AN ENHANCED ECN-BASED CONGESTION MITIGATION WITH TRAFFIC REROUTING FOR CLOS DATA CENTER NETWORKS
Explicit Congestion Notification (ECN) is widely used in modern data center networks to signal congestion without incurring packet loss. ECN is effective in reducing queue buildup and preserving high throughput. However, congestion signals are generated only after queue buildup has begun, which may delay mitigation in large-scale AI data centers where synchronized, high-throughput traffic flows are common. This paper examines the limitations of conventional ECN behavior in spine-leaf data center fabrics and proposes an enhanced ECN-based mechanism that utilizes in-network feedback and dynamically adapts Equal-Cost Multi-Path (ECMP) paths. By proactively rerouting traffic away from congested paths before widespread congestion propagation, the proposed approach aims to improve job completion time, reduce unnecessary rate throttling, and better utilize available network resources. Additionally, incorporating real-time buffer telemetry into this path-selection process provides a powerful extension that directly elevates fabric stability and load distribution. This work shows the potential of a closer integration of congestion notification and path selection mechanisms may help shorten congestion resolution and improve the efficiency of AI training and inference workloads in modern data center environments.
Copyright (c) 2026 Mohitkumar Savaliya (Author) https://creativecommons.org/licenses/by-nc/4.0
Architecting Scalable Event-Driven Microservices for Resilient Distributed Systems Through Asynchronous Communication and Real-Time Stream Processing
Distributed systems continue to absorb increasing operational complexity while simultaneously being expected to exhibit lower latency, higher availability, and stronger fault tolerance. Event-driven microservice architectures emerged as a response to the rigidity of monolithic deployments and the coordination overhead associated with tightly coupled service ecosystems. Yet much of the contemporary discourse remains dominated by optimistic claims concerning elasticity and scalability while underestimating the systemic costs introduced by asynchronous communication, eventual consistency, event ordering ambiguities, and stream-processing bottlenecks. The result is a recurring mismatch between architectural theory and production reality. Performance claims often conceal coordination debt. This study investigates the architectural mechanisms through which event-driven microservices achieve resilience in distributed environments and examines the conditions under which those mechanisms begin to degrade. A qualitative-analytical methodology is employed, synthesizing existing scholarship, industrial architecture patterns, and observed operational failure modes. Attention is directed toward asynchronous messaging infrastructures, distributed event brokers, stream-processing frameworks, fault isolation strategies, and state management approaches. The evidence suggests that scalability emerges less from service decomposition itself than from the controlled management of communication entropy across distributed execution boundaries. The friction lies in the interaction surfaces. Findings indicate that real-time stream processing enhances responsiveness and operational visibility, yet simultaneously amplifies challenges associated with observability, consistency maintenance, and cascading failure propagation. Resilience appears not as a property of architecture alone but as a negotiated outcome among infrastructure constraints, communication patterns, and organizational governance structures.
Copyright (c) 2026 Athil Kurusidth Ali (Author) https://creativecommons.org/licenses/by-nc/4.0
PREDICTIVE QUALITY MANAGEMENT USING SAP QM AND MACHINE LEARNING
This paper examines the integration of SAP Quality Management (QM) systems and machine learning (ML) for predictive quality management in modern industrial environments. It highlights how SAP QM enables real-time quality control, while ML techniques analyze large datasets to identify patterns and predict potential defects. The review synthesizes existing literature to identify key themes such as transformation of quality assurance models, implementation challenges, and the need for interdisciplinary collaboration. Despite notable advancements, gaps remain in model standardization, user feedback integration, and organizational readiness. The study also discusses the evolving role of predictive quality management within the context of Industry 4.0 and digital transformation. Finally, a conceptual framework is proposed to guide future research and practical implementation in enhancing product quality and operational efficiency.
Copyright (c) 2025 Nirmala Parixit Patel , Elayaraja Subbaiah (Author) https://creativecommons.org/licenses/by-nc/4.0
OPTIMIZING QUALITY INSPECTION IN SAP QM THROUGH DYNAMIC SAMPLING STRATEGIES FOR HIGH-VOLUME MANUFACTURING
Manufacturers in high-volume settings focus on flexible sampling to improve quality. This research compares adaptive strategies with fixed plans. We evaluate how these methods perform in real-world settings. This work looks at speed, cost, and results. It checks how well they adapt to new situations. This study is needed. Production grows larger and more complex every day. Fixed plans follow set rules. They do not adjust for changes in production or new data. This rigidity causes waste. Plans might check too many or too few parts. Adaptive sampling allows for real-time changes based on current findings. These adjustments make quality control faster and products more reliable. Moving to flexible sampling has many benefits. It helps daily work and keeps a company competitive. This study looks at how companies use SAP Quality Management (QM) systems. These systems provide a way to use flexible sampling. SAP QM connects quality tasks to factory management. It is a tool for firms that want to use data to guide their work. Current reports show the link between adaptive sampling and automated monitoring. Advanced software helps find more defects and save resources. Changes to sampling methods depend on specific production line traits. Managers look at process stability and output changes. They look at past defect rates. Custom tools help firms stay fast when markets change. They help improve the quality of products. This study shows how adaptive methods compare to fixed plans. It focuses on high-volume settings where speed and cost matter. We will study defect rates, costs, and time. This data shows if adaptive systems work in practice. Our analysis supports better quality control choices. It follows industry standards and adaptive management rules. Firms now value customer satisfaction and reliability. This shift makes new quality methods popular. Early results show that adaptive sampling cuts errors and costs over time. This change offers a clear value to businesses. Past studies provide a base for quality strategies. This research adds facts from high-volume factories. We focus on case studies and industry standards. These show how to use both adaptive and fixed plans. This comparison provides clear steps for workers who want to improve their quality checks. We want to explain the benefits of flexible sampling. It shows that adaptive plans help factory work and quality. This work adds to academic knowledge. It guides leaders who want to update their quality practices for today's challenges. (Septa S S et al.) (Yang Y) (Dominguez O). An important aspect of this investigation centers on the deployment of SAP Quality Management (QM) systems as a framework for implementing these dynamic sampling methodologies. SAP QM has the capability of integrating quality processes into the management controls of manufacturing systems, making it an essential tool for firms aiming to leverage data-driven strategies (Hamid SR et al.) . Existing literature highlights the interplay between adaptive sampling and automated quality monitoring facilitated by advanced software solutions, suggesting that incorporation of these technologies may lead to improved defect detection rates and more judicious use of resources (Mahesh B et al.) (Supriya V Sullad et al.) . Furthermore, the adaptation of sampling approaches is critically tied to the specific characteristics of production lines, including process stability, variability of output, and historical data regarding defect rates. Such tailored solutions are fundamental for organizations that seek to remain agile in the face of evolving market demands while enhancing product quality (MSc. Ragnhild J Eleftheriadis et al.) (Nair et al.) (Sung H Park) . The intended outcomes of evaluating these dynamic sampling strategies include a comprehensive understanding of how adaptive methods compare against fixed plans in specific operational contexts, particularly in high-volume settings where time and resource efficiencies are paramount. Quantitative metrics such as defect rates, sampling costs, and time efficiencies will be analyzed to ascertain the practical viability of such adaptive frameworks. This analysis will present the case for robust decision-making in quality control practices, aligning with the theoretical frameworks of adaptive management and critical quality parameters established within the field (N/A) (Chiscop F et al.). As firms increasingly prioritize customer satisfaction and product reliability, the rationale for incorporating advanced methodologies in quality management grows more compelling. A preliminary overview reveals that adopting adaptive sampling strategies can lead to significant reductions in both sampling errors and inspection costs over time (Cheng Y et al.), thereby presenting a clear value proposition for businesses. While existing studies have laid a solid groundwork for understanding sampling strategies in quality management, this research aims to fill the gaps by providing empirical evidence drawn from high-volume manufacturing environments. Emphasis will be placed on case studies and industry benchmarks that highlight best practices and practical implications for both adaptive and fixed sampling frameworks (Yadin K et al.) (R Poloczek et al.). Through a detailed comparative analysis, the findings are expected to yield actionable insights for practitioners seeking to refine their quality assurance processes. The ultimate goal is to elucidate the benefits of dynamic sampling procedures, reinforcing the argument that adaptive strategies can significantly enhance overall manufacturing performance and product quality (R Poloczek) (Isania Z et al.) (Dong J et al.) (Anonyuo S et al.) (Li X et al.) (Alzubaidi L et al.). This exploration not only contributes to the academic discourse surrounding quality management systems but also serves as a practical guide for industry stakeholders aiming to innovate their QC practices in line with contemporary production challenges.
DIGITAL TWIN INTEGRATION WITH SAP QM FOR PREDICTIVE DEFECT ANALYSIS
This dissertation examines how Digital Twin technology joins with SAP Quality Management (QM) systems. It aims to improve how factories predict defects in manufacturing processes. The work focuses on how current methods fail to use real-time data for good defect detection. The study shows how to build a strong predictive framework for these processes. It combines factory data, defect logs, and performance numbers from SAP QM with simulation data from Digital Twins. The results show that defect rates dropped by up to 30 percent. Teams responded much faster to potential quality problems. These results prove that combining these systems works well. The meaning of these results goes beyond just manufacturing. They suggest big changes for the healthcare industry. Hospitals need quality management to keep patients safe and keep operations running well. Healthcare groups find and lower risks in medical devices and medical steps. They use advanced predictions made possible by Digital Twin technology to do this. This work changes how people handle quality management practices. It shows that digital tools help healthcare run better and improve patient health. This study shows why healthcare must use new data methods for quality checks. These tools help make healthcare more reliable and safe for everyone who needs care.
A Scalable Distributed Data Architecture Model for High Throughput Systems Using Event Driven Microservices Paradigm
The exponential growth of real-time data from IoT, financial trading, and social media platforms demands high-throughput systems capable of processing millions of events per second with low latency. Traditional monolithic and request-response architectures often become bottlenecks due to tight coupling and centralized data stores. This paper proposes a scalable distributed data architecture model leveraging the event-driven microservices paradigm. Unlike conventional approaches that rely on synchronous database queries, our model utilizes append-only event logs, partitioned streaming platforms (e.g., Apache Kafka), and CQRS (Command Query Responsibility Segregation) with polyglot persistence. Each microservice maintains its own private data store and communicates through immutable events, ensuring loose coupling, horizontal scalability, and fault tolerance. We analyze throughput metrics, data consistency using the Saga pattern, and state management via event sourcing. The model demonstrates linear scalability under increasing load and provides resilience against partial failures. Experimental simulations indicate that the proposed architecture can handle upwards of 1.5 million events per second on a commodity cluster. The paper concludes with implementation considerations for production environments.
Copyright (c) 2025 Henry Britto Francis (Author) https://creativecommons.org/licenses/by-nc/4.0
A Privacy-Enhanced Federated Intrusion Detection System for Healthcare Networks with Differential Privacy Guarantees
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.
Copyright (c) 2025 Prashant Singh Rana (Author) https://creativecommons.org/licenses/by-nc/4.0
Developing a QoS-Aware Framework for Multi-Cloud Interoperability and Service Orchestration
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.
Copyright (c) 2025 Bishwajeet Kumar Pandey (Author) https://creativecommons.org/licenses/by-nc/4.0
Assessing the Impact of Network Latency on Service Quality in Distributed Cloud Infrastructures
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.
Copyright (c) 2025 Siva Teja Reddy Kandula (Author) https://creativecommons.org/licenses/by-nc/4.0
Regulatory Challenges and Compliance Strategies in Cross-Border Financial Services Transactions
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.
Copyright (c) 2025 Lakshmi Prasad Rongali (Author) https://creativecommons.org/licenses/by-nc/4.0
Investigating the Role of Data Augmentation Strategies in Improving Robustness of Vision Transformers
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.
Copyright (c) 2025 Hardik A. Gohel Journal (Author) https://creativecommons.org/licenses/by-nc/4.0
INVESTIGATING THE ETHICAL TRADE-OFFS IN HYPERPERSONALIZED FINANCIAL PRODUCT RECOMMENDATIONS USING AI MODELS
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.
Copyright (c) 2025 Subhash Bondhala Journal (Author) https://creativecommons.org/licenses/by-nc/4.0
ENERGY-AWARE RESOURCE SCHEDULING IN GREEN CLOUD COMPUTING ENVIRONMENTS USING REINFORCEMENT LEARNING
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.
Copyright (c) 2026 Beura Maddukuri Veluri (Author) https://creativecommons.org/licenses/by-nc/4.0
DETECTION OF VARIOUS TYPES OF CRACKS USING THE INNER ELECTRICAL RESISTIVITY MEASUREMENT IN REINFORCED CONCRETE MEMBERS: A REVIEW
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.
Inner Electrical Resistivity Measurements
Square Inner Electrical Resistivity Measurement
Linear Inner Electrical Resistivity Measurement
Decimal Logarithm Resistivity Anisotropy (DLRA)
Copyright (c) 2025 Mostafa Hassan, Mohamed Ihab Sherif ELmasry, Nabil Hassan Elashkar (Author) https://creativecommons.org/licenses/by-nc/4.0
SECURITY AND COMPLIANCE CHALLENGES IN CLOUD-NATIVE CI/CD PIPELINES FOR ENTERPRISE SOFTWARE DELIVERY
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.
Copyright (c) 2026 Dahir Hashi Sheed (Author) https://creativecommons.org/licenses/by-nc/4.0
Investigation of Cloud Security Posture Management Strategies Using Automated Infrastructure Provisioning Tools
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.
Copyright (c) 2022 Murata Akiyuki Kanae (Author) https://creativecommons.org/licenses/by-nc/4.0
BENCHMARKING THE PERFORMANCE OF CONTAINERIZED MICROSERVICES USING DISTRIBUTED TRACING AND DYNAMIC LOAD TESTING
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.
Copyright (c) 2022 Shohei Ooka Ogawa (Author) https://creativecommons.org/licenses/by-nc/4.0
APPLICATION OF ANT COLONY OPTIMIZATION FOR SCHEDULING CONTAINER DEPLOYMENTS IN CLOUD NATIVE CI/CD PIPELINES
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.
Copyright (c) 2022 Osamu Shikibu Keigo (Author) https://creativecommons.org/licenses/by-nc/4.0
FROM FEATURES TO FINANCIAL PERSONAS: MAPPING FEATURE TRANSFORMATION EFFICACY TO CUSTOMER ARCHETYPES IN BEHAVIORAL BANKING DATA
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 were uniquely powerful for predicting the activity of The Traditional Accumulator archetype. Model interpretability tools, specifically SHAP (SHapley Additive exPlanations), were employed post-hoc to deconstruct why certain transformations ‘resonate’ with specific personas. The analysis revealed that for The Credit-Reliant segment, polynomial transformations successfully captured the non-linear, threshold-based relationship between credit utilization ratio and churn risk a pattern absent in other segments. This research makes a pivotal contribution by shifting the paradigm from a one-size-fits-all feature engineering strategy to a persona-aware modeling framework. We demonstrate that strategic feature transformation must be guided by customer segment characteristics, thereby offering bankers and data scientists a nuanced roadmap for developing more precise, interpretable, and ultimately actionable predictive models. The implications extend beyond activity prediction to personalized marketing, risk assessment, and customer lifetime value modeling.
Copyright (c) 2024 Rajitha Gentyala (Author) https://creativecommons.org/licenses/by-nc/4.0
Benchmarking Prompt Architectures: A Quantitative Study of Contextual and Decomposed Prompting for Complex ETL Code Generation
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 automatically evaluated for functional correctness through unit-test execution on sample datasets, for computational efficiency via runtime profiling, and for robustness through stress testing with edge-case data. Our quantitative results demonstrate a statistically significant superiority of structured prompt architectures over direct prompting. Contextual prompting reduced critical runtime errors by approximately 60% by mitigating schema hallucinations, while decomposed prompting improved functional correctness on complex multi-step tasks by over 45% compared to the baseline. However, we also identify a trade-off: decomposed prompts increased total token consumption and initial latency by an average of 30%. The study concludes that the choice of an optimal prompt architecture is contingent upon the specific task complexity and operational priorities correctness versus latency. These findings provide the first rigorous, evidence-based framework for prompt engineering in data engineering, moving beyond heuristic advice to deliver actionable guidelines for implementing reliable, AI-assisted ETL development. We contribute our benchmark suite and evaluation toolkit to the community to foster further research in this domain.
Copyright (c) 2025 Rajitha Gentyala (Author) https://creativecommons.org/licenses/by-nc/4.0
MAPPING IMPERFECTIONS TO INSTRUMENTS: A UNIFIED TAXONOMY FOR DATA ENGINEERING IN BEHAVIORAL ECONOMICS
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 and within-subject variation in information provision as a formalized tool for the “beliefs” column of our matrix. Second, we integrate insights from Gabaix & Koijen (2023), “Inattention and the Limits of Inflation Stabilization,” whose macro-finance model uses asset price and flow data to back out a time-varying inattention parameter. While their data is observational, their structural approach exemplifies “model-based data engineering,” a category we formalize, where the economic model itself defines the necessary moment conditions that guide what constitutes engineered data. To demonstrate the DEM’s utility, we conduct two proof-of-concept replication-and-extension studies. In the first, we apply the DEM to a classic problem of retirement savings, showing how the matrix prescribes a specific sequence: belief elicitation on returns (following Enke & Graeber’s method) followed by a process-tracing analysis of information acquisition (e.g., using a pension simulator with clickstream data) to separate present bias from exponential growth bias. In the second, we apply the DEM to a laboratory market experiment on price formation, illustrating how the matrix guides the integration of communication transcripts (as a form of process data) with trading outcomes to disentangle strategic uncertainty from fundamental uncertainty. Our results indicate that research designs informed by the DEM achieve significantly higher out-of-sample predictive validity in identifying the dominant cognitive mechanism at play compared to single-instrument approaches. The paper concludes by discussing the DEM’s role as a tool for research design, peer evaluation, and the development of a cumulative science of imperfect decision-making. It argues that such a framework is a prerequisite for the broader application of data engineering beyond cognitive economics, facilitating symbiotic advances in theory and measurement.
Copyright (c) 2026 Rajitha Gentyala (Author) https://creativecommons.org/licenses/by-nc/4.0
A Comparative Study of Monitoring Telemetry Pipelines with Prometheus and Grafana in Cloud-Native Java Applications
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.
Copyright (c) 2022 Glomb Asgarova Caspary (Author) https://creativecommons.org/licenses/by-nc/4.0
Algorithmic Generation of GraphQL Schema Definitions Using Large Language Models Trained on Open-Source APIs
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.
Copyright (c) 2022 Baric Finck Skala (Author) https://creativecommons.org/licenses/by-nc/4.0
END-TO-END SECURITY MODELING IN MICROSERVICES ARCHITECTURES USING OAUTH2 AND JWT IN A SPRING BOOT ECOSYSTEM
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.
Copyright (c) 2022 Pokorny Lagger Bognar (Author) https://creativecommons.org/licenses/by-nc/4.0
Classification Models for Identifying Safety Hazards from Wearable Device Data in Construction Sites
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.
Copyright (c) 2022 Christopher Fuentes William (Author) https://creativecommons.org/licenses/by-nc/4.0
MACHINE LEARNING DRIVEN QUALITY CONTROL SYSTEMS FOR DEFECT DETECTION IN STRUCTURAL COMPONENT FABRICATION
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.
Copyright (c) 2022 Pablo Ross Susan (Author) https://creativecommons.org/licenses/by-nc/4.0
MULTI-AGENT ARTIFICIAL INTELLIGENCE SYSTEMS FOR COORDINATED TASK SCHEDULING IN COMPLEX CONSTRUCTION PROJECTS
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.
Copyright (c) 2022 Kimberly Calvo William (Author) https://creativecommons.org/licenses/by-nc/4.0
DAMAGE DETECTION IN REINFORCED CONCRETE STRUCTURES USING VIBRATION-BASED MONITORING TECHNIQUES
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.
Copyright (c) 2022 Christopher Antonio Labanyi (Author) https://creativecommons.org/licenses/by-nc/4.0
PERFORMANCE-BASED SEISMIC DESIGN FRAMEWORKS FOR RESILIENT INFRASTRUCTURE DEVELOPMENT
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.
Copyright (c) 2022 Adelaida Corbalan Ryan (Author) https://creativecommons.org/licenses/by-nc/4.0
AUGMENTED REALITY TOOLS FOR ENHANCING WORKER TRAINING AND DESIGN VISUALIZATION IN CONSTRUCTION PROJECTS
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.
Copyright (c) 2022 Salvador Giner Carmen (Author) https://creativecommons.org/licenses/by-nc/4.0