European Advanced Journal for Emerging Technologies (EAJET)
Journal Summary The European Advanced Journal for Emerging Technologies (EAJET), p-ISSN 3050-9734, e-ISSN 3050-9742, is an international, peer-reviewed, open-access journal dedicated to publishing original research, review papers, and technical communications across engineering, applied sciences, and emerging technological domains. The journal covers areas including Artificial Intelligence & Machine Learning, Blockchain & Decentralized Systems, IoT & Smart Technologies, Cybersecurity & Digital Privacy, Cloud Computing, and Next-Generation Engineering Solutions. EAJET follows a rigorous double-blind peer-review process and adheres to publication ethics guidelines set out by COPE. The journal is published quarterly and provides immediate, unrestricted open access to all published content under a CC BY 4.0 license. EAJET is published by ESA Research and aims to bridge theoretical research with real-world technological application for a global academic and industry audience.
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Collaborative Multi-Agent RL for Adaptive Healthcare Decision-Making
Autonomous, real-time exchange and adaptation of healthcare data is critical for optimizing clinical decisions while respecting trust, safety, and privacy concerns. An approach using multi-agent reinforcement learning (MARL) is proposed, enabling patient-specific, inference-based, and standard-compliant data-sharing policies between agents positioned at junctures in patient journeys. Data agents support decision agents and policy agents—the former learning to optimize care pathways from historical patient data, while the latter define safe, accurate, and reliable operation zones for responsiveness-complexity trade-offs. Prioritizing clinical efficacy and local communication, independent MARL minimizes safety risk; centralised Joint-Action Learning mitigates non-stationarity in sample-efficient subproblems; hybrid coordination strategise scale. The system architecture remains modular, resilient, and audit-ready—all properties supporting integration into clinical information ecosystems and real-time data streams such that decision support and patient-specific clinical data-sharing governance can occur concurrently. Multiple avenues of privacy-preserving MARL research advance the prospects of real-time healthcare data compliance and quality. Recent focus on accessibility has included methods for safeguarding de-identification against re-identification attacks and calculating privacy risk before, during, and after sharing sensitive data, achieving compliance with local regulations such as the US Health Insurance Portability and Accountability Act. Further research is addressing privacy-preserving MARL for the de-identification of sensitive data shared across inference-based systems such as those driven by HL7 FHIR.
Copyright (c) 2023 Niklas Andersson (Author) https://creativecommons.org/licenses/by/4.0
AI-Driven Clinical Data Engineering for Real-Time Precision Medicine
Medicine is progressively evolving towards a personalized approach, and it soon may be possible to administer the right treatment to the right patient at the right time. Timely inference of mortality risk or the need for curative-intent therapy greatly influences decision-making in critical situations, such as in the intensive care unit and in the emergency department. Passive surveillance of patients who are constantly monitored and undergo frequent routine testing leverages this goal. Translational work encompasses clinical data engineering and the application of machine-learning algorithms to enable real-time systems that detect patient deterioration and support clinical decisions. The availability of clinical data stored in electronic health record systems has attracted great interest in automatic patient management, offering the potential for augmentation of clinical decisions or even automatic, risk-averse intervention. Timely and reliable decision-support systems are developed and validated, allowing for rapid inference of patients’ risk for various outcomes during the course of a hospital admission. Carefully crafted closing metrics determine when a system must produce a reliable prediction while remaining in “wait and see” mode otherwise. Such metrics assess not only accuracy but also the validity of a signed prediction. Consistency analysis during this phase emphasizes expected clinical behavior: predictions should not flip or wander over time and most patients should not enter an active waiting mode.
Copyright (c) 2023 Elena Petrova (Author) https://creativecommons.org/licenses/by/4.0
Automated Sanctions Screening and Compliance Data Platform
Increasing regulatory scrutiny has put anti-money laundering and counter-terrorist financing policies and related operational processes under pressure. Regulatory compliance requires continuous and automated monitoring and reporting for various legal requirements. In response to these challenges, and building on the foundation of cloud-native data engineering, a cloud-based framework for regulatory compliance is presented. At its core are cloud-based data screening pipelines that continuously and automatically monitor data with respect to risk indications, rule violations, sanctions lists, and complex business rules. To realise these pipelines and support other regulatory requirements, a cloud-native data engineering framework was developed, engineered, and defined. Keywords: Cloud-Native Data Engineering, Regulatory Compliance Automation, Continuous Compliance Monitoring, Cloud Infrastructure, Cloud Services.
Copyright (c) 2023 Carlos Mendoza (Author) https://creativecommons.org/licenses/by/4.0
LLM Agent Pipelines for Smart Customer Service Workflows
Integrating autonomous agents within service management and service desk systems requires defining an appropriate operational model, context, scope, and specification. Autonomous agents are self-contained LLM AI applications fulfilling tasks and roles without user intervention. Generative AI frameworks and pipelines are models that describe the operational and interaction stages needed to fulfil predefined tasks using AI technologies. Each stage performs a well-defined operation that contributes to a complete process, enabling service integration and/or support. Environment-specific or domain knowledge provides the agent with the necessary context and enables support or service desk workflow decision-making. Conversational user experience design defines the standard of user interaction with the AI agent. The proposed design explicitly maps the interaction and process stages within a generative AI pipeline. Service operation requirements inform the specific design scope, defining the basis for monitoring, maintenance, and reassessment of an LLMAI model used in an agent context. Framework performance and validation track essential operational characteristics and metrics required for modulating the service integration. The design has immediate application in customer service platforms and Cloud service marketplaces, where such models integrate autonomously with existing service portfolio offerings. The broader use case integrates LLMAI agents with service desk solutions used by organizations, enhancing the service desk environment, stimulating knowledge contributions, and enriching knowledge repositories.
Copyright (c) 2025 Elena Petrova (Author) https://creativecommons.org/licenses/by/4.0
Secure Cloud-Native Architectures for Generative AI in Enterprise Workflows
Generative artificial intelligence (GenAI) is a rapidly developing technology with potential for multiple applications, yet it is complex, resource-intensive, and prone to risks. Deployment of GenAI in enterprise workflow platforms requires approaches that enable secure operation while maintaining availability, reliability, and quality. A synthesis of cloud-native architectural patterns with contemporary risk frameworks provides insights into essential security aspects for GenAI. Findings indicate that careful consideration of the safeguards available for prompt injection mitigation, model inversion protection, and data privacy when developing GenAI within a service-mesh architecture can reduce the likelihood of future attack success or damage. Cloud-native generative artificial intelligence (GenAI) deployment in enterprise workflow platforms is increasingly common, especially for support documentation creation. However, safeguarding the system against attacks that target the availability, reliability, or data privacy of the service remains challenging. Leveraging cloud-native patterns of scalability, resilience, composability, portability, and observability can guide security-enhancing measures. Mapping the security-by-design concept to GenAI services within a service-mesh architecture identifies a range of security controls rooted in established identity and access management principles, the concept of security through obscurity, the defense-in-depth principle, and the auditing of logs and monitoring alerts.
Copyright (c) 2025 Yannis Ioannidis (Author) https://creativecommons.org/licenses/by/4.0
Dynamic Risk Policies for Data Centers Using Generative AI
Risk policies safeguard against system exposures, yet they can become obsolete owing to environmental change, temporal drift, or adjustment errors. In multilayered regulatory environments, the legal-mosaic risk management landscape poses additional difficulties. Formalization techniques form the foundation for automated synthesis. Adaptive policy generation enables tailored, up-to-date policy formulation that draws upon contextual knowledge and experience. Generative AI has been successfully harnessed for the development of software code, nuclear indices, wording for disclaimers, disclaimers, and marketing material. Despite the unsettled state of generative AI, it remains a powerful tool for language-based tasks and can logically and verbally reason at a high level with careful prompting. Irrespective of the generative AI model architecture for ChatGPT-style applications, it is fundamentally an LLM and ML methods applicable for those models are therefore valid. When cited for policy development, however, care has to be taken to ensure that internally developed policies conform to the organisations standards for such content. Generative AI–driven development of locally valid policies that comply with domain constraints can be achieved through prompt engineering that influences the model in a top-down manner. Comprehension of the policy numbers, context, and problem-influencing event elements are always accessible. The similarity of the style and intent of many Policies enables grouping by shared feature components. Constraints on variables in the knowledge structures of Regulatory and Domain-Integration Alignment Core can be encoded, and enforced in the synthesis process. The infrastructure has been built to support the synthesis of data governance and compliance policies for a complex multi-jurisdictional data environment.
Copyright (c) 2025 Hiroshi Tanaka (Author) https://creativecommons.org/licenses/by/4.0
AI-Driven Cloud Architecture for Real-Time Derivatives Risk and Collateral Optimization
Derivatives play an important role in modern finance, allowing one party to transfer risks to another willing to accept them. The counterparty risk that emerges from bilateral settlement is decisive, particularly in volatile markets—yet at the same time, the liquidity and funding of a bank and the derivatives markets are threatened by regulatory capital charges for potential future exposure. Therefore, timely and reliable estimates of future counterparty credit exposures, possible future collateral shortfalls, and their impact on funding liquidity are crucial for decision making. To meet these requirements in a cloud-native enterprise architecture for derivatives trading, the necessary monitoring capability needs to be enhanced using AIs with low latency that can ingest new data on market changes. Insights into emerging AI technology reveal its increasing use for high-risk, high-value applications with growing complexity and rapidly changing demand and supply. At the same time, cloud-native technology is establishing itself as the enterprise architecture of choice for many new and emerging applications. The two technologies form a promising combination, enabling real-time credit risk and liquidity analysis of derivatives trades and portfolios for timely decision support. Yet there is little structured guidance for combining them. To satisfy these tipping-point criteria, these AI applications need to deliver millisecond-level latency; gracefully scale up and down; and provide continuous operational resilience through fault isolation, redundancy, and failover across cloud regions and service providers. Without such characteristics, AI function cannot meet business needs.
Copyright (c) 2024 Carlos Mendoza (Author) https://creativecommons.org/licenses/by/4.0
Generative AI for Adaptive Fraud Intelligence in Cloud-Native InsurTech
Phishing, identity theft, fake claims, and other forms of fraud give rise to material losses and legal liability, making adaptive fraud intelligence a pressing requirement for InsurTech organizations. Such systems materially enhance deterrent capability, minimize Simon's busca reveladora supplies, and can suppress customer churn by transmitting behavioural data on fraud attempts. InsurTech provides a cloud-native environment for implementing adaptive capabilities at scale, with a dynamic threat landscape evolving alongside increasing data availability, privacy sensitivity, and collaboration with fraud actors. The combination of machine learning and service composability across organizations create new opportunities and render previous solutions inadequate. Generative AI can be tapped as the supply-side enabler to reduce the data requirements, while transfer learning and anomaly detection approaches lessen the total cost of ownership along the demand side. False positive and negative predictions come with their own search costs. The key objective is to develop an adaptive fraud-intelligence solution for InsurTech organizations in a fraud constellation ecosystem that is privacy preserving, can be deployed in a cloud-native architecture, and remains within an acceptable boundaries for government policy. Privacy and compliance enable improved process coverage by drawing in additional sources of data not normally accessible, including the transactions with the fraud actors themselves. Similiar conclusions apply to the total cost of ownership and search costs with fraud. When successful, these actions translate into a major operational advantage, providing not only lower delivery costs but also better detection accuracy and response times than competitors that are unable or reluctant to undertake such monitoring and analysis.
Copyright (c) 2024 Tanja Schultz (Author) https://creativecommons.org/licenses/by/4.0
Autonomous Frameworks for Real-Time IoT Data Engineering
Agentic AI frameworks for real-time streaming intelligence enable a novel approach to autonomous Internet of Things data engineering. The absence of fully autonomous real-time environments for Internet of Things data engineering is no longer attainable thanks to agent-based streaming intelligence. Many agent implementations have been proposed and implemented in different fields over the years. Where the agent's intelligence is triggered step by step by the incoming data, it is called streaming intelligence, and data engineering is the conversion of data from a lower state to a higher state. Agentic real-time streaming intelligence is now universally applicable in practice. Agentic means that the actions of the proposed technique can replace human actions, and real-time data streams are temporally ordered sequences of data. A data engineering process executable within the streaming intelligence domains has been implemented, and its performance for camera-based autonomous vehicle data in the VisualQA dataset using multimodal tai models with no background knowledge is provided as an example of its application. Although the seam for an end-to-end training is open, a minimal test seam is already available: free-label-class-classifier hints resident at the labels of single-frame-clear-image data comparisons against the data inside motion-blur labels inside. The seamless flow within agentic routing is open in all directions of the graph structure, enabling an inverse direction of input-output-exit for agent-based simulation across time. Ideal-response-bandwidth radius comparisons enable the visual-phrase data resolvers to check bandwidth of intrinsic surprise entering the visual-phrase stream.
Copyright (c) 2026 Michael Anderson (Author) https://creativecommons.org/licenses/by/4.0
Self-Directed AI Agents Orchestrating the Digital Supply Chain Lifecycle
In many industries, supply chains are undergoing new product introductions with shorter life cycles, growing product variety, and heightened customer expectations for service. Rising consumer and regulatory pressure for sustainable supply solutions reinforces the demand for greater flexibility, agility, and responsiveness. Supporting these diverse supply-side challenges requires appropriate architectural concepts, design primitives, and enabling technologies. Incorporating autonomous AI agents into digital technology solutions holds great promise for addressing these demands collectively. Unlike traditional digital solutions that represent agentless collections of orchestrator-driven features, technologies built around autonomous digital agents deploy an entirely different orchestration mechanism. Open digital networks of interconnected, contractually governed, and self-aware AI agents can sense context, decide automatically when to act, make and receive promises, negotiate collaboratively, and even resolve conflicts. Currently available agent technologies privately communicate within mission or product delivery teams. Extending these technologies to support contractually governed agent networks at scale through appropriate support processes and infrastructure has not yet been implemented and remains a future area of research. Defining a comprehensive conceptual foundation for agent-based orchestration reveals the many camera-ready research projects it supports. An increasing number of industry project applications covering manufacturing, logistics, and retail are proving the promise of this approach by demonstrably delivering real business value. Managed well, responsible AI can thereby satisfy the conflicting yet urgent demand for intelligent technology.
Copyright (c) 2024 Yannis Ioannidis (Author) https://creativecommons.org/licenses/by/4.0
Building Responsive Data Streams for Modern Healthcare Systems
Data-driven disruption in healthcare enables advanced analytical capabilities for near-real-time monitoring, prediction, and prevention. However, existing interoperability standards remain inadequate for large-scale operational systems. An innovative event-driven architectural pattern addresses the requirements for near-real-time healthcare data streaming and corresponding data models. Key streams in acute care and telemetry from medical devices illustrate the application of these concepts. Research demonstrates that many common event-driven architectural patterns well-known in business domains apply equally to near-real-time infrastructures in healthcare. Data-driven technologies are rapidly disrupting business and government sectors. Purpose-built data pipelines collect, curate, and stage data for near-real-time operational insights into customer and environmental factors. Emerging analytical techniques such as complex event processing, predictive analytics, and machine learning rely on these data streams for service innovation, business performance and resilience, fraud detection, and automated response. Similar capabilities are desirable in healthcare, whether for monitoring ICU patients, predicting hospital readmissions, preventing clinical deterioration, detecting adverse events, optimizing resource allocation, or curtailing the spread of disease. Nevertheless, the healthcare sector has not yet realized these advanced operational insights. The primary reason is the lack of sufficiently low-latency, scalable healthcare data streams. Drivers of change include research demonstrating suboptimal patient outcomes resulting from failure to act on existing evidence, the infectious nature of COVID-19, concerns regarding healthcare system capacity, and a growing risk of influenza-H1N1 co-infection.
Copyright (c) 2026 Pierre Moreau (Author) https://creativecommons.org/licenses/by/4.0
Experimental and Machine Learning Approaches for Discharge Estimation in Arched Labyrinth Weirs
A weir is a hydraulic structure installed across or parallel to an open channel to regulate or measure flow discharge. Weirs are classified based on shape, discharge characteristics, crest width, and crest type. Among these, the arched labyrinth weir is characterized by its extended crest length, achieved through arching and notches, which enhances its hydraulic efficiency. This study presents a comprehensive analytical and experimental investigation into the discharge characteristics of arched labyrinth weirs in open channels. The artificial neural network model demonstrated exceptional predictive performance, achieving a mean squared error of 2.84×10−7, a mean absolute percentage error of 0.000191, and a correlation coefficient approaching 1, highlighting its accuracy and reliability in discharge estimation.
Copyright (c) 2025 Ajmal Hussain, Mujib Ansari, Mohd Danish, Hazi Azamathulla (Author) https://creativecommons.org/licenses/by/4.0
Scalable Medallion Lakehouse with MLOps on Azure Databricks
This Medallion Architecture design and implementation leverages Databricks and Azure technology while aligning with MLOps principles to engender a comprehensive, scalable solution for enterprise data intelligence, a topic of increasing relevance across commercial sectors. As organizations expand their bases of data assets, the need for an integrated framework that supports both analytics and machine learning with a clear vision for data intelligence emerges as a priority. Today, machine learning is applied across many industries and is advancing rapidly. However, the majority of ML models remain on pilot status or just a few have been productized. Furthermore, their use, maintenance, and governance sprinkle a wide array of manual and poorly controlled actions. The Achilles heel of these initiatives that are such a big promise of added value is the implementation of a continuous integration/continuous deployment (CI/CD) flow. ML-Ops is the answer to these impediments and allows exploring ways to build ML products properly.
Copyright (c) 2024 Sophia Martinez (Author) https://creativecommons.org/licenses/by/4.0
Generative AI-Driven Financial Crime Compliance via Event Streaming
Smart integration of generative AI with event-driven streaming architectures offers a promising avenue for enhancing Financial Crime Compliance. Presenting an approach for Intelligent Financial Crime Compliance (IFCC), this paper defines, delineates, and discusses the concept before illustrating its application in the context of strengthening detection of money laundering (ML) and terrorist financing (TF) activities. Generative techniques for detection, data risk/scoring, and anomaly identification provide accurate, explainable, actionable, and adaptive insights into potential compliance breaches. Evaluation frameworks tailored to the streaming regimes of compliance workflows address the urgency and operational nuances associated with real-time AML/CFT surveillance. Fed by diverse continuous data feeds and often governing timely responses to regulatory penalties, Financial Crime Compliance (FCC) processes traditionally viewed through a descriptive lens comprise a rich target for AI-enabled automation within a data-driven paradigm. Gaps in rule-based detection technologies—designed in hindsight for known patterns of illicit behavior or anomalies and lacking continual risk calibration—emphasize the need for a data-centric streaming architecture. The intensifying real-time nature of ML and counter-terrorism financing activities further motivates broad streaming detection. At the same time, the availability of such vast data resources in near real-time offers potential to improve compliance decision-making, including for risk scoring, alert generation, and near real-time data lineage.
Copyright (c) 2025 Benjamin Clark (Author) https://creativecommons.org/licenses/by/4.0
A Unified Big Data Framework for Predictive Healthcare Analytics
Healthcare predictive analytics involves the integration of large data volumes from heterogeneous sources together with AI algorithms to yield predictions in the form of risk levels, timelines, or progression forecasts. The ClintelliBase architecture synthesizes these components to formalize the entire predictive healthcare lifecycle and its generalization into other healthcare domains. It encompasses data ingestion, governance, compliance, security, storage, analysis, external client access, and the execution of predictive models. The proposed predictive healthcare methodology complements the formative work, empowering the ClintelliBase architecture to address predictive healthcare as a whole. Formative work developed predictive models for hospital readmission risk and disease progression forecasting for several diseases, converging the findings with that of other works and the underlying architecture to provide a comprehensive overview. Altogether, predictive healthcare has been demarcated into a dedicated natural SLOPE space, establishing supporting elements relative to the two specific innovations on error and clinical utility.
Copyright (c) 2023 Alexander Miller (Author) https://creativecommons.org/licenses/by/4.0
Neural Demand Forecasting for Modern Retail
Retail demand forecasting is crucial for intelligent decision-making, resource allocation, and meeting customer expectations. Algorithms based on machine learning or deep learning have improved the state of the art. However, companies can fall short of realized benefits due to data engineering deficits. Consequently, data ingestion and preparation need to be in place to support such algorithms in production environments. Data acquisition, quality and governance, lineage tracking, and compliance play critical roles. Robust model backtesting and validation, together with systematic monitoring and alerting, are equally vital. The requirements are presented, and an architecture designed around them is described, supporting an ensemble hybrid forecasting algorithm. These capabilities represent a significant step toward a complete AI-augmented demand-forecasting solution. Demand forecasting influences almost all intelligent decisions in retail operations, from procurement and distribution to store staffing and marketing. Accurate forecasting enables optimal resource allocation and alignment with demand, thereby satisfying customer expectations. Many different quantitative forecasting techniques have been developed over the years, ranging from traditional statistical approaches to more recently developed machine learning or deep learning algorithms. The accuracy of demand forecasts has gradually improved because machine learning and deep learning algorithms have been executed in business environments. Nevertheless, the investment in AI or AI-capable algorithms does not guarantee a return in the form of improved forecasting accuracy. While investment in algorithms is part of the equation, other factors come into play during the tedious—yet often underestimated—task of supply and feature engineering.
Copyright (c) 2026 European Advanced Journal for Emerging Technologies (EAJET)
Data Sovereignty Across Commerce Borders
Cross-border e-commerce (CBEC) data governance encompasses frameworks, processes, and mechanisms for data management and usage by an enterprise and its stakeholders. It has become a significant trend and research theme in the era of the rapid international expansion of e-commerce businesses. Since 2016, cross-border e-commerce data governance has been a critical area of study across the international community. Existing contributions, however, primarily focus on commercial and operational services of e-commerce, without consideration of supporting data governance architectures. A scalable data governance architecture is essential to enabling sustainable e-commerce development. A set of architectural principles and guidelines has been developed based on the definition and components of cross-border e-commerce data governance. These emphasize modular building blocks and service-oriented governance components and enable scalable, maintainable, and evolving data governance in hybrid, multi-cloud, and cross-border e-commerce deployments. To validate these architectural findings, a pictorial approach to cross-border e-commerce case description has been applied, and reference architectures for cloud platforms supporting data governance in CBEC have been established.
Copyright (c) 2025 European Advanced Journal for Emerging Technologies (EAJET)
Stream-First Fraud Defense in Distributed FinTech Systems
Fraud in FinTech systems, namely in-banking and insurances, represents a complex and multifactorial phenomenon that has been further amplified by social distancing and confinement measures to cope with the COVID-19 crisis. According to selected reports, fraud in Europe alone is estimated at 2.2 billion Euros for 20224 and is expected to continue its otherwise upward trend in different markets. To mitigate risks and minimize losses, private and public organizations invest continuously on fraud monitoring systems. However, fraud crime continuously adapts its detection methods and hence, systems are still required to evolve in the fraud investigation and detection area. To facilitate benchmark and comparison of different approaches, the application of an open-source distributed and scalable data engineering model is applied to create fraud monitoring systems in a FinTech environment. Fraud monitoring systems in distributed environments require different and specific execution models in an integrated and open-source environment. Data engineering platforms allow integration of different data sources and types with the possibility of executing distributed data engineering tasks in batch, streaming, micro-batch and real-time modes. An integrated analytical environment is used to support fraud monitoring in enterprises outside the banking sector. GDPR and data anonymization measures are used to guarantee the data privacy with a clear impact of the climate change index in a fraud model. Detection of anti-money laudering events related to COVID-19 is also presented and discussed. Finally, a case study in the European Banking Federation area is further analyzed.
Copyright (c) 2025 European Advanced Journal for Emerging Technologies (EAJET) https://creativecommons.org/licenses/by/4.0
Cloud-Based AI Chatbots for HR Automation
Cloud-integrated Artificial Intelligence (AI)-enabled chatbots can play a pivotal role in enhancing the service delivery and operational performance of Human Resources (HR) departments in organizations. The application of chatbots in HR has gained considerable traction recently, particularly as part of the shift to HR service delivery through common service centers. The cloud paradigm offers an excellent environment for a wide array of automated applications. Nevertheless, many existing implementations only partially automate specific functions. A cloud-integrated architecture allows for multiple HR functions to be automated through a single cloud-enabled chatbot, thereby optimizing resource utilization and service delivery. A cloud-integrated AI-enabled chatbot has been developed. The study specifically addresses the automation of employee onboarding and offboarding functions, with the solution being tested in a large enterprise environment. Evaluation metrics—including efficiency, accuracy, and response time—are then presented. Additionally, risk-management measures are discussed with a particular focus on fairness, bias, and transparency.
Observability Frameworks for Cloud-Native Healthcare Analytics
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.
Artificial Intelligence (AI) is increasingly being integrated into Knowledge Management (KM) systems to provide enhanced decision-making support to Information Technology (IT) operations teams. AI-driven systems are empowering these teams to reduce service resolution times, streamline troubleshooting processes, and share knowledge more effectively by improving the speed, relevance, and contextual accuracy of knowledge retrieval and understanding. Alongside these advantages, such AI-powered capabilities are redesigning the nature of knowledge work, augmenting the collaborative aspect of KM efforts, and shifting the composition and skill requirements of IT operations teams. Fundamental considerations for developing AI-driven KM systems for IT operations are explored, including a discussion of deployed architectural patterns, data quality and governance concerns, and deployment and processing options. Further, the impact of these systems and capabilities on the KM aspect of IT operations teams is examined. Together, these insights provide a foundation for the adoption of AI capabilities within KM systems used by IT operations teams.
Copyright (c) 2024 European Advanced Journal for Emerging Technologies (EAJET)
AI-Driven Big Data Analytics for Supply Chain Resilience
Over recent years, natural disasters, the COVID-19 pandemic, and geopolitical tensions have highlighted vulnerabilities within global supply chains. Breakdowns in production, logistics, and distribution have highlighted a need for resilience – the ability to prepare for, respond to, and recover from disruptions. Artificial Intelligence (AI) and data science offer one pathway to resilience, helping to improve demand forecasts, optimize inventory policies, manage supplier and customer ecosystems, predict events, and provide asset and risk intelligence. Nevertheless, despite partnering to deliver the next generation of ground-breaking intelligent systems, traditional AI techniques cannot learn nothing without data. Supply chains can be considered Big Data Ecosystems, as vast quantities of internal and external data are sourced from multiple systems and tiers, and flow in all directions. By extending big data concepts with empirical research, evidence is provided to support the development of a modern architecture and testable theoretical framework for AI-augmented data analytics. It argues that without addressing fundamental data issues, the application of cutting-edge AI techniques will be limited in scale and impact, concentrated on the forecasting and synthetic production approach, rather than everything that resembled production problems through the Supply Chain Management (SCM) area. Furthermore, only a small part of the potential value creation hidden within big data will finally be realized. An assessment of industry case studies and empirical applications illustrated that the framework can be deployed across multiple sectors. However, in order to be successfully implemented, it must be properly completed by the supporting data infrastructure.
Geospatial Crime Analysis of Patna Municipal Corporation
Crime is a reflection of society and a key concern in the fields of sociology, criminology, psychology, urban planning, and local government. Urban crime mapping is a systematic approach to collecting and mapping crime incidents to identify hotspots for policy intervention. Crime mapping identifies the spatial distribution of crime as well as temporal trends, seasonality, and clustering, all of which inform local government policy. Using yearly crime data from the Patna Municipal Corporation and tools such as ArcGIS 10, GeoDA, QGIS, and R, this project maps crime distribution and highlights wards with increased crime risk. Serious crime typologies such as property crimes, violent crimes, and thefts are emphasized, with a particular focus on significant crime increases and ward concentration. Crime is a reflection of society and a key concern in the fields of sociology, criminology, psychology, urban planning, and local government. Urban crime mapping is a systematic approach to collecting and mapping crime incidents to identify hotspots that require policy intervention. Crime mapping identifies the spatial distribution of crime, as well as temporal trends, seasonality, and clustering, all of which in turn inform local government policy. Using annual crime data from the Patna Municipal Corporation and tools such as ArcGIS 10, GeoDA, QGIS, and R, this study maps crime distribution and highlights wards with increased risk. Serious crime typologies—particularly property crimes, violent crimes, and thefts—are emphasized, with a specific focus on serious increases and concentration within certain wards.
Smart Agents for Sustainable Retail and Paint Supply Chains
Agentic systems are autonomous socio-technical systems that leverage artificial intelligence for more intelligent and tactically effective operation. The educational retail and chemical paint manufacturing industries have significant footprints for education, economic vitality, and environmental impact. Within those industries, agentic artificial intelligence enables supply-chain optimization and promotes sustainability across four dimensions: reduced negative influence on the social environment and community; improved capability for quality service, customer satisfaction, and community engagement; reduced use of non-renewable resources; and reduced risk of failure to meet applicable environment-related regulations. Realizing the potential of agentic artificial intelligence requires accessible Sensor-to-Decision System of Systems data infrastructure capable of overcoming traditional silos within and among organizations. For the retail industry, the infrastructure supports agentic processes that enhance demand forecasting, inventory, risk of stockout, logistics, and fulfillment by detecting demand signals, understanding customer preferences and price responses, striking a balance between service and cost, minimizing lead time, and applying customer-centric fulfilment policies. Similarly, for the paint industry, the infrastructure enables agentic processes that enhance raw-material supply risk control and formulation transparency by assessing supply risk, cultivating strong supplier-firm relationships, establishing formulation-traceability matrices, exploring substitutes for environmentally harmful substances, monitoring product quality, and ensuring compliance with relevant product-requirement regulations.
Graph-Based Analytics for Healthcare Data Streams
Graph-level neural architectures provide a unique set of modeling capabilities that can be leveraged toward predictive analytics for rich, heterogeneous healthcare systems. A broad methodological framework defines the steps required to extract semantically interoperable event streams from diverse internal and external clinical data sources. The algorithmic framework is capable of supporting GNN-driven predictive analytics for historical events within institutional data, as well as forward-looking safety-monitoring architectures that continuously evaluate future prognosis during live operations and ignite alerts whenever the predictions rise above user-defined thresholds. Case studies support safe operations of a temperature-controlled drug-shipping distribution network and an active location-based epidemic containment management., Graph Neural Network (GNN) architectures hold great promise for predictive modeling of large-scale healthcare systems by directly representing the clinical network and incorporating heterogeneous information sources. Nevertheless, the proper data representation and preparation remain challenges that need to be addressed. A methodological framework broadens the horizons for GNN applications beyond conventional settings toward prediction of risk and hazard in rich, heterogeneous environments. Interoperable event streams from different clinical departments constitute the basis for two concrete applications.
Copyright (c) 2023 European Advanced Journal for Emerging Technologies (EAJET)
Cloud-Native AI Pipelines for Insurance Fraud and Risk Intelligence
Cloud-native DevOps for AI pipelines in insurance risk intelligence and fraud detection promotes replicability and composability across use cases. Support for scalable workloads addresses high costs, disk and memory limits, and specific cost and time-saving demands. Cloud-native principles—composability, elasticity, portability, observability, security, formal compliance, and data governance—are applied to underpin a generative AI-oriented insurance architecture. The DevOps component tailors continuous integration and continuous delivery (CI/CD) practices to be responsible, reproducible, reliable, and safe. Infrastructure depends on containerization and orchestration technologies to fully contemplate reproducibility, observability, isolation, and multi-cloud portability. AI workload provisioning adheres to a service model. For safe operation, specialized AI CI/CD pipelines provide model versioning and rollback, data versioning and validation, evaluation, performance metrics and safety gates for canary releases, and go/no-go decisions based on business risk appetite. Their design highlights domain-specific operational risk controls. Different generative AI applications in insurance cover a wide spectrum of functionalities, underlining the fundamental basis for the creation of these models and the capability of these applications to adapt to different environments. Nevertheless, models must be handled with care in order to mitigate potential ethical, social, legal, regulatory, and business-related issues. Risk management and operational risk must be part of the implementation which, when properly completed, can provide a safe way to integrate these types of applications. Several risk intelligence use cases point to specific implementations demonstrated during the CapGemini FBDX programme and depict high-priority paths in the integration of such approaches, illustrating data origin and target indicators.