Even though Hybrid Transactional/Analytical Processing (HTAP) environments seek to resolve this gap, most are built on the idea of optimizing the database instead of solving ecosystem-wide issues such as the propagation of incremental changes between services, service-to-service coordination, the integration of analytics into the embedded platform, and system design centered on throughput. Consequently, loosely coupled architectures tend to exhibit issues of cascading latency, irregular state synchronization as well as reduced flexibility to workload changes. This paper proposes an architectural framework called the Throughput-Oriented Integrated Data and Analytics Ecosystem (TIDAE) as a single, architectural framework aimed at high-volume, mixed-workload systems. The suggested system brings together transaction-cherished processing, change data capture (CDC) entertained by streaming, incremental process organizations, efficient analytical storage, and inbuilt analytics displays in an orchestrated throughput-disposed developing. Formal throughput analysis, architectural modeling, and empirical benchmarking are used to prove that the framework supports 35-50% better throughput and about 40% less synchronization latency than traditional decoupled architectures, and meets analytically-challenged latency SLOs. The findings confirm the usefulness of isolated workload, progressive processing and scalable synchronization solutions in facilitating enterprise-caliber systems in smoothly incorporating analytics into operational pipelines with no disruption to performance or resiliency.
Integrated Operational And Analytical Systems
Mixed Operational And Analytical Workloads
High-Throughput Application Data Processing
Omnichannel AI-Enabled Healthcare Contact Centers: Enabling Seamless Patient Journey Continuity
At the same time, modern health systems seek to connect with patients across voice, SMS, web chat, email, patient portals, and mobile apps. Too often, however, these patient touchpoints are a series of disconnected silos that result in an inconsistent patient experience and suboptimal business processes. The article analyzes the architecture, functionalities, and organizational outcomes of omnichannel AI-enabled healthcare contact centers, which are contact centers that unify disparate channels into smart engagement ecosystems. The article conducts a systematic review of peer-reviewed literature and identifies the technical underpinnings of multichannel integration: (1) AI-enabled contextual intelligence engines that conduct intent classification and sentiment analysis using natural language processing (NLP), (2) unified patient data solutions that achieve 360-degree views of patients by enabling interoperable systems to connect, and (3) continuous context that avoids repeated information requests. In general, the evidence for process automation's effect on such things as accuracy of processing and timeliness to resolution, as well as patient experience, is strong and correlates well with patient-reported access quality and care coordination. Omnichannel AI infrastructure is a foundational capability health systems must possess to achieve patient-centric care delivery by enabling cross-channel continuity, interdepartmental care coordination, operational efficiencies from digital deflection of routine patient engagement inquiries, and differentiation in value-based care environments. As healthcare delivery continues to transition toward data-driven and personalized care, integrated communication architectures designed for delivery systems will be a key enabler for operational excellence and improved engagement outcomes․
The Rise of Embedded Finance: UX Challenges in Banking-As-A-Service (BaaS)
The rapid growth of embedded finance is propelling a major shift in financial services, allowing non-financial brands to integrate banking capabilities directly within their platforms and products. This approach, powered by Banking-as-a-Service (BaaS) infrastructure, enables seamless payments, lending, and other financial features delivering consumer value at precisely the right moment. However, delivering these embedded experiences presents significant UX challenges, including fragmented user journeys, regulatory complexity, and balancing accessibility with strong security. This paper explores the technological foundations, essential user experience principles, modern use cases, and ongoing barriers within embedded finance and BaaS. The findings highlight that overcoming UX hurdles will be key to driving financial inclusion, trust, and long-term value in this rapidly evolving domain.
Device Identity and Trust Establishment in Mass-Manufactured IoT Systems
Security in mass-manufactured IoT systems depends on device identity established during manufacturing and preserved across operational lifecycles spanning 10-20 years. Current deployments use identity models that fail under physical access, supply-chain compromise, or credential abuse. This paper analyzes device identity as an architectural primitive, examines failures in automotive infotainment, HVAC, and water-heating deployments, and presents a hardware-rooted, lifecycle-aware architecture that emphasizes establishing manufacturing-time trust, explicit revocation, and zero-trust operation.
Self-Healing Infrastructure - Predictive Automation for High-Availability Banking Systems
Modern banking platforms rely even more on intricate, cloud-native architectures to provide real-time, always-on financial services. Reliability of infrastructure has increased significantly, but operational downtime from unsafe changes, configuration drift and human error remain a leading cause of service disruption. This paper presents a prevention-first, self-healing infrastructure paradigm based on predictive automation, built-in governance and analytics-driven use of AI. Instead of concentrating on the reactive remediation, this approach is interested in proactive aversion of high-risk system states before they actualize and affect customers.
Publishing Financial Data in Multi‑Format Outputs: XML, JSON, CSV, APIs
In this paper, a governance-based structure of publishing financial data with a regular frequency across XML, JSON, CSV, and API interfaces are suggested. It claims that the problem of multi-format publishing is essentially a matter of semantics and control, and not a file-conversion issue. Whether it is a transformation or an operational data-quality performance, the study assesses the accuracy of transformation, schema governance, reconstruction integrity, and data-quality of operational information, based on the use of a canonical semantic data model as the foundation. Continuous quality measures are very useful as early warning signals of semantic and comparability risk. The paper also illustrates that APIs can act as delivery mechanisms as well as governance tools when regulated in the form of formal contracts. These results find that to have sustainable financial data disclosure, integrated canonical modeling, stringent schema control, reconciliation control, and ongoing quality monitoring are needed to provide trustful, regulatorially defensible, and consumer-useful multi-format financial reporting.
Reducing Supervisory Risk through Validation Frameworks: Designing Preventive Controls for CCAR Edit Checks and Data Quality Failures
Supervisory risk in Comprehensive Capital Analysis and Review (CCAR) programs increasingly arises not from capital inadequacy, but from weaknesses in data quality, validation execution, and the late discovery of reporting defects through downstream edit failures [1], [2]. Federal Reserve examinations repeatedly highlight deficiencies where institutions rely on post-aggregation reconciliation, manual review, or cycle-end remediation to identify issues that could have been prevented earlier in the reporting lifecycle. This paper examines preventive validation as a distinct supervisory risk control discipline and proposes a validation framework that embeds data quality enforcement and CCAR edit checks directly into reporting execution. Drawing on practitioner experience across CCAR, capital reporting, and regulatory validation programs, the paper introduces a preventive control model that shifts validation upstream, integrates supervisory intent into edit logic, and produces audit-ready evidence as part of normal processing. By treating validation as supervisory infrastructure rather than a post hoc quality function, large banking institutions can materially reduce supervisory risk, minimize repeat findings, and improve the sustainability of CCAR reporting outcomes.
The Evolution of Release Engineering: From Manual Deployments to Fully Automated Cloud Pipelines
Release Engineering (RelEng) has been a discipline that has experienced a revolutionary evolution as a fragmented manual deployment process to a fully automated, policy-driven cloud-native delivery pipeline. Dealing in a manual scripting-driven and ad-hoc build state once, contemporary release engineering features the use of continuity integration (CI), continuous delivery (CD), and infrastructure-as-code (IaC) frameworks to provide consistent, audit-capable, and repeatable software releases. This paper will discuss the history of release engineering, the technological facilitators, and implications on governance with reference to how the move to automated pipelines has re-established the manner in which the operations of the enterprise and regulated space in terms of efficiency, security assurance, and compliance verification. The study, filed out in terms of a Design Science Research (DSR) and comparative case study design, builds and tests a multi-layered framework, as Automated Release Governance Framework, which incorporates GitOps, Policy-as-Code, and AIOps to offer resiliency, traceability, and self-rectifying release workflows. The framework was tested on a hybrid environment and a cloud-native one based on Kubernetes, Jenkins, and GitHub Actions (with financial and telecom workloads simulation). The quantitative assessments showed that the number of release rollbacks had dropped by 72%, the mean time to deploy (MTTD) had been reduced by 65%, the number of manual interventions had reduced by 90%, and policy compliance validation latency was reduced by 58% with integrated automation policies. The qualitative data of the 20 respondents on the topic of DevOps and reliability experts highlighted the importance of the cultural change that comes with automation as significant as the technology itself. Companies that incorporated the concepts of governance and observability in their CI/CD systems recorded quantifiable gains in their reliability, auditability and productivity of their developers. This research paper concludes that release engineering has become a strategic governance discipline- it is the gap between development agility and operational assurance. Autonomous release engineering In the future release engineering will lie in autonomous release intelligence (ARI) when machine learning can organize release decisions, risk evaluation, and compliance checks in distributed systems dynamically.
Building Secure Enterprise Data Lakes on Azure: Governance, Compliance, and Scalability Challenges
The contemporary enterprise landscape is characterized by a paradox of abundance: while organizations possess more data than ever before, their ability to extract secure, compliant, and scalable value from this asset is frequently compromised by architectural and organizational inertia. The shift toward cloud-native environments, specifically the Microsoft Azure platform, has provided the raw technological capability to store and process information at an unprecedented scale. However, the industry has observed a significant trend where enterprise data lake initiatives fail not due to the limitations of Azure’s services, but because of a pervasive reliance on legacy data management paradigms. These legacy methods, which emphasize perimeter-based security, manual governance, and monolithic storage patterns, are fundamentally incompatible with the requirements of modern, regulated, and high-growth environments. Addressing these failures requires a departure from incremental modernization. Instead, organizations must adopt a "governance-by-design" framework that treats security and compliance as foundational engineering disciplines rather than elective add-ons. This report explores the core challenges of building secure enterprise data lakes on Azure, introducing the concept of governance debt, detailing the transition to zero-trust and identity-first security models, and outlining the architectural patterns such as the Medallion and Data Mesh models that enable sustainable scalability. Furthermore, it examines the role of automated governance tools like Microsoft Purview and anticipates the evolution of these platforms into intelligent, agentic systems by 2030.
Data Compliance and Regulatory Governance
Beyond the Algorithm: A Longitudinal Analysis of Data Heterogeneity and Clinician Trust as Determinants of Predictive Tool Adoption and Patient Outcomes in Personalized Medicine
The promise of predictive analytics in personalized medicine remains largely unrealized despite significant technological advances, as the translation of algorithmic innovation into routine clinical practice continues to encounter formidable barriers. This longitudinal study investigates the multifactorial determinants governing the adoption of a predictive big data algorithm for Parkinson's disease progression originally validated using heterogeneous, multi-source data from the Parkinson's Progression Markers Initiative within three academic medical centers over a 24-month period following implementation. Drawing upon foundational evidence that model-free machine learning approaches can achieve diagnostic accuracy exceeding 96% when applied to integrated imaging, genetic, and clinical data , we examine how data heterogeneity and clinician trust interact to influence both adoption patterns and subsequent patient outcomes. The study design incorporates mixed methods, including quantitative usage analytics from electronic health record integration logs, serial surveys measuring clinician trust dimensions across 147 neurologists and primary care providers, and structured observations of clinical workflow integration. Preliminary findings from listening sessions conducted with diverse stakeholder groups informed our conceptual framework, which recognizes clinicians as central intermediaries in the research-to-practice translation process. Results indicate that data heterogeneity operationalized as variability in data completeness, standardization across sources, and temporal continuity significantly predicts initial tool utilization (β = 0.42, p < 0.01), while clinician trust, particularly regarding algorithm transparency and alignment with clinical judgment, emerges as the dominant predictor of sustained adoption at 18 months (OR = 3.87, 95% CI 2.14–6.98). Notably, sites achieving higher adoption demonstrated modest but significant improvements in time-to-diagnosis confirmation (mean reduction 4.3 days, p < 0.05) and patient-reported quality of life measures at 12-month follow-up. However, the relationship between adoption intensity and patient outcomes was nonlinear, suggesting diminishing returns beyond optimal integration thresholds. These findings extend earlier work characterizing barriers to stakeholder engagement in big data research and address the critical translational gap between population-level analytic capabilities and individual patient benefit. The study contributes empirical evidence that technological efficacy alone is insufficient; rather, the successful implementation of predictive analytics in personalized medicine requires simultaneous attention to data infrastructure quality and the cultivation of clinician trust through transparent, participatory design processes.