Open Access Repository
type: channel
language: en
publication name: Open Access Repository
items: A Privacy-Enhanced Federated Intrusion Detection System for Healthcare Networks with Differential Privacy Guarantees
items: Developing a QoS-Aware Framework for Multi-Cloud Interoperability and Service Orchestration
items: Assessing the Impact of Network Latency on Service Quality in Distributed Cloud Infrastructures
items: Regulatory Challenges and Compliance Strategies in Cross-Border Financial Services Transactions
items: Investigating the Role of Data Augmentation Strategies in Improving Robustness of Vision Transformers
items: INVESTIGATING THE ETHICAL TRADE-OFFS IN HYPERPERSONALIZED FINANCIAL PRODUCT RECOMMENDATIONS USING AI MODELS
items: ENERGY-AWARE RESOURCE SCHEDULING IN GREEN CLOUD COMPUTING ENVIRONMENTS USING REINFORCEMENT LEARNING
items: DETECTION OF VARIOUS TYPES OF CRACKS USING THE INNER ELECTRICAL RESISTIVITY MEASUREMENT IN REINFORCED CONCRETE MEMBERS: A REVIEW
items: SECURITY AND COMPLIANCE CHALLENGES IN CLOUD-NATIVE CI/CD PIPELINES FOR ENTERPRISE SOFTWARE DELIVERY
items: Investigation of Cloud Security Posture Management Strategies Using Automated Infrastructure Provisioning Tools
items: BENCHMARKING THE PERFORMANCE OF CONTAINERIZED MICROSERVICES USING DISTRIBUTED TRACING AND DYNAMIC LOAD TESTING
items: APPLICATION OF ANT COLONY OPTIMIZATION FOR SCHEDULING CONTAINER DEPLOYMENTS IN CLOUD NATIVE CI/CD PIPELINES
items: FROM FEATURES TO FINANCIAL PERSONAS: MAPPING FEATURE TRANSFORMATION EFFICACY TO CUSTOMER ARCHETYPES IN BEHAVIORAL BANKING DATA
items: Benchmarking Prompt Architectures: A Quantitative Study of Contextual and Decomposed Prompting for Complex ETL Code Generation
items: MAPPING IMPERFECTIONS TO INSTRUMENTS: A UNIFIED TAXONOMY FOR DATA ENGINEERING IN BEHAVIORAL ECONOMICS
items: A Comparative Study of Monitoring Telemetry Pipelines with Prometheus and Grafana in Cloud-Native Java Applications
items: Algorithmic Generation of GraphQL Schema Definitions Using Large Language Models Trained on Open-Source APIs
items: END-TO-END SECURITY MODELING IN MICROSERVICES ARCHITECTURES USING OAUTH2 AND JWT IN A SPRING BOOT ECOSYSTEM
items: Classification Models for Identifying Safety Hazards from Wearable Device Data in Construction Sites
items: MACHINE LEARNING DRIVEN QUALITY CONTROL SYSTEMS FOR DEFECT DETECTION IN STRUCTURAL COMPONENT FABRICATION
items: MULTI-AGENT ARTIFICIAL INTELLIGENCE SYSTEMS FOR COORDINATED TASK SCHEDULING IN COMPLEX CONSTRUCTION PROJECTS
items: DAMAGE DETECTION IN REINFORCED CONCRETE STRUCTURES USING VIBRATION-BASED MONITORING TECHNIQUES
items: PERFORMANCE-BASED SEISMIC DESIGN FRAMEWORKS FOR RESILIENT INFRASTRUCTURE DEVELOPMENT
items: AUGMENTED REALITY TOOLS FOR ENHANCING WORKER TRAINING AND DESIGN VISUALIZATION IN CONSTRUCTION PROJECTS
items: NATURAL LANGUAGE PROCESSING APPLICATIONS FOR DOCUMENT AUTOMATION AND CLAIM ANALYSIS IN CONSTRUCTION CONTRACTS
items: Autonomous Code Review and Refactoring Suggestions Using AI-Augmented Software Engineering Agents
items: Closed-Loop Feedback Systems in DevOps Automation Using AI for Predictive Failure Recovery and Deployment Rollbacks
items: Workflow Optimization in Function-as-a-Service Systems Through Intelligent Event Routing and Cold Start Profiling
items: END-TO-END PERFORMANCE MONITORING OF SERVERLESS ARCHITECTURES IN LATENCY-CRITICAL APPLICATION WORKLOADS
items: MODEL-DRIVEN ENGINEERING FOR MICROSERVICES DEPLOYMENT WITH FOCUS ON SERVICE INTEROPERABILITY AND API GOVERNANCE

A Privacy-Enhanced Federated Intrusion Detection System for Healthcare Networks with Differential Privacy Guarantees
type: item
description: Intrusion Detection Systems (IDS) are essential in protecting healthcare networks from cyber threats. However, centralized IDS architectures raise privacy concerns due to the sensitive nature of patient data. This paper presents a Federated Learning (FL)-based Intrusion Detection System, integrated with Differential Privacy (DP) to preserve data confidentiality while ensuring detection efficiency. We evaluate our model on two publicly available healthcare cybersecurity datasets, showing significant improvements in detection accuracy and privacy preservation. Simulation results demonstrate our system's feasibility for real-time deployment in decentralized healthcare infrastructures.
subject: Research paper
  topic: section
subject: Research Articles
  topic: category
subject: Federated Learning
  topic: keywords
subject: Differential Privacy
  topic: keywords
subject: Intrusion Detection System
  topic: keywords
subject: Healthcare Security
  topic: keywords
subject: Privacy Preservation
  topic: keywords
subject: Cybersecurity
  topic: keywords
creator: Prashant Singh Rana
rights: Copyright (c) 2025 Prashant Singh Rana (Author) https://creativecommons.org/licenses/by-nc/4.0
license: https://creativecommons.org/licenses/by-nc/4.0
date: 2025-08-17
publication date: 2025-08-17

Developing a QoS-Aware Framework for Multi-Cloud Interoperability and Service Orchestration
type: item
description: Multi-cloud computing enables organizations to harness the capabilities of various cloud platforms, but ensuring interoperability and meeting Quality of Service (QoS) expectations remains a challenge. This paper proposes a QoS-aware framework that facilitates dynamic service orchestration across heterogeneous cloud providers. Leveraging semantic compatibility, ontology-based selection, and AI-driven decision models, the framework enhances resource provisioning, availability, and performance. We evaluate our approach using a simulation of hybrid-cloud scenarios with variable workloads and demonstrate improved latency and cost trade-offs over traditional models.
subject: Research paper
  topic: section
subject: Research Articles
  topic: category
subject: Multi-cloud'
  topic: keywords
subject: QoS-aware
  topic: keywords
subject: Interoperability
  topic: keywords
subject: Service Orchestration
  topic: keywords
subject: Cloud Federation
  topic: keywords
subject: Semantic Compatibility
  topic: keywords
subject: Resource Optimization
  topic: keywords
creator: Bishwajeet Kumar Pandey
rights: Copyright (c) 2025 Bishwajeet Kumar Pandey (Author) https://creativecommons.org/licenses/by-nc/4.0
license: https://creativecommons.org/licenses/by-nc/4.0
date: 2025-02-18
publication date: 2025-02-18

Assessing the Impact of Network Latency on Service Quality in Distributed Cloud Infrastructures
type: item
description: Network latency remains a critical bottleneck in distributed cloud environments, especially with increasing adoption of latency-sensitive applications such as IoT, real-time analytics, and virtualized services. This paper investigates the quantitative and qualitative impact of network latency on overall service quality in distributed cloud infrastructures. Through literature synthesis, simulations, and comparative data analysis, we demonstrate the correlation between latency and service performance indicators such as response time, throughput, and SLA compliance. Optimizing latency is vital to ensure robust, resilient, and responsive cloud services.
subject: Research paper
  topic: section
subject: Research Articles
  topic: category
subject: Network Latency
  topic: keywords
subject: Distributed Cloud
  topic: keywords
subject: Service Quality
  topic: keywords
subject: Edge Computing
  topic: keywords
subject: QoS
  topic: keywords
subject: Virtualization
  topic: keywords
subject: Cloud Infrastructure
  topic: keywords
creator: Siva Teja Reddy Kandula
rights: Copyright (c) 2025 Siva Teja Reddy Kandula (Author) https://creativecommons.org/licenses/by-nc/4.0
license: https://creativecommons.org/licenses/by-nc/4.0
date: 2025-02-21
publication date: 2025-02-21

Regulatory Challenges and Compliance Strategies in Cross-Border Financial Services Transactions
type: item
description: Cross-border financial services are growing in complexity and volume, driven by globalization and digital transformation. However, this growth is accompanied by rising regulatory challenges stemming from jurisdictional differences, anti-money laundering (AML) requirements, data localization mandates, and evolving tax regimes. This paper explores the regulatory issues confronting cross-border transactions and proposes strategic compliance frameworks based on harmonization, technology adoption, and cooperative governance. It combines literature insights, data visualization, and forward-looking perspectives.
subject: Research paper
  topic: section
subject: Research Articles
  topic: category
subject: Cross-border transactions
  topic: keywords
subject: regulatory compliance
  topic: keywords
subject: financial services
  topic: keywords
subject: AML/KYC
  topic: keywords
subject: jurisdictional risk
  topic: keywords
subject: digital finance
  topic: keywords
subject: blockchain regulation
  topic: keywords
creator: Lakshmi Prasad Rongali
rights: Copyright (c) 2025 Lakshmi Prasad Rongali (Author) https://creativecommons.org/licenses/by-nc/4.0
license: https://creativecommons.org/licenses/by-nc/4.0
date: 2025-03-21
publication date: 2025-03-21

Investigating the Role of Data Augmentation Strategies in Improving Robustness of Vision Transformers
type: item
description: Vision Transformers (ViTs) have achieved remarkable performance across various visual recognition tasks. However, their robustness against real-world variability remains a challenge. This paper investigates the influence of diverse data augmentation techniques on the performance and robustness of ViTs. Through empirical analysis across benchmark datasets and augmentation strategies such as RandAugment, CutMix, and adversarial perturbations, we highlight their role in improving generalization. Results show that tailored augmentation pipelines can significantly improve the robustness of ViTs without additional architectural changes.
subject: Research paper
  topic: section
subject: Research Articles
  topic: category
subject: Vision Transformers
  topic: keywords
subject: model robustness
  topic: keywords
subject: Data Augmentation
  topic: keywords
subject: Computer Vision
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: RandAugment'
  topic: keywords
subject: Adversarial Noise'
  topic: keywords
creator: Hardik A. Gohel Journal
rights: Copyright (c) 2025 Hardik A. Gohel Journal (Author) https://creativecommons.org/licenses/by-nc/4.0
license: https://creativecommons.org/licenses/by-nc/4.0
date: 2025-07-16
publication date: 2025-07-16

INVESTIGATING THE ETHICAL TRADE-OFFS IN HYPERPERSONALIZED FINANCIAL PRODUCT RECOMMENDATIONS USING AI MODELS
type: item
description: The growing integration of artificial intelligence (AI) into financial services has led to the emergence of hyperpersonalized recommendation systems. These systems tailor products such as credit cards, insurance plans, and investment portfolios to individual user profiles. While this enhances customer experience and operational efficiency, it introduces significant ethical dilemmas including algorithmic bias, surveillance, manipulation, and lack of informed consent. This paper investigates the ethical implications of AI-powered hyperpersonalization in financial contexts, drawing from scholarly literature and industry data. It presents analytical visualizations and outlines emerging frameworks to balance innovation with ethical responsibility.
subject: Research paper
  topic: section
subject: Research Articles
  topic: category
subject: Hyperpersonalization
  topic: keywords
subject: AI Ethics
  topic: keywords
subject: Financial Services
  topic: keywords
subject: Algorithmic Bias
  topic: keywords
subject: Data Privacy
  topic: keywords
subject: Fairness
  topic: keywords
subject: Consent
  topic: keywords
creator: Subhash Bondhala Journal
rights: Copyright (c) 2025 Subhash Bondhala Journal (Author) https://creativecommons.org/licenses/by-nc/4.0
license: https://creativecommons.org/licenses/by-nc/4.0
date: 2025-05-10
publication date: 2025-05-10

ENERGY-AWARE RESOURCE SCHEDULING IN GREEN CLOUD COMPUTING ENVIRONMENTS USING REINFORCEMENT LEARNING
type: item
description: Purpose: The study aims to address the growing environmental concerns caused by the rising energy demands of data centers in cloud computing. It investigates the potential of reinforcement learning (RL)-based scheduling to enhance energy efficiency in cloud environments without compromising service quality. Design/methodology/approach: This research adopts a reinforcement learning approach to energy-aware resource scheduling in cloud computing infrastructures. The model dynamically allocates resources by analyzing real-time factors such as workload variability, energy demand, and environmental conditions, ensuring optimal energy consumption during operations. Findings: The proposed RL-based scheduling strategy significantly reduces energy usage in data centers while maintaining or improving service-level performance. It enables cloud systems to adapt to changing conditions in real-time, resulting in more efficient and sustainable operations. Practical implications: The findings demonstrate a practical pathway for cloud service providers to integrate intelligent scheduling mechanisms that reduce operational costs and carbon footprint. This approach can be implemented across various data center architectures to support greener cloud computing practices. Originality/value: This study contributes to the emerging field of green cloud computing by presenting a novel application of reinforcement learning for energy-aware resource management. It highlights the value of adaptive, intelligent systems in achieving sustainability goals in modern cloud infrastructure.
subject: Research paper
  topic: section
subject: Research Articles
  topic: category
subject: Green Cloud Computing
  topic: keywords
subject: Resource Scheduling
  topic: keywords
subject: Energy-Aware
  topic: keywords
subject: Reinforcement Learning
  topic: keywords
subject: Data Centers
  topic: keywords
subject: Sustainability
  topic: keywords
subject: Cloud Environments
  topic: keywords
creator: Beura Maddukuri Veluri
rights: Copyright (c) 2026 Beura Maddukuri Veluri (Author) https://creativecommons.org/licenses/by-nc/4.0
license: https://creativecommons.org/licenses/by-nc/4.0
date: 2026-01-22
publication date: 2026-01-22

DETECTION OF VARIOUS TYPES OF CRACKS USING THE INNER ELECTRICAL RESISTIVITY MEASUREMENT IN REINFORCED CONCRETE MEMBERS: A REVIEW
type: item
description: This research aims to detect any types of cracks whether flexural, shear, or torsional generated inside any RC members whether made of normal or heavy weight concrete using efficient non-destructive method such as linear inner electrical resistivity and square inner electrical resistivity measurements. The two efficient parameters used to detect the cracks inside the RC members in real time are the percentage change in inner electrical resistivity between uncracked and cracked members and the decimal logarithm resistivity anisotropy (DLRA) at different setups of measurements inside the cracked members to show the crack's presence and its direction instantaneously.
subject: Research paper
  topic: section
subject: Research Articles
  topic: category
subject: Electrical Resistivity
  topic: keywords
subject: Inner Electrical Resistivity Measurements
  topic: keywords
subject: Square Inner Electrical Resistivity Measurement
  topic: keywords
subject: Linear Inner Electrical Resistivity Measurement
  topic: keywords
subject: Cracks
  topic: keywords
subject: Decimal Logarithm Resistivity Anisotropy (DLRA)
  topic: keywords
subject: Heavy Weight Concrete
  topic: keywords
creator: Mostafa Hassan
creator: Mohamed Ihab Sherif ELmasry
creator: Nabil Hassan Elashkar
rights: Copyright (c) 2025 Mostafa Hassan, Mohamed Ihab Sherif ELmasry, Nabil Hassan Elashkar (Author) https://creativecommons.org/licenses/by-nc/4.0
license: https://creativecommons.org/licenses/by-nc/4.0
date: 2025-01-22
publication date: 2025-01-22

SECURITY AND COMPLIANCE CHALLENGES IN CLOUD-NATIVE CI/CD PIPELINES FOR ENTERPRISE SOFTWARE DELIVERY
type: item
description: Purpose: This paper investigates the emerging security and compliance risks associated with cloud-native Continuous Integration and Continuous Deployment (CI/CD) pipelines in enterprise software delivery. It aims to provide a comprehensive understanding of how cloud-native transformation impacts data protection, access control, vulnerability management, and auditability within CI/CD environments. Design/methodology/approach: The study adopts a qualitative, analytical approach by synthesizing current research, industry practices, and regulatory requirements. It examines case studies from enterprise environments and maps CI/CD workflows against prominent compliance frameworks (e.g., ISO 27001, SOC 2, HIPAA) to identify critical vulnerabilities and control gaps. Findings: The research reveals that while cloud-native CI/CD pipelines enhance agility and scalability, they introduce significant security risks if not properly governed. Key findings include a lack of integrated access controls in pipeline tools, poor visibility into vulnerability propagation during automated deployments, and audit challenges due to decentralized cloud infrastructure. Practical implications: Organizations must realign their CI/CD practices with security and compliance requirements to mitigate potential breaches and regulatory penalties. The paper outlines practical strategies for embedding security controls and audit mechanisms into CI/CD workflows, ensuring resilience and trustworthiness in enterprise software delivery. Originality/value: This work provides one of the first in-depth examinations of the intersection between cloud-native CI/CD pipelines and enterprise-grade security/compliance. It offers actionable insights for DevSecOps professionals, compliance teams, and enterprise architects aiming to build secure, compliant, and audit-ready delivery pipelines in a cloud-native context.
subject: Research paper
  topic: section
subject: Research Articles
  topic: category
subject: Cloud-native
  topic: keywords
subject: CI/CD Pipelines
  topic: keywords
subject: Security
  topic: keywords
subject: Compliance
  topic: keywords
subject: Enterprise Software Delivery
  topic: keywords
subject: Data Protection
  topic: keywords
subject: Vulnerability Management
  topic: keywords
subject: Access Control\
  topic: keywords
creator: Dahir Hashi Sheed
rights: Copyright (c) 2026 Dahir Hashi Sheed (Author) https://creativecommons.org/licenses/by-nc/4.0
license: https://creativecommons.org/licenses/by-nc/4.0
date: 2026-01-22
publication date: 2026-01-22

Investigation of Cloud Security Posture Management Strategies Using Automated Infrastructure Provisioning Tools
type: item
description: Cloud Security Posture Management (CSPM) is a critical domain in ensuring the compliance, integrity, and security of cloud-native environments. With the rapid adoption of Infrastructure as Code (IaC) and automation tools such as Terraform, AWS CloudFormation, and Ansible, organizations are redefining how they approach cloud infrastructure provisioning and its associated security challenges. This paper investigates the integration of automated provisioning tools with CSPM strategies to enhance threat detection, compliance enforcement, and vulnerability management in dynamic cloud environments. We evaluate architectural models, operational workflows, and real-world use cases to highlight the benefits, limitations, and emerging research directions in the automated management of cloud security postures.
subject: Research paper
  topic: section
subject: Research Articles
  topic: category
subject: cloud security
  topic: keywords
subject: cloud security posture management
  topic: keywords
subject: infrastructure as code
  topic: keywords
subject: terraform
  topic: keywords
subject: aws cloudformation
  topic: keywords
subject: ansible
  topic: keywords
subject: cloud compliance
  topic: keywords
subject: automated provisioning
  topic: keywords
subject: security automation
  topic: keywords
subject: cspm strategy
  topic: keywords
subject: threat detection
  topic: keywords
subject: cloud governance
  topic: keywords
subject: posture drift
  topic: keywords
subject: cloud-native security
  topic: keywords
subject: risk assessment
  topic: keywords
creator: Murata Akiyuki Kanae
rights: Copyright (c) 2022 Murata Akiyuki Kanae (Author) https://creativecommons.org/licenses/by-nc/4.0
license: https://creativecommons.org/licenses/by-nc/4.0
date: 2022-07-19
publication date: 2022-07-19

BENCHMARKING THE PERFORMANCE OF CONTAINERIZED MICROSERVICES USING DISTRIBUTED TRACING AND DYNAMIC LOAD TESTING
type: item
description: The rapid proliferation of microservices architectures, especially in cloud-native environments, necessitates robust methods for evaluating their performance under real-world conditions. Traditional performance evaluation tools fall short in capturing the complexities of containerized microservices, particularly in heterogeneous deployment environments. This paper proposes an integrated approach combining distributed tracing with dynamic load testing to benchmark performance more holistically. By observing latency, throughput, and resource consumption across service dependencies, our method offers a deeper understanding of performance bottlenecks and scalability limits. We implement this approach in a Kubernetes environment using open-source observability tools and dynamic stress generators, providing a modular benchmarking framework for DevOps and SRE teams. Empirical results demonstrate how fine-grained tracing and adaptive load generation enable proactive performance tuning and improve service reliability under fluctuating workloads.
subject: Research paper
  topic: section
subject: Research Articles
  topic: category
subject: Microservices
  topic: keywords
subject: Distributed Tracing
  topic: keywords
subject: Load Testing
  topic: keywords
subject: Performance Benchmarking
  topic: keywords
subject: Observability
  topic: keywords
subject: Kubernetes
  topic: keywords
subject: Service Mesh
  topic: keywords
subject: Performance Metrics
  topic: keywords
subject: Container Orchestration
  topic: keywords
subject: Dynamic Workloads'
  topic: keywords
creator: Shohei Ooka Ogawa
rights: Copyright (c) 2022 Shohei Ooka Ogawa (Author) https://creativecommons.org/licenses/by-nc/4.0
license: https://creativecommons.org/licenses/by-nc/4.0
date: 2022-07-14
publication date: 2022-07-14

APPLICATION OF ANT COLONY OPTIMIZATION FOR SCHEDULING CONTAINER DEPLOYMENTS IN CLOUD NATIVE CI/CD PIPELINES
type: item
description: The evolution of software delivery methodologies, particularly with the adoption of cloud-native continuous integration and continuous deployment (CI/CD) pipelines, has led to complex challenges in resource allocation and scheduling of containerized workloads. Traditional heuristic methods often fall short in addressing the dynamic and distributed nature of such environments. This paper proposes the use of Ant Colony Optimization (ACO) as a metaheuristic approach to optimize the scheduling of container deployments in cloud-native CI/CD pipelines. By mimicking the foraging behavior of ants, ACO adapts to changing system states and ensures efficient resource utilization and reduced deployment latency. Simulation-based experiments demonstrate the viability of the approach in handling high-volume, multi-tenant CI/CD tasks across distributed cloud infrastructure.
subject: Research paper
  topic: section
subject: Research Articles
  topic: category
subject: Ant Colony Optimization
  topic: keywords
subject: Container Orchestration
  topic: keywords
subject: CI/CD Pipelines
  topic: keywords
subject: Cloud-native Deployment
  topic: keywords
subject: Metaheuristics
  topic: keywords
subject: Scheduling Algorithms
  topic: keywords
subject: Kubernetes Optimization
  topic: keywords
subject: Software Delivery Automation
  topic: keywords
subject: DevOps
  topic: keywords
subject: Deployment Latency
  topic: keywords
creator: Osamu Shikibu Keigo
rights: Copyright (c) 2022 Osamu Shikibu Keigo (Author) https://creativecommons.org/licenses/by-nc/4.0
license: https://creativecommons.org/licenses/by-nc/4.0
date: 2022-07-08
publication date: 2022-07-08

FROM FEATURES TO FINANCIAL PERSONAS: MAPPING FEATURE TRANSFORMATION EFFICACY TO CUSTOMER ARCHETYPES IN BEHAVIORAL BANKING DATA
type: item
description: Current research in customer behavior prediction within retail banking heavily benchmarks the aggregate performance of feature engineering and machine learning pipelines. While these studies demonstrate that techniques like logarithmic scaling, polynomial expansion, and interaction term creation improve overall classification accuracy for metrics such as customer activity status, they provide scant insight into the heterogeneous effects these transformations have across a bank’s diverse customer base. This study posits that the efficacy of a feature transformation is not universal but is intrinsically linked to the underlying financial behavior pattern, or ‘persona,’ of the customer. To address this critical gap, we conduct a two-stage analytical process on a real-world dataset of 30,000 customers from a major European bank. First, we employ a combination of k-means clustering and Gaussian Mixture Models on raw transactional and demographic features to derive five distinct, interpretable customer archetypes: The Digital Nomad (high-frequency, low-value digital transactions), The Traditional Accumulator (low-frequency, high-value branch-based savings), The Credit-Reliant (revolving credit users), The Multi-Product Holder (diversified product portfolio), and The Dormant Saver. Subsequently, we apply eight common feature transformation techniques (including log1p, Box-Cox, polynomial degrees 2 & 3, and PCA) to create multiple engineered datasets. A Random Forest classifier is then trained and evaluated on each archetype’s subpopulation separately, using both raw and transformed features. Our findings reveal a striking disproportion in transformation utility. For instance, logarithmic transformation dramatically improved recall for The Digital Nomad (from 0.72 to 0.89) by normalizing heavy-tailed transaction frequency data but provided negligible improvement for The Traditional Accumulator. Conversely, interaction terms between branch visit frequency and account tenure …
subject: Research paper
  topic: section
subject: Research Articles
  topic: category
subject: Customer Archetypes
  topic: keywords
subject: Feature Engineering
  topic: keywords
subject: Interpretable Machine Learning
  topic: keywords
subject: Personalized Banking
  topic: keywords
subject: Behavioral Segmentation
  topic: keywords
subject: SHAP
  topic: keywords
subject: Predictive Modeling'
  topic: keywords
creator: Rajitha Gentyala
rights: Copyright (c) 2024 Rajitha Gentyala (Author) https://creativecommons.org/licenses/by-nc/4.0
license: https://creativecommons.org/licenses/by-nc/4.0
date: 2024-05-15
publication date: 2024-05-15

Benchmarking Prompt Architectures: A Quantitative Study of Contextual and Decomposed Prompting for Complex ETL Code Generation
type: item
description: The integration of large language models (LLMs) into data engineering workflows promises significant acceleration in the development of Extract, Transform, and Load (ETL) pipelines. However, the practical adoption of this paradigm is hindered by a lack of empirical rigor in evaluating the efficacy of different prompt design strategies, particularly for tasks of high complexity. While current pedagogical resources effectively teach the art of prompt crafting, they do not provide quantitative evidence on which prompt architectures yield the most reliable, accurate, and efficient code generation. This study addresses this critical gap by conducting a systematic, benchmark-driven evaluation of two advanced prompt-design paradigms contextual prompting and decomposed prompting against a baseline of generic, direct prompts for the generation of complex ETL logic. Our methodology constructs a novel benchmark suite of 50 challenging data transformation tasks, derived from real-world scenarios, including nested JSON flattening, hierarchical data aggregation, and time-series imputation with conditional logic. For each task, we generate code solutions using the state-of-the-art GPT-4 model across three distinct prompt architectures: a minimal direct prompt, a context-rich prompt incorporating domain-specific schema definitions and constraint annotations, and a decomposed prompt that breaks the problem into a sequenced chain of subtasks. Drawing inspiration from the human-AI collaboration framework explored by Wu et al. (2022) in "PromptChainer: Chaining Large Language Model Prompts through Visual Programming," we formalize the decomposed prompting strategy into a reproducible chain-of-thought process tailored for data engineering. Furthermore, we extend the evaluation principles for code-generating models discussed by Chen et al. (2021) in "Evaluating Large Language Models Trained on Code," by applying a multi-faceted assessment metric. Each generated code artifact is automatic…
subject: Research paper
  topic: section
subject: Research Articles
  topic: category
subject: Prompt Engineering
  topic: keywords
subject: Large Language Models
  topic: keywords
subject: Data Engineering
  topic: keywords
subject: ETL Pipelines
  topic: keywords
subject: Code Generation
  topic: keywords
subject: Benchmark Evaluation
  topic: keywords
subject: Contextual Prompting
  topic: keywords
subject: Chain-of-Thought
  topic: keywords
subject: GPT-4
  topic: keywords
subject: Software Quality
  topic: keywords
creator: Rajitha Gentyala
rights: Copyright (c) 2025 Rajitha Gentyala (Author) https://creativecommons.org/licenses/by-nc/4.0
license: https://creativecommons.org/licenses/by-nc/4.0
date: 2025-06-05
publication date: 2025-06-05

MAPPING IMPERFECTIONS TO INSTRUMENTS: A UNIFIED TAXONOMY FOR DATA ENGINEERING IN BEHAVIORAL ECONOMICS
type: item
description: The burgeoning field of cognitive economics seeks to disentangle preferences, beliefs, and decision-making errors, a task impossible with standard choice data alone. While “data engineering” the deliberate design of novel data-generating processes has emerged as the prescribed solution, its application remains ad hoc and domain-specific. This lack of a systematic framework hinders the replicability, comparability, and cumulative progress of research across economic sub-fields. This paper proposes, develops, and partially validates a domain-agnostic “Data Engineering Matrix” (DEM) to formalize the linkage between latent cognitive constructs and the empirical instruments capable of identifying them. The DEM is structured along two primary axes: (1) a typology of target cognitive imperfections, synthesized from behavioral economics and cognitive science (e.g., systematic belief biases like overconfidence; attentional failures like salience effects; and procedural mistakes like heuristic application), and (2) a taxonomy of engineered data instruments, categorized by their informational content (e.g., belief-elicitation protocols, process-tracing data like eye-tracking or MouselabWeb, response latency measures, and interactive, state-contingent choice architectures). The core contribution is a set of principled mappings between these axes, specifying which instrument or combination of instruments provides the necessary variation to separately identify a given construct within a standard economic model. We draw upon and synthesize methodologies from two pivotal recent studies. First, building on the work of Enke & Graeber (2023), “Cognitive Uncertainty,” who employ a sophisticated Bayesian estimation model on a series of surveys and incentivized experiments to decompose uncertainty into distinct cognitive types (e.g., aleatory vs. epistemic, or “fuzzy thinking”), our taxonomy explicitly incorporates the instruments they pioneer namely, finely graded probabilistic surveys …
subject: Research paper
  topic: section
subject: Research Articles
  topic: category
subject: Data Engineering
  topic: keywords
subject: Behavioral Economics
  topic: keywords
subject: Cognitive Imperfections
  topic: keywords
subject: Experimental Methodology
  topic: keywords
subject: Research Design
  topic: keywords
subject: Belief Elicitation
  topic: keywords
subject: Process Tracing
  topic: keywords
subject: Taxonomy
  topic: keywords
creator: Rajitha Gentyala
rights: Copyright (c) 2026 Rajitha Gentyala (Author) https://creativecommons.org/licenses/by-nc/4.0
license: https://creativecommons.org/licenses/by-nc/4.0
date: 2025-01-05
publication date: 2025-01-05

A Comparative Study of Monitoring Telemetry Pipelines with Prometheus and Grafana in Cloud-Native Java Applications
type: item
description: In cloud-native Java applications, monitoring telemetry pipelines is critical for observability, performance tuning, and reliability. This paper presents a comparative study of two popular open-source tools—Prometheus (for metrics collection and alerting) and Grafana (for visualization and dashboarding)—in the context of a microservices-based Java application deployed on Kubernetes. We instrument the Java services using JMX exporters, configure Prometheus to scrape metrics, and build Grafana dashboards to visualize latency, throughput, error rates and JVM metrics. Key evaluation criteria include setup complexity, scalability (scrape frequency and time-series retention), query performance, alerting responsiveness, dashboard usability, and resource overhead. Our results show that while Prometheus excels at time-series collection and alerting, Grafana significantly enhances interpretability via dashboards, but careful design is needed to scale effectively. We also identify trade-offs around data retention, cardinality, and query workload. Practical recommendations for cloud-native Java developers are provided.
subject: Research paper
  topic: section
subject: Research Articles
  topic: category
subject: Telemetry Pipeline
  topic: keywords
subject: Prometheus
  topic: keywords
subject: Grafana'
  topic: keywords
subject: Cloud-Native Java
  topic: keywords
subject: Microservices Monitoring
  topic: keywords
subject: JMX Exporter
  topic: keywords
subject: Metrics Collection
  topic: keywords
subject: Observability
  topic: keywords
creator: Glomb Asgarova Caspary
rights: Copyright (c) 2022 Glomb Asgarova Caspary (Author) https://creativecommons.org/licenses/by-nc/4.0
license: https://creativecommons.org/licenses/by-nc/4.0
date: 2022-04-28
publication date: 2022-04-28

Algorithmic Generation of GraphQL Schema Definitions Using Large Language Models Trained on Open-Source APIs
type: item
description: This paper explores the use of Large Language Models (LLMs) for the algorithmic generation of GraphQL schema definitions derived from open-source API documentation. By leveraging pretrained transformer architectures fine-tuned on structured API datasets, schema components such as queries, mutations, and type relationships can be automatically constructed with minimal developer input. The study evaluates model accuracy, schema validity, and integration efficiency with Node.js and Apollo Server environments. Experiments reveal that LLM-generated schemas maintain functional correctness while reducing development effort by over 60%. Furthermore, schema optimization techniques improve model-driven inference consistency across heterogeneous API sources, underscoring the potential of AI-augmented schema engineering.
subject: Research paper
  topic: section
subject: Research Articles
  topic: category
subject: GraphQL
  topic: keywords
subject: Large Language Models
  topic: keywords
subject: Schema Generation
  topic: keywords
subject: Open-Source APIs
  topic: keywords
subject: Transformer Models
  topic: keywords
subject: Automation
  topic: keywords
subject: AI-driven Development
  topic: keywords
creator: Baric Finck Skala
rights: Copyright (c) 2022 Baric Finck Skala (Author) https://creativecommons.org/licenses/by-nc/4.0
license: https://creativecommons.org/licenses/by-nc/4.0
date: 2022-06-22
publication date: 2022-06-22

END-TO-END SECURITY MODELING IN MICROSERVICES ARCHITECTURES USING OAUTH2 AND JWT IN A SPRING BOOT ECOSYSTEM
type: item
description: Modern microservices architectures require robust security mechanisms that can scale seamlessly across services. This paper explores a comprehensive end-to-end security model using OAuth2 and JWT (JSON Web Tokens) within a Spring Boot ecosystem. We propose a layered model that supports fine-grained access control, token validation, and secure communication between distributed services. Integration with Spring Security simplifies enforcement, while OAuth2 provides industry-standard authorization flows.
subject: Research paper
  topic: section
subject: Research Articles
  topic: category
subject: Microservices Security
  topic: keywords
subject: OAuth2
  topic: keywords
subject: JWT
  topic: keywords
subject: Spring Boot
  topic: keywords
subject: API Gateway
  topic: keywords
subject: Token Validation
  topic: keywords
creator: Pokorny Lagger Bognar
rights: Copyright (c) 2022 Pokorny Lagger Bognar (Author) https://creativecommons.org/licenses/by-nc/4.0
license: https://creativecommons.org/licenses/by-nc/4.0
date: 2022-06-22
publication date: 2022-06-22

Classification Models for Identifying Safety Hazards from Wearable Device Data in Construction Sites
type: item
description: The integration of wearable technologies in construction has revolutionized safety management by enabling real-time monitoring of workers' physiological and environmental conditions. This paper explores the application of classification models to analyze data from wearable devices, aiming to identify safety hazards proactively on construction sites. By leveraging machine learning algorithms, the study demonstrates how predictive analytics can enhance hazard detection, thereby reducing accidents and improving overall site safety.
subject: Research paper
  topic: section
subject: Research Articles
  topic: category
subject: Wearable Technology
  topic: keywords
subject: Construction Safety
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Hazard Detection
  topic: keywords
subject: Predictive Analytics
  topic: keywords
creator: Christopher Fuentes William
rights: Copyright (c) 2022 Christopher Fuentes William (Author) https://creativecommons.org/licenses/by-nc/4.0
license: https://creativecommons.org/licenses/by-nc/4.0
date: 2022-06-15
publication date: 2022-06-15

MACHINE LEARNING DRIVEN QUALITY CONTROL SYSTEMS FOR DEFECT DETECTION IN STRUCTURAL COMPONENT FABRICATION
type: item
description: Structural component fabrication is a critical phase in engineering where precision and integrity are paramount. Traditional quality control systems often fall short in terms of adaptability and real-time responsiveness. This paper explores machine learning (ML) applications in automated defect detection, demonstrating their potential to significantly enhance fabrication reliability. Leveraging image recognition, anomaly detection, and supervised learning models, ML systems offer cost-effective, scalable, and high-accuracy alternatives to manual inspection. Through a review of prior works and analysis of recent advancements, we propose a framework integrating convolutional neural networks (CNNs) and real-time sensors to detect and classify fabrication defects with high precision.
subject: Research paper
  topic: section
subject: Research Articles
  topic: category
subject: Machine Learning
  topic: keywords
subject: Quality Control
  topic: keywords
subject: Defect Detection
  topic: keywords
subject: Structural Fabrication
  topic: keywords
subject: Convolutional Neural Networks
  topic: keywords
subject: Smart Manufacturing
  topic: keywords
creator: Pablo Ross Susan
rights: Copyright (c) 2022 Pablo Ross Susan (Author) https://creativecommons.org/licenses/by-nc/4.0
license: https://creativecommons.org/licenses/by-nc/4.0
date: 2022-06-11
publication date: 2022-06-11

MULTI-AGENT ARTIFICIAL INTELLIGENCE SYSTEMS FOR COORDINATED TASK SCHEDULING IN COMPLEX CONSTRUCTION PROJECTS
type: item
description: The complexity and scale of modern construction projects demand dynamic, real-time scheduling systems capable of handling diverse interdependencies across tasks, resources, and stakeholders. This paper proposes the application of Multi-Agent Artificial Intelligence (MAAI) systems to manage coordinated task scheduling in construction environments. Through a review of key literature, analysis of agent-based models, and exploration of AI integration, we demonstrate how MAAI enhances efficiency, conflict resolution, and adaptability. Simulation results highlight the capacity of agent-based systems to outperform traditional methods under uncertainty and changing project conditions.
subject: Research paper
  topic: section
subject: Research Articles
  topic: category
subject: Multi-Agent Systems
  topic: keywords
subject: Task Scheduling
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Construction Management
  topic: keywords
subject: Coordination
  topic: keywords
subject: Project Optimization
  topic: keywords
creator: Kimberly Calvo William
rights: Copyright (c) 2022 Kimberly Calvo William (Author) https://creativecommons.org/licenses/by-nc/4.0
license: https://creativecommons.org/licenses/by-nc/4.0
date: 2022-05-06
publication date: 2022-05-06

DAMAGE DETECTION IN REINFORCED CONCRETE STRUCTURES USING VIBRATION-BASED MONITORING TECHNIQUES
type: item
description: Reinforced concrete (RC) structures are fundamental in modern infrastructure, yet their integrity is frequently challenged by environmental stresses, aging, and loading. Vibration-based monitoring techniques (VBMT) have emerged as reliable methods for early damage detection due to their sensitivity to changes in dynamic behavior. This study presents a concise evaluation of VBMT, focusing on modal parameters and machine learning integrations. A literature survey underpins theoretical evolution, while graphical illustrations show real-world applicability. Flowcharts outline decision-making processes in VBMT frameworks, and tables summarize key algorithmic approaches. The paper provides recommendations for implementing smart VBMT systems in civil infrastructure management.
subject: Research paper
  topic: section
subject: Research Articles
  topic: category
subject: Structural Health Monitoring
  topic: keywords
subject: Reinforced Concrete
  topic: keywords
subject: Vibration-Based Monitoring
  topic: keywords
subject: Modal Analysis
  topic: keywords
subject: Damage Detection
  topic: keywords
subject: Signal Processing
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Dynamic Response
  topic: keywords
subject: Non-Destructive Testing
  topic: keywords
creator: Christopher Antonio Labanyi
rights: Copyright (c) 2022 Christopher Antonio Labanyi (Author) https://creativecommons.org/licenses/by-nc/4.0
license: https://creativecommons.org/licenses/by-nc/4.0
date: 2022-04-28
publication date: 2022-04-28

PERFORMANCE-BASED SEISMIC DESIGN FRAMEWORKS FOR RESILIENT INFRASTRUCTURE DEVELOPMENT
type: item
description: Performance-Based Seismic Design (PBSD) frameworks aim to develop infrastructure that not only resists earthquake forces but also ensures quick functionality recovery post-event. This paper outlines the theoretical foundation, implementation challenges, and potential innovations in PBSD for resilient development. The framework emphasizes risk-informed decision-making, multilevel performance targets, and resilience metrics. With increasing urbanization and seismic threats, the adoption of PBSD is essential to mitigate infrastructure failures and economic disruptions.
subject: Research paper
  topic: section
subject: Research Articles
  topic: category
subject: Performance-Based Design
  topic: keywords
subject: Seismic Resilience
  topic: keywords
subject: Infrastructure
  topic: keywords
subject: Risk Mitigation
  topic: keywords
subject: Earthquake Engineering
  topic: keywords
creator: Adelaida Corbalan Ryan
rights: Copyright (c) 2022 Adelaida Corbalan Ryan (Author) https://creativecommons.org/licenses/by-nc/4.0
license: https://creativecommons.org/licenses/by-nc/4.0
date: 2022-04-27
publication date: 2022-04-27

AUGMENTED REALITY TOOLS FOR ENHANCING WORKER TRAINING AND DESIGN VISUALIZATION IN CONSTRUCTION PROJECTS
type: item
description: Augmented Reality (AR) has emerged as a transformative technology in the construction sector, facilitating interactive design visualization and immersive worker training. As the construction industry grapples with skilled labor shortages and rising design complexity, AR provides real-time, on-site simulation environments. These tools enhance decision-making, increase accuracy, and reduce errors. This paper explores the contemporary applications of AR tools in training and visualization, reviewing past literature and evaluating recent advancements through quantitative and qualitative assessments.
subject: Research paper
  topic: section
subject: Research Articles
  topic: category
subject: Augmented Reality (AR)
  topic: keywords
subject: Construction
  topic: keywords
subject: Worker Training
  topic: keywords
subject: Design Visualization
  topic: keywords
subject: Immersive Learning
  topic: keywords
subject: Construction Innovation
  topic: keywords
creator: Salvador Giner Carmen
rights: Copyright (c) 2022 Salvador Giner Carmen (Author) https://creativecommons.org/licenses/by-nc/4.0
license: https://creativecommons.org/licenses/by-nc/4.0
date: 2022-04-25
publication date: 2022-04-25

NATURAL LANGUAGE PROCESSING APPLICATIONS FOR DOCUMENT AUTOMATION AND CLAIM ANALYSIS IN CONSTRUCTION CONTRACTS
type: item
description: Natural Language Processing (NLP) has emerged as a transformative tool in the construction industry, particularly in the domains of document automation and claim analysis. Given the legal and contractual complexity inherent in construction projects, NLP enables systematic processing, classification, and extraction of information from construction contracts, streamlining operations and mitigating disputes. This paper explores current applications of NLP in this field, reviews pertinent literature, and proposes a structured methodology for incorporating NLP tools into contractual workflows.
subject: Research paper
  topic: section
subject: Research Articles
  topic: category
subject: NLP
  topic: keywords
subject: Construction Contracts
  topic: keywords
subject: Document Automation
  topic: keywords
subject: Claim Analysis
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Information Extraction
  topic: keywords
creator: Xudayberdiyeva Kholdorov
rights: Copyright (c) 2022 Xudayberdiyeva Kholdorov (Author) https://creativecommons.org/licenses/by-nc/4.0
license: https://creativecommons.org/licenses/by-nc/4.0
date: 2022-04-10
publication date: 2022-04-10

Autonomous Code Review and Refactoring Suggestions Using AI-Augmented Software Engineering Agents
type: item
description: The increasing complexity of modern software systems necessitates intelligent tools for code maintenance and quality assurance. This paper explores the integration of AI-augmented software engineering agents for autonomous code review and refactoring. Drawing on recent advances in machine learning, multi-agent systems, and large language models (LLMs), we present the evolution, methodologies, and impact of these intelligent agents. A detailed literature review and a comparative analysis of current systems are included, highlighting trends, limitations, and future directions.
subject: Research paper
  topic: section
subject: Research Articles
  topic: category
subject: Autonomous Code Review
  topic: keywords
subject: AI Agents
  topic: keywords
subject: Software Refactoring
  topic: keywords
subject: LLMs
  topic: keywords
subject: Software Engineering
  topic: keywords
subject: Developer Productivity
  topic: keywords
creator: Nomura Ishikane Ichikawa
rights: Copyright (c) 2022 Nomura Ishikane Ichikawa (Author) https://creativecommons.org/licenses/by-nc/4.0
license: https://creativecommons.org/licenses/by-nc/4.0
date: 2022-03-06
publication date: 2022-03-06

Closed-Loop Feedback Systems in DevOps Automation Using AI for Predictive Failure Recovery and Deployment Rollbacks
type: item
description: The integration of Artificial Intelligence (AI) into DevOps pipelines has transformed modern software engineering by enabling closed-loop feedback systems that autonomously predict, detect, and recover from deployment failures. These intelligent systems significantly reduce Mean Time to Recovery (MTTR), enhance system availability, and automate rollback processes through proactive interventions. This paper explores the architecture, operational model, and advantages of AI-powered feedback mechanisms in continuous delivery environments. It also presents a critical analysis of existing approaches, outlines key components such as anomaly detection and automated rollback, and examines the measurable impact of AI in improving deployment resilience. Empirical metrics, conceptual diagrams, and comparative evaluations provide a comprehensive understanding of predictive intelligence in DevOps automation.
subject: Research paper
  topic: section
subject: Research Articles
  topic: category
subject: DevOps
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Closed-loop Feedback
  topic: keywords
subject: Deployment Rollbacks
  topic: keywords
subject: Predictive Recovery
  topic: keywords
subject: AIOps
  topic: keywords
subject: CI/CD
  topic: keywords
creator: Risa Minato Wataya
rights: Copyright (c) 2022 Risa Minato Wataya (Author) https://creativecommons.org/licenses/by-nc/4.0
license: https://creativecommons.org/licenses/by-nc/4.0
date: 2022-02-12
publication date: 2022-02-12

Workflow Optimization in Function-as-a-Service Systems Through Intelligent Event Routing and Cold Start Profiling
type: item
description: Function-as-a-Service (FaaS) revolutionizes cloud-native application deployment by providing stateless execution environments. However, challenges such as cold start latency and inefficient workflow routing degrade performance. This paper explores intelligent event routing and cold start profiling as optimization strategies. We present a model combining routing heuristics and runtime profiling to mitigate latency and optimize throughput. Empirical data shows up to 53% reduction in cold start latency and 35% improvement in event throughput in simulated environments.
subject: Research paper
  topic: section
subject: Research Articles
  topic: category
subject: Serverless Computing
  topic: keywords
subject: Cold Start
  topic: keywords
subject: Event Routing
  topic: keywords
subject: FaaS
  topic: keywords
subject: Workflow Optimization
  topic: keywords
subject: Profiling
  topic: keywords
subject: Latency Reduction
  topic: keywords
creator: Kenzaburo Natsuo Kirino Journal
rights: Copyright (c) 2022 Kenzaburo Natsuo Kirino Journal (Author) https://creativecommons.org/licenses/by-nc/4.0
license: https://creativecommons.org/licenses/by-nc/4.0
date: 2022-02-11
publication date: 2022-02-11

END-TO-END PERFORMANCE MONITORING OF SERVERLESS ARCHITECTURES IN LATENCY-CRITICAL APPLICATION WORKLOADS
type: item
description: Serverless computing has emerged as a disruptive cloud paradigm, enabling developers to deploy code without managing infrastructure. However, its benefits come with performance trade-offs, particularly in latency-critical application workloads. This paper explores the challenges and solutions associated with end-to-end performance monitoring of serverless systems, where traditional observability techniques may fail due to dynamic execution models, cold starts, and third-party dependencies. Through a synthesis of academic literature and empirical data from AWS Lambda, Azure Functions, and Google Cloud Functions, we present a comprehensive evaluation of latency bottlenecks. A novel hybrid monitoring framework is proposed that leverages OpenTelemetry tracing, machine learning-based anomaly detection, and orchestration-aware instrumentation. Our results demonstrate that strategic monitoring interventions can reduce latency violations by over 30% in multi-step workflows.
subject: Research paper
  topic: section
subject: Research Articles
  topic: category
subject: Serverless Computing
  topic: keywords
subject: Performance Monitoring
  topic: keywords
subject: End-to-End Monitoring
  topic: keywords
subject: Latency-Critical Applications
  topic: keywords
subject: Application Performance
  topic: keywords
subject: Cloud Computing
  topic: keywords
subject: Function As A Service (FaaS)
  topic: keywords
subject: Observability
  topic: keywords
subject: Real-Time Monitoring
  topic: keywords
subject: Serverless Architecture
  topic: keywords
subject: Latency Optimization
  topic: keywords
subject: Workload Profiling
  topic: keywords
subject: Distributed Tracing
  topic: keywords
subject: Performance Bottlenecks
  topic: keywords
subject: Monitoring Tools
  topic: keywords
subject: Resource Utilization
  topic: keywords
subject: Service-Level Objectives (SLOs)
  topic: keywords
subject: Cold Start Latency
  topic: keywords
subject: Telemetry Data
  topic: keywords
subject: Monitoring Framework
  topic: keywords
creator: Espido Cristina Morales Journal
rights: Copyright (c) 2026 Espido Cristina Morales Journal (Author) https://creativecommons.org/licenses/by-nc/4.0
license: https://creativecommons.org/licenses/by-nc/4.0
date: 2026-01-22
publication date: 2026-01-22

MODEL-DRIVEN ENGINEERING FOR MICROSERVICES DEPLOYMENT WITH FOCUS ON SERVICE INTEROPERABILITY AND API GOVERNANCE
type: item
description: Microservices architecture, while enabling modular and scalable system development, introduces complexities in deployment and interoperability. Model-Driven Engineering (MDE) addresses these issues by offering abstract modeling mechanisms to automate deployment, enforce consistency, and govern APIs. This paper investigates the application of MDE in microservices deployment with a focus on enhancing service interoperability and API governance. It includes a structured literature review, an architecture model, comparative metrics, and use-case data analysis.
subject: Research paper
  topic: section
subject: Research Articles
  topic: category
subject: Microservices
  topic: keywords
subject: Model-Driven Engineering (MDE)
  topic: keywords
subject: API Governance
  topic: keywords
subject: Interoperability
  topic: keywords
subject: Deployment Automation
  topic: keywords
subject: DevOps
  topic: keywords
creator: Quevedo Carmen Laforet Journal
rights: Copyright (c) 2026 Quevedo Carmen Laforet Journal (Author) https://creativecommons.org/licenses/by-nc/4.0
license: https://creativecommons.org/licenses/by-nc/4.0
date: 2026-01-21
publication date: 2026-01-21