International Journal of Computer (IJC)
type: channel
description: The International Journal of Computer (IJC) is an open access International Journal for scientists and researchers to publish their scientific papers in Computer Science related fields. IJC plays its role as a refereed international journal to publish research results conducted by researchers. This journal accepts scientific papers for publication after passing the journal's double peer review process (within 4 weeks). For detailed information about the journal kindly check About the Journal page. All IJC published papers in Computer Science will be available for scientific readers for free; no fees are required to download published papers in this international journal.
publisher: Mohammad Nassar for Researches (MNFR)
language: en
publication name: International Journal of Computer (IJC)
issn: 2307-4523
copyright: Authors who submit papers with this journal agree to the following terms.
items: Data Integrity Validation Methodologies for High-Volume Healthcare ETL Pipelines: Automated Testing Strategies and Quality Assurance Frameworks
items: Ensuring Data Integrity and Regulatory Compliance in Large-Scale Cloud Database Systems
items: The Role of Data Governance in Ensuring Ethical Standards for the Use of Artificial Intelligence Systems
items: SSE Event-Contract Design for Frontend Communication with Intelligent Agents: An Analytical Architectural Perspective
items: A Hybrid Cryptography and Steganography Approach Using RSA and LSB for Secure Message Transmission
items: Economic Coordination and Market Dynamics in Multi-Agent E-Commerce Platforms
items: Agentic Personalization in E-Commerce: From Reactive Recommendation Engines to Goal-Driven Consumer Agents
items: Event Correlation and Request Tracing in Asynchronous Microservice Architectures
items: The Role of Cloud Technologies in the Development of the Connected Car Ecosystem and the Internet of Things
items: Algorithmic Approaches to Trust and Safety in Real-Time Social Discovery
items: Engineering Autonomous Multi-Agent Software Systems: Implementing Hybrid Architectures, Interaction Protocols, and Execution Loops
items: EDI-Driven Dynamic Pricing for Retail Systems Using Real-Time Supply Chain Data
items: Bridging the Paradigm Gap: A Framework for Interoperability between Classical IoT and Quantum Computing Networks
items: Architecture for Integrating Federal Databases into a Unified State Monitoring Perimeter
items: Optimization of Regression Testing Using Graph-Based Dependency Models: A Theoretical Review
items: Evolution of the IT Project Manager’s Role under Hyperautomation of Corporate Processes
items: A Methodology for Secure Remote Deployment of Internet of Things Devices Based on Cryptographic Authentication
items: Evolution of Cloud Architecture for Global Corporate Learning Management Systems
items: Analysis of Migration Strategies for Legacy Software Complexes to Modern Technology Stacks
items: A Gate-Model Approach to Test Layer Scheduling in CI/CD Pipelines and its Effect on Deployment Frequency
items: Rethinking the Methodology of Pair Programming in the Context of Intelligent Agent Integration
items: Semantic Classification of Artificial Intelligence Incidents Based on Vector Embeddings and the TAIM Framework
items: Design and Implementation of an E-Commerce System for Babcock University Supermarket
items: Architectural Patterns for Observability in Enterprise-Grade Distributed Billing Systems
items: Organizational Models for SAP BW Project Teams in International Offshore and Onshore Delivery Structures
items: Personnel Strategies for the Formation of Engineering Teams for High-Load Projects
items: Comparative Analysis of Leader Election Algorithms In Distributed Systems
items: Strategies for Handling Barge-in Interruptions in Conversational AI Interfaces
items: An Econometric Framework for Estimating the Joint Elasticity of Advertising and Promotions on Retail Sales
items: Reducing “Unknown Certificate” Risk at Scale: A Comparative Evaluation of TLS Certificate Discovery Architectures
items: Implementation of Zero-Trust Architecture in Mobile Applications of the Financial Sector
items: Evolution of Cloud LMS Architecture: From Local Spreadsheet Workflows to a Distributed White-Label System
items: Conceptual Approaches to Organizing Feature Stores in High-Load Machine Learning Systems
items: Vehicle Feature VQA: Visual Question Answering for Vehicle Feature
items: Powering the Future of AI – Leveraging Mukkudam SHP's Clean Energy for Sustainable Computational Intelligence
items: AI-Driven Predictive Maintenance in Retail IT Systems Using DevOps: A Review
items: Applying Machine Learning for Network Security Monitoring in Kubernetes Environments
items: Development of Quality Assurance Standards for the Industrialization of Software Applications Prototyped Using Intelligent Assistants
items: Distributed Caching and Real-Time Sync in Active-Active Environments for Critical Cloud Native Systems
items: Methodological Aspects of Integrating Security Processes into Continuous Development Pipelines (CI/CD Security)

Data Integrity Validation Methodologies for High-Volume Healthcare ETL Pipelines: Automated Testing Strategies and Quality Assurance Frameworks
type: item
description: The article examines methodologies for validating data integrity in high-volume healthcare ETL/ELT pipelines where heterogeneous source systems, evolving interoperability standards, and strict compliance constraints amplify the consequences of defects. Relevance stems from the operational dependence of clinical analytics, revenue-cycle reporting, and population health workflows on accurate, traceable, and scalable transformed data. Novelty is provided by an integrated validation model that connects data-quality theory, secure-processing guidance, and modern transformation testing practices into a single quality-assurance workflow tailored to healthcare semantics. The work aims to synthesize automated testing strategies that reduce undetected schema drift, mapping errors, and business-rule violations across batch and near-real-time processing. For this purpose, the article applies analytical review, comparative synthesis, and structured mapping of controls to pipeline stages, drawing on recent peer-reviewed research and authoritative standards. The concluding section formulates implementation-ready recommendations for layered checks, evidence logging, and governance linkages. The article will benefit healthcare data engineers, analytics leaders, and compliance stakeholders responsible for reliable data delivery.
subject: Articles
  topic: section
subject: data integrity
  topic: keywords
subject: healthcare ETL
  topic: keywords
subject: ELT
  topic: keywords
subject: data validation
  topic: keywords
subject: automated testing
  topic: keywords
subject: data quality
  topic: keywords
subject: interoperability
  topic: keywords
subject: dbt testing
  topic: keywords
subject: auditability
  topic: keywords
subject: quality assurance
  topic: keywords
creator: Mehulkumar Joshi
rights: Copyright (c) 2026 Mehulkumar Joshi https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-04-17
publication date: 2026-04-17
starting page: 255
ending page: 264

Ensuring Data Integrity and Regulatory Compliance in Large-Scale Cloud Database Systems
type: item
description: The purpose of the project is to identify architectural mechanisms that ensure data integrity and regulatory compliance in large-scale cloud database systems operating under distributed and multi-cloud conditions. The research problem lies in reconciling high-throughput transactional performance with cryptographically verifiable state evolution and auditability requirements imposed by modern regulatory frameworks. The study addresses this problem through comparative architectural analysis, structural modeling of ledger-based data structures, and synthesis of contemporary research on confidential execution, audit synchronization, semantic analytics, and CI/CD validation mechanisms. The analysis demonstrates that state-oriented verification models, combined with batching strategies, cryptographic aggregation, and synchronized audit layers, reduce verification complexity and embed compliance into the structural configuration of database engines. The conclusions indicate that sustainable regulatory resilience emerges from coordinated interaction between cryptographic data structures, concurrency control, and lifecycle governance rather than from append-only logging alone. The significance of the project lies in providing a unified analytical framework for designing cloud database systems where scalability and compliance are structurally aligned.
subject: Articles
  topic: section
subject: audit architectures
  topic: keywords
subject: cloud databases
  topic: keywords
subject: cryptographic verification
  topic: keywords
subject: data integrity
  topic: keywords
subject: distributed transactions
  topic: keywords
subject: multi-cloud systems
  topic: keywords
subject: regulatory compliance
  topic: keywords
subject: verifiable ledger
  topic: keywords
creator: Ronak Jani
rights: Copyright (c) 2026 Ronak Jani https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-05-11
publication date: 2026-05-11
starting page: 389
ending page: 400

The Role of Data Governance in Ensuring Ethical Standards for the Use of Artificial Intelligence Systems
type: item
description: The article focuses on an in-depth examination of how data governance shapes the maintenance of ethical standards in the deployment and operation of artificial intelligence systems, while the relevance of this inquiry emerges from the accelerating diffusion of AI technologies across domains with substantial societal impact together with the widening discrepancy between formally articulated ethical principles and their concrete operational realization in technological practice, and the scientific contribution of the study resides in the reinterpretation of data governance not as a secondary compliance instrument but as a structuring layer that significantly conditions ethical outcomes in AI infrastructures, with the research further presenting a layered architecture of governance arrangements, exploring oversight procedures that extend across the entire lifecycle of AI development and application, and investigating the relationship between responsible data stewardship and mechanisms of algorithmic supervision. Special attention is paid to large language models, certification regimes, and transnational regulatory coordination. The goal of the study is to systematize governance approaches and identify structural conditions that enable ethical robustness. Comparative analysis, thematic synthesis, and source examination are used to achieve this objective. The conclusion suggests that ethical AI depends on coherent integration of data governance across institutional and lifecycle levels. The article will be useful for researchers, policymakers, AI practitioners, and corporate governance specialists.
subject: Articles
  topic: section
subject: AI governance
  topic: keywords
subject: algorithmic accountability
  topic: keywords
subject: artificial intelligence ethics
  topic: keywords
subject: data governance
  topic: keywords
subject: large language models
  topic: keywords
subject: lifecycle oversight
  topic: keywords
subject: regulatory coordination
  topic: keywords
subject: trustworthiness
  topic: keywords
creator: Priyam Das
rights: Copyright (c) 2026 Priyam Das https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-05-01
publication date: 2026-05-01
starting page: 300
ending page: 311

SSE Event-Contract Design for Frontend Communication with Intelligent Agents: An Analytical Architectural Perspective
type: item
description: This article develops an analytical architectural perspective on the use of Server-Sent Events (SSE) in frontend communication with intelligent agents in user-facing systems. The study synthesizes a bounded corpus of ten publications spanning real-time web transport, agent communication protocols, multi-agent orchestration, dynamic interface generation, observability, retrieval-grounded generation, output-layer security, and lifecycle evaluation. The purpose is to formulate a transparent conceptual model of browser-facing event streaming and to derive architecture-level design propositions for stable rendering, controlled state exposure, observable execution, governed latency handling, and tenant-safe delivery. The analysis indicates that SSE is analytically well-suited to browser-facing agent streaming when implemented as a typed event channel within a broader event-governed architecture. The article should therefore be read as a conceptual architectural proposal for React-based interfaces, agent platforms, and production teams designing streaming assistants.
subject: Articles
  topic: section
subject: server-sent events
  topic: keywords
subject: intelligent agents
  topic: keywords
subject: frontend architecture
  topic: keywords
subject: React
  topic: keywords
subject: streaming protocols
  topic: keywords
subject: agent communication
  topic: keywords
subject: generative UI
  topic: keywords
subject: observability
  topic: keywords
subject: RAG
  topic: keywords
subject: output validation
  topic: keywords
creator: Tounzal Elias
rights: Copyright (c) 2026 Tounzal Elias https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-04-17
publication date: 2026-04-17
starting page: 245
ending page: 254

A Hybrid Cryptography and Steganography Approach Using RSA and LSB for Secure Message Transmission
type: item
description: Information security has become a critical concern in digital communication, requiring robust methods to ensure both confidentiality and imperceptibility. Cryptographic techniques such as RSA effectively protect message content by transforming plaintext into ciphertext; however, the presence of encrypted data may raise suspicion and become a target for attacks. Conversely, steganography conceals the existence of the message but lacks strong protection against extraction if detected. This study proposes a hybrid approach that integrates RSA cryptography with Least Significant Bit (LSB) steganography to enhance secure message transmission. The proposed method first encrypts the secret message using RSA, and subsequently embeds the resulting ciphertext into a digital image using the LSB technique. The system is implemented in Python and evaluated using Mean Square Error (MSE) and Peak Signal-to-Noise Ratio (PSNR) to assess image quality and imperceptibility. Experimental results demonstrate that the proposed method achieves high PSNR values (above 49 dB) and low MSE, indicating minimal visual distortion and high fidelity of the stego image. The integration of RSA and LSB not only secures the message content but also effectively conceals its presence, providing a dual layer of security. Therefore, the proposed hybrid method is suitable for secure digital communication applications requiring both data confidentiality and stealth.
subject: Articles
  topic: section
subject: Information security
  topic: keywords
subject: cryptography
  topic: keywords
subject: steganography
  topic: keywords
subject: RSA
  topic: keywords
subject: Least Significant Bit (LSB)
  topic: keywords
subject: data hiding
  topic: keywords
subject: secure communication
  topic: keywords
subject: image steganography
  topic: keywords
creator: Ida Ayu Gde Suwiprabayanti Putra
creator: Gst. Ayu Vida Mastrika Giri
creator: Ngakan Putu Bagus Ananta Wijaya
creator: Desak Putu Tia Rusilia Wati
rights: Copyright (c) 2026 Ida Ayu Gde Suwiprabayanti Putra, Gst. Ayu Vida Mastrika Giri, Ngakan Putu Bagus Ananta Wijaya, Desak Putu Tia Rusilia Wati https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-04-17
publication date: 2026-04-17
starting page: 229
ending page: 244

Economic Coordination and Market Dynamics in Multi-Agent E-Commerce Platforms
type: item
description: The rapid expansion of digital marketplaces has transformed traditional commercial interactions by enabling large-scale transactions between buyers and sellers through online platforms. Within these environments, economic coordination among numerous participants becomes increasingly complex due to heterogeneous preferences, dynamic pricing behaviors, and evolving market competition. Multi-agent systems provide a computational framework capable of modeling decentralized decision-making processes and automated interactions among economic actors in such digital ecosystems. This study investigates the mechanisms of economic coordination and the resulting market dynamics in multi-agent e-commerce platforms by integrating theories of platform economics with agent-based modeling techniques. The research develops a conceptual framework in which buyer agents, seller agents, and platform coordination agents interact through negotiation protocols, pricing strategies, and reputation-based trust mechanisms. Using an agent-based simulation approach, the study evaluates how coordination strategies influence transaction success rates, market participation, and consumer attention distribution across competing platforms. The results indicate that reputation-driven coordination and adaptive negotiation strategies significantly improve transaction reliability and market efficiency compared with centralized coordination models. In addition, the analysis highlights the influence of two-sided market structures, platform governance mechanisms, and consumer attention patterns on the evolution of digital commerce ecosystems. These findings contribute to the growing body of knowledge on digital platform economics by demonstrating how autonomous agent interactions shape economic outcomes in complex online marketplaces. The study also provides practical insights for platform operators seeking to design efficient coordination mechanisms that enhance trust, optimize pricing strategies, and suppor…
subject: Articles
  topic: section
subject: Multi-Agent Systems
  topic: keywords
subject: E-Commerce Platforms
  topic: keywords
subject: Platform Economics
  topic: keywords
subject: Agent-Based Modeling
  topic: keywords
subject: Market Coordination
  topic: keywords
subject: Digital Market Dynamics
  topic: keywords
creator: Piyush Tiwari
rights: Copyright (c) 2026 Piyush Tiwari https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-05-11
publication date: 2026-05-11
starting page: 366
ending page: 388

Agentic Personalization in E-Commerce: From Reactive Recommendation Engines to Goal-Driven Consumer Agents
type: item
description: Personalization has become a central capability in modern e-commerce platforms, enabling digital marketplaces to provide tailored product recommendations that enhance user experience and commercial performance. Traditional recommender systems primarily operate using reactive mechanisms that analyze historical user interactions, browsing patterns, and purchase behaviors to generate suggestions. Although these systems have significantly improved product discovery and engagement, they often struggle to capture evolving user intentions, contextual preferences, and long-term decision goals. As digital commerce ecosystems become increasingly complex, there is a growing need for more intelligent personalization strategies capable of proactive assistance. This study explores the emerging paradigm of agentic personalization, which represents a shift from conventional recommendation engines toward autonomous, goal-driven consumer agents. Unlike traditional systems that passively respond to past interactions, agentic personalization systems actively interpret user objectives, reason about possible actions, and optimize recommendations to achieve long-term user satisfaction. The research proposes a conceptual architecture that integrates user goal inference, contextual knowledge modeling, and reinforcement learning based policy optimization to support intelligent consumer agents within e-commerce environments. Experimental evaluation using simulated large-scale interaction datasets compares traditional collaborative filtering models, deep neural recommendation architectures, and the proposed agentic framework. The results demonstrate that agent-driven personalization significantly improves recommendation accuracy, user engagement levels, and conversion rates compared with conventional reactive recommendation approaches. These findings highlight the potential of goal-driven consumer agents to transform the next generation of adaptive and intelligent digital commerce systems.
subject: Articles
  topic: section
subject: Agentic personalization
  topic: keywords
subject: E-commerce recommender systems
  topic: keywords
subject: Consumer agents
  topic: keywords
subject: Reinforcement learning
  topic: keywords
subject: Intelligent recommendation systems
  topic: keywords
subject: Digital commerce personalization
  topic: keywords
creator: Piyush Tiwari
rights: Copyright (c) 2026 Piyush Tiwari https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-05-11
publication date: 2026-05-11
starting page: 344
ending page: 365

Event Correlation and Request Tracing in Asynchronous Microservice Architectures
type: item
description: This article presents an analytical synthesis of scientific approaches to examining event correlation and request tracing in asynchronous microservice architectures. The study is conducted as a systematic analysis of peer-reviewed publications and focuses on interpreting the architectural and operational factors that determine the reconstruction of causal relationships under event-driven and hybrid inter-service communication. Particular attention is given to the impact of asynchrony, message delivery semantics, fault-tolerance mechanisms, and architectural antipatterns on the interpretability of distributed traces and the robustness of event correlation. It is demonstrated that traditional linear request tracing proves insufficient for explaining the behavior of asynchronous systems, whereas event correlation assumes the role of a foundational architectural mechanism rather than a supplementary observability tool. The findings indicate that correlation degradation exhibits a systemic nature and is largely driven by the structure of interactions among services, execution parameters, and the quality of context propagation, rather than by the selection of specific monitoring instruments. The transition toward managed analysis and response loops is shown to amplify the requirements for architectural consistency in correlation, as event-binding errors lead to distorted analytical conclusions and incorrect control interventions. The obtained results refine the architectural conditions for robust event correlation and can be applied in the design and operation of asynchronous microservice platforms with stringent demands for explainability and manageability.
subject: Articles
  topic: section
subject: microservice architecture
  topic: keywords
subject: asynchronous systems
  topic: keywords
subject: event correlation
  topic: keywords
subject: request tracing
  topic: keywords
subject: observability
  topic: keywords
subject: distributed traces
  topic: keywords
creator: Gleb D. Shkriabin
rights: Copyright (c) 2026 Gleb D. Shkriabin https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-04-17
publication date: 2026-04-17
starting page: 219
ending page: 228

The Role of Cloud Technologies in the Development of the Connected Car Ecosystem and the Internet of Things
type: item
description: The article examines how cloud technologies shape the connected car ecosystem and its interaction with the Internet of Things. Relevance follows from the rapid growth of software-defined vehicles, continuous telemetry streams, and service models based on remote computation, storage, and lifecycle analytics. Novelty is associated with an integrated synthesis that links in-vehicle data acquisition, edge/fog mediation, and cloud-native backends to concrete engineering decisions in automotive software production, with emphasis on Google Cloud Platform–oriented stacks and Java-centric enterprise integration. The work aims to systematize architectural patterns that enable scalable ingestion, secure communication, fleet-level management, and data-driven services across vehicles, roadside infrastructure, and IoT platforms. The study applies analytical review, comparative reasoning, and structured source analysis to examine peer-reviewed research and provider architectures. The conclusion formulates technology implications for platform design, data governance, and deployment practices in automobile software, highlighting trade-offs among latency, resilience, cost, and security. The article targets researchers and practitioners in automotive IT, cloud engineering, and applied AI.
subject: Articles
  topic: section
subject: connected car
  topic: keywords
subject: Internet of Things
  topic: keywords
subject: cloud computing
  topic: keywords
subject: edge computing
  topic: keywords
subject: fog computing
  topic: keywords
subject: vehicle telemetry
  topic: keywords
subject: cloud-native architecture
  topic: keywords
subject: Google Cloud Platform
  topic: keywords
subject: automotive software
  topic: keywords
subject: software-defined vehicle
  topic: keywords
creator: Madugula Abhiram
rights: Copyright (c) 2026 Madugula Abhiram https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-04-17
publication date: 2026-04-17
starting page: 211
ending page: 218

Algorithmic Approaches to Trust and Safety in Real-Time Social Discovery
type: item
description: This article examines algorithmic approaches to trust and safety in real-time social discovery systems under conditions of increasing interaction speed, uncertainty, and accelerated transitions from online communication to offline encounters. The study is based on an analytical synthesis of contemporary empirical, computational, and architectural research in which trust and safety are interpreted not as auxiliary moderation functions, but as systemic properties of algorithmic organization. The analysis draws on studies addressing perceived risk, user value and loyalty, automated detection of harmful content, trust inference in social graphs, robustness under privacy constraints, and the deployment of production-scale safety systems. It is shown that user risk perception affects platform value and loyalty primarily through structural properties of algorithms, including ranking logic, predictability of recommendations, and transparency of decision-making, rather than through isolated negative incidents. The results demonstrate that effective trust and safety mechanisms depend on the integration of trust inference and risk assessment directly into search and ranking processes, as well as on the ability of models to remain robust under partial data availability. Particular attention is given to the trade-off between accuracy, interpretability, and scalability, highlighting the limitations of relying solely on complex models with higher formal accuracy. The study argues that sustainable real-time social discovery requires multi-level algorithmic architectures combining interpretable trust models, adaptive ranking, risk-aware visibility control, and human oversight. The article may be of interest to researchers and practitioners in social computing, recommender systems, and the design of trust- and safety-oriented digital platforms. The main contribution of this study lies in conceptualizing trust and safety as an intrinsic property of algorithmic architectures in real-ti…
subject: Articles
  topic: section
subject: trust and safety
  topic: keywords
subject: real-time social discovery
  topic: keywords
subject: perceived risk
  topic: keywords
subject: trust inference
  topic: keywords
subject: algorithmic ranking
  topic: keywords
subject: interpretability
  topic: keywords
subject: social platforms
  topic: keywords
creator: Venkata Karunakara Reddy Revunuru
rights: Copyright (c) 2026 Venkata Karunakara Reddy Revunuru https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-04-17
publication date: 2026-04-17
starting page: 200
ending page: 210

Engineering Autonomous Multi-Agent Software Systems: Implementing Hybrid Architectures, Interaction Protocols, and Execution Loops
type: item
description: The paper examines the engineering transition from static software paradigms to autonomous agentic architectures (Software Engineering 3.0). Instead of focusing on organizational theory, the analysis concentrates on the technical implementation of functional subject attributes in software agents: goal planning, causal reasoning, and standardized execution. Key architectural patterns for deploying distributed multi-agent systems (MAS) are synthesized, specifically focusing on the integration of generative models with Case-Based Reasoning for strictly typed decision reproducibility. The study details the technical requirements for Agent-to-Agent (A2A) communication protocols and the establishment of stable behavioral contracts between autonomous entities. Implementation challenges are addressed through the lens of data engineering, specifically context memory management and vector data integration within existing IT landscapes. Furthermore, the paper structures the technical aspects of Artificial Intelligence Trust, Risk, and Security Management (AI TRiSM), defining methods for behavioral control, immutable logging, and decision traceability in high-load environments. The efficiency analysis is reframed from general business ROI to specific engineering metrics, correlating system performance with the costs of development, integration, and computational maintenance.
subject: Articles
  topic: section
subject: Software Engineering 3.0
  topic: keywords
subject: Multi-agent systems implementation
  topic: keywords
subject: Agent-to-Agent protocols
  topic: keywords
subject: Hybrid software architectures
  topic: keywords
subject: AI TRiSM
  topic: keywords
subject: System integration
  topic: keywords
subject: Autonomous execution loops
  topic: keywords
creator: Mykhailo Nykoliuk
rights: Copyright (c) 2026 Mykhailo Nykoliuk https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-04-23
publication date: 2026-04-23
starting page: 275
ending page: 289

EDI-Driven Dynamic Pricing for Retail Systems Using Real-Time Supply Chain Data
type: item
description: Retail pricing decisions depend on several operational variables, including supplier costs, customer demand, and inventory availability. Many retailers still implement scheduled price updates through manual processing or periodic calculations based on databases. This may cause a delay between supply chain events and price changes. Electronic Data Interchange (EDI) systems are already used for the transmission of large volumes of data between suppliers and retailers in a structured format. The data exchanged includes information on supplier costs, confirmations, and inventory levels. However, the data exchanged through EDI systems is rarely integrated directly with pricing algorithms. This study proposes a system architecture that connects EDI transaction data with automated pricing models. The framework integrates an EDI message interface, transaction processing layer, centralized retail database, and pricing engine. The proposed architecture demonstrates how supply chain events transmitted through EDI messages can trigger automated pricing calculations, providing continuous operational input for retail pricing decisions.
subject: Articles
  topic: section
subject: Electronic Data Interchange
  topic: keywords
subject: Dynamic Pricing; Retail Systems
  topic: keywords
subject: Supply Chain Integration
  topic: keywords
subject: Pricing Engine
  topic: keywords
subject: Real-Time Data
  topic: keywords
creator: Konstantin Nikolaev
rights: Copyright (c) 2026 Konstantin Nikolaev https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-05-08
publication date: 2026-05-08
starting page: 338
ending page: 343

Bridging the Paradigm Gap: A Framework for Interoperability between Classical IoT and Quantum Computing Networks
type: item
description: The proliferation of the Internet of Things has created a massive ecosystem of classical devices that are increasingly constrained by security and computational limits. While quantum computing offers a revolutionary paradigm for data processing, a "paradigm gap" exists between classical binary telemetry and quantum state representations. This paper proposes a software-defined middleware framework designed to bridge this gap. Our primary contribution is a novel Digit-Based Gate Mapping (DBGM) method that translates standard MQTT-based IoT data into quantum circuit configurations. By positioning the Translator Core at the application layer, we enable heterogeneous classical devices to participate in quantum workflows without hardware modification. This architectural foundation establishes the feasibility of Quantum-as-a-Service for legacy IoT infrastructure.
subject: Articles
  topic: section
subject: Classical-Quantum Bridge
  topic: keywords
subject: Digit-Based Gate Mapping
  topic: keywords
subject: Heterogeneous Networks
  topic: keywords
subject: Protocol Translation
  topic: keywords
subject: Quantum Gate Encoding
  topic: keywords
subject: Quantum IoT Interoperability
  topic: keywords
subject: Quantum Middleware
  topic: keywords
subject: Software-Defined Integration
  topic: keywords
creator: Abu Elhassan M. Elamin
rights: Copyright (c) 2026 Abu Elhassan M. Elamin https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-04-23
publication date: 2026-04-23
starting page: 265
ending page: 274

Architecture for Integrating Federal Databases into a Unified State Monitoring Perimeter
type: item
description: The article examines the strategic relevance of database integration amid fragmented regulatory data, cross-agency dependence, and a growing demand for evidence-based public oversight. Its purpose is to develop a theoretical model and target architecture that connect governance arrangements, metadata management, data quality rules, lineage requirements, and platform design within a single supervisory system. The study proceeds from the premise that effective state monitoring depends on semantically aligned, traceable, and standardized data flows across heterogeneous federal and departmental sources. The novelty of the article lies in the proposed model and architecture of a unified state monitoring perimeter grounded in DAMA-DMBOK principles and adapted to regulatory banking practice. The main conclusions show that integration should be organized through a layered architecture including source systems, an integration layer, a harmonization layer, a quality and lineage control layer, and a unified analytical platform functioning as a Single Source of Truth. The article also demonstrates that federated data governance with centralized standards provides the institutional basis for accountability, coherence, and supervisory use of integrated data. The article will be useful for researchers, public administrators, financial regulators, and designers of state information systems.
subject: Articles
  topic: section
subject: Federal databases
  topic: keywords
subject: unified state monitoring perimeter
  topic: keywords
subject: data governance
  topic: keywords
subject: integration architecture
  topic: keywords
subject: DAMA-DMBOK
  topic: keywords
subject: metadata
  topic: keywords
subject: data lineage
  topic: keywords
creator: Pratikkumar Chaudhari
rights: Copyright (c) 2026 Pratikkumar Chaudhari https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-06-02
publication date: 2026-06-02
starting page: 449
ending page: 461

Optimization of Regression Testing Using Graph-Based Dependency Models: A Theoretical Review
type: item
description: The paper provides a theoretical justification for optimizing regression testing using graph-based dependency models under conditions of frequent releases and high test execution costs. The relevance of the study is that the growth of software systems and the load on continuous integration infrastructure make full regression testing a chronic bottleneck, requiring formalized approaches to selecting, ordering, and reducing test suites at a controlled level of risk. The aim of the review is to synthesize the results of eight peer-reviewed studies and to construct a unified conceptual framework in which regression testing is interpreted as a risk-management problem over a dependency structure rather than as a mechanical re-execution of everything. The scientific novelty lies in interpreting the dependency graph as a carrier of a risk field, where edge and node weights reflect the strength of influence, interaction frequency, and node criticality, and an affected test is defined through reachability within an influence subgraph, subject to depth constraints and significance thresholds. The review systematizes three main classes of graph-based models (source-code level, component/service level, and bipartite tests–code graphs) and relates them to the operations of selection, prioritization, minimization, and time-budgeting, supplementing them with requirements for interpretability, reproducibility, and accounting for the total cost of ownership of the model. As a promising trajectory, the paper outlines a transition towards adaptive multimodal code–test–requirement–incident graphs enriched with observability data and learnable estimates of regression probability. The paper is intended for researchers and practicing software quality engineers who design or deploy methods for optimizing regression testing in scalable CI/CD processes.
subject: Articles
  topic: section
subject: regression testing
  topic: keywords
subject: graph-based dependency models
  topic: keywords
subject: change impact analysis
  topic: keywords
subject: test selection
  topic: keywords
subject: test prioritization
  topic: keywords
creator: Pranay Raj Kanakala
rights: Copyright (c) 2026 Pranay Raj Kanakala https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-06-12
publication date: 2026-06-12
starting page: 462
ending page: 472

Evolution of the IT Project Manager’s Role under Hyperautomation of Corporate Processes
type: item
description: The article examines how hyperautomation changes the role of the IT project manager in large corporate environments and explains the shift from manual coordination to event-driven process orchestration. The study addresses the growing complexity of distributed IT operations, stricter regulatory expectations, and the limits of managerial routines built on fragmented communication, manual checks, and dependence on individual actors. The article is designed as a conceptual paper grounded in literature on operational resilience, human error, distributed coordination, and project management, with SOAS used as an illustrative architecture rather than as a validated case study. The central contribution is a conceptual model of the IT orchestrator, a project leader who defines event logic, routing rules, validation layers, and escalation paths across interconnected systems. The analysis shows that hyperautomation changes the managerial object of work. Attention shifts toward process architecture, exception design, latent vulnerability mapping, and control of integration delay. A competency profile for the IT orchestrator is proposed. The article may be useful for researchers of digital governance, IT project managers, business process architects, and digital transformation leaders.
subject: Articles
  topic: section
subject: hyperautomation
  topic: keywords
subject: IT project management
  topic: keywords
subject: operational resilience
  topic: keywords
subject: integration delay
  topic: keywords
creator: Ievgenii Lysenko
rights: Copyright (c) 2026 Ievgenii Lysenko https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-05-01
publication date: 2026-05-01
starting page: 290
ending page: 299

A Methodology for Secure Remote Deployment of Internet of Things Devices Based on Cryptographic Authentication
type: item
description: The article is dedicated to the development of a methodological framework for secure remote deployment of Internet of Things devices based on cryptographic authentication. The relevance of the study is determined by the expansion of distributed IoT infrastructures, the growing dependence of industrial and service environments on remote device administration, and the rising exposure of connected endpoints to impersonation, firmware tampering, unauthorized enrollment, and trust-chain disruption. The scientific novelty lies in the integrated interpretation of remote deployment as a multi-phase cryptographic lifecycle rather than as an isolated provisioning event. The work describes the structural logic of identity provisioning, attestation-assisted trust establishment, secure channel creation, authenticated firmware distribution, and decentralized verification. Special attention is paid to hardware-backed onboarding, software-based attestation for constrained and legacy devices, segmented update delivery, and distributed trust validation. The work sets itself the goal of systematizing architectural and security principles that shape contemporary approaches to protected remote IoT deployment. To solve this task, comparative analysis, source synthesis, structural interpretation, and analytical generalization were used. The conclusion describes the transition toward lifecycle-oriented security models. The article will be useful for researchers, system architects, and engineers working with secure IoT infrastructures.
subject: Articles
  topic: section
subject: Internet of Things
  topic: keywords
subject: secure deployment
  topic: keywords
subject: cryptographic authentication
  topic: keywords
subject: remote attestation
  topic: keywords
subject: firmware update
  topic: keywords
subject: device provisioning
  topic: keywords
subject: decentralized trust
  topic: keywords
subject: secure onboarding
  topic: keywords
subject: IoT security
  topic: keywords
subject: certificate-based identity
  topic: keywords
creator: Kydiuk Oleksandr
rights: Copyright (c) 2026 Kydiuk Oleksandr https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-05-08
publication date: 2026-05-08
starting page: 325
ending page: 337

Evolution of Cloud Architecture for Global Corporate Learning Management Systems
type: item
description: The article examines the evolution of cloud architecture in global corporate learning management systems as a response to the expansion of workforce training across dispersed organizational structures, hybrid work settings, cross-border compliance regimes, and continuous reskilling demands. The study aims to explain how architectural change has transformed the LMS from a centralized training platform into a distributed enterprise infrastructure embedded in broader systems of organizational capability. Its relevance stems from the growing dependence of multinational firms on digital learning environments that support scalability, resilience, integration, governance, and data protection. The novelty of the article lies in its synthesis of workplace learning research, cloud migration studies, software architecture scholarship, and data governance literature into a single sociotechnical account of LMS development. The article shows that the transition from on-premises systems to cloud-hosted legacy platforms, SaaS solutions, and cloud-native service ecosystems reflects a structural redefinition of enterprise learning infrastructure. The main findings indicate that cloud-native architectures provide stronger selective scaling, fault isolation, integration with HR and business systems, and support for analytics- and AI-based learning functions, while security, privacy, tenant isolation, and regulatory compliance remain constitutive design conditions. This article will be useful for researchers in digital education and information systems, enterprise architects, LMS developers, and corporate learning strategists.
subject: Articles
  topic: section
subject: cloud architecture
  topic: keywords
subject: learning management systems
  topic: keywords
subject: corporate learning
  topic: keywords
subject: cloud-native systems
  topic: keywords
subject: SaaS
  topic: keywords
subject: microservices
  topic: keywords
creator: Penmetsa Murali Krishna Raju
rights: Copyright (c) 2026 Penmetsa Murali Krishna Raju https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-06-02
publication date: 2026-06-02
starting page: 437
ending page: 448

Analysis of Migration Strategies for Legacy Software Complexes to Modern Technology Stacks
type: item
description: The article examines strategies for migrating legacy software systems to modern technology stacks, accounting for architectural constraints, technical debt, hidden domain logic, and organizational dependencies. The aim of the study is to compare the principal transition trajectories, identify the factors shaping strategy selection, and determine the conditions under which migration preserves the operability of critical functions and establishes a foundation for the development of the enterprise digital environment. The study's relevance lies in the growing requirements for integration, scaling, independent component deployment, and the manageability of the engineering lifecycle, which legacy systems often struggle with. The novelty of the article lies in considering migration as a unified architectural, technological, and organizational process in which the choice of strategy is determined by the condition of the source complex, the density of integrations, the value of accumulated logic, and the level of acceptable risk. It is concluded that no universal migration strategy exists and that the greatest resilience is provided by a phased transition based on architectural audit, inventory of data and interfaces, sequential transfer of functions, and control of the integrity of application scenarios. The article will be useful to researchers, software system architects, modernization engineers, and IT transformation managers.
subject: Articles
  topic: section
subject: legacy software complexes
  topic: keywords
subject: system migration
  topic: keywords
subject: technology stack
  topic: keywords
subject: microservice architecture
  topic: keywords
subject: software modernization
  topic: keywords
creator: Abdul Nadeem Mohammed
rights: Copyright (c) 2026 Abdul Nadeem Mohammed https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-05-08
publication date: 2026-05-08
starting page: 312
ending page: 324

A Gate-Model Approach to Test Layer Scheduling in CI/CD Pipelines and its Effect on Deployment Frequency
type: item
description: The article examines a gate model for scheduling test layers in CI/CD pipelines and its effect on deployment frequency, feedback speed, and the resilience of software delivery processes. The relevance of the study is determined by the growing complexity of microservice systems and by the limitations of monolithic E2E runs, which slow down change validation, increase infrastructure load, and reduce confidence in automated testing results. The aim of the work is to develop and empirically verify an architecture that links test layers with discrete quality gates and software development lifecycle events. The article's scientific novelty lies in formalizing the relationship among test granularity, trigger-based execution schedules, and CI/CD pipeline throughput. It is shown that moving lightweight checks to the commit and Pull Request stages, while retaining E2E tests in the schedule, enables reducing total test execution time by 90%, decreasing flakiness from 25% to 2%, and increasing deployment frequency to multiple deployments per day. The article will be useful for DevOps engineers, system architects, QA leads, and software engineering researchers.
subject: Articles
  topic: section
subject: CI/CD
  topic: keywords
subject: gate model
  topic: keywords
subject: trigger model
  topic: keywords
subject: DORA metrics
  topic: keywords
subject: deployment frequency
  topic: keywords
subject: test decomposition
  topic: keywords
subject: Shift-Left
  topic: keywords
creator: Skadorva Vitali
rights: Copyright (c) 2026 Skadorva Vitali https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-06-02
publication date: 2026-06-02
starting page: 422
ending page: 436

Rethinking the Methodology of Pair Programming in the Context of Intelligent Agent Integration
type: item
description: This article examines the transformation of pair programming methodology in the context of integrating intelligent assistants based on large language models. The study is conducted as a structured narrative review and analytical synthesis of academic publications devoted to AI-assisted programming, human–AI collaboration, and collaborative software development. The main focus is on changes in the structure of developer interaction, the redistribution of roles, and the reconfiguration of cognitive functions between human participants and intelligent agents. Key characteristics of traditional and AI-assisted pair programming are compared, including differences in evaluation parameters, learning effects, and behavioural outcomes. It is shown that the influence of intelligent assistants is not limited to improving productivity, but is realised through changes in the organisation and perception of the development process. It is also demonstrated that isolated use of AI tools does not fundamentally alter collaborative practices unless they are integrated into a coherent interaction structure. An original model of AI-integrated pair programming is proposed, describing the distributed nature of solution generation, evaluation, and refinement within a human–AI system. The results make it possible to consider pair programming as a hybrid form of collaboration, where the effectiveness of development depends on the coordination between human and intelligent components. The article may be of interest to software engineering researchers, educators in computing disciplines, and developers of AI-supported programming tools.
subject: Articles
  topic: section
subject: pair programming
  topic: keywords
subject: AI-assisted programming
  topic: keywords
subject: intelligent agents
  topic: keywords
subject: human–AI collaboration
  topic: keywords
subject: software development
  topic: keywords
subject: code generation
  topic: keywords
subject: large language models
  topic: keywords
creator: Sergei Kuznetsov
rights: Copyright (c) 2026 Sergei Kuznetsov https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-06-02
publication date: 2026-06-02
starting page: 410
ending page: 421

Semantic Classification of Artificial Intelligence Incidents Based on Vector Embeddings and the TAIM Framework
type: item
description: The article examines the semantic classification of artificial intelligence incidents based on vector embeddings and the TAIM framework. The aim of the study is to develop and validate a methodology that links textual descriptions of AI incidents with the Govern, Map, Measure, and Manage domains. The relevance of the work is determined by the growing number of incidents, the fragmentation of data sources, and the limitations of manual expert annotation. The scientific novelty lies in the construction of an end-to-end pipeline that transforms unstructured reports into quantitative metrics of semantic similarity with TAIM control areas, while preserving confidentiality through local use of Ollama. It is shown that vector embeddings increase the completeness of identifying conceptually related risks, including cases of terminological divergence. The largest share of incidents falls within the Manage domain, which indicates the importance of infrastructural, organizational, and procedural response measures. The obtained results confirm the potential of semantic mapping for AI audit, monitoring, and risk management. The article will be useful for specialists in AI Governance, information security, compliance, AI auditing, and researchers of digital technology risks.
subject: Articles
  topic: section
subject: artificial intelligence
  topic: keywords
subject: AI incidents
  topic: keywords
subject: semantic classification
  topic: keywords
subject: vector embeddings
  topic: keywords
subject: TAIM framework
  topic: keywords
subject: AI risk management
  topic: keywords
subject: cosine similarity
  topic: keywords
creator: Anton Kulyk
rights: Copyright (c) 2026 Anton Kulyk https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-06-02
publication date: 2026-06-02
starting page: 401
ending page: 409

Design and Implementation of an E-Commerce System for Babcock University Supermarket
type: item
description: Today’s buying and selling can be seamless only with the involvement of e-commerce systems. At Babcock University, Ilisan-Remo, Ogun State, Nigeria, students and member of staff often face challenges such as standing in long queues to make payments, price discrepancies, and limited product availability while shopping at the university supermarket. This study seeks to tackle these issues by building an e-commerce system for Babcock University Supermarket. The incremental process model was adopted during the system development. The implementation tools include HTML, CSS, JavaScript (for the interactive/friendly interface), PHP and MySQL (for the backend and data management). The system also includes a chatbot integrated via FastBots API, which helps users with common queries and navigation. Another key feature is the content-based filtering recommendation system, which suggests products to users based on their browsing history. In conclusion, this AI-powered e-commerce system provides an efficient solution for improving supermarket operations in a university setting. This helps make shopping easier, faster, and more efficient by providing an online platform where one can browse and purchase products without hassle. The result shows how AI and e-commerce can be used to enhance retail efficiency and customer satisfaction. Future direction could incorporate integrating mobile payment options, enhancing the AI features for better product recommendations, and introducing delivery tracking to further improve shopping experience.
subject: Articles
  topic: section
subject: E-commerce
  topic: keywords
subject: Chatbot Integration
  topic: keywords
subject: Content Filtering
  topic: keywords
subject: Personalized Recommendations
  topic: keywords
subject: Recommender System
  topic: keywords
creator: Akinbogun Morontowumi
creator: Oganyi Kevin Gift
creator: Afelogun Moshope
creator: Adekola Olubukola D
creator: Daramola Christiana Jumoke
creator: Sanusi Funmilayo
rights: Copyright (c) 2026 Akinbogun Morontowumi, Oganyi Kevin Gift, Afelogun Moshope, Adekola Olubukola D, Daramola Christiana Jumoke, Sanusi Funmilayo https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-07-04
publication date: 2026-07-04
starting page: 482
ending page: 502

Architectural Patterns for Observability in Enterprise-Grade Distributed Billing Systems
type: item
description: Enterprise-grade billing platforms process high-volume monetary events across loosely coupled services, where technical failures and semantic inconsistencies often emerge as delayed, fragmented, or weakly correlated signals. This article develops an analytical model of observability for distributed billing systems and treats observability as an architectural property of the billing flow itself. The study aims to identify stable architectural patterns that improve traceability, diagnostic precision, and operational decision quality in billing-critical environments. The materials consist of ten recent peer-reviewed sources on cloud-native observability, distributed tracing, anomaly detection, root cause analysis, trace sampling, and proactive SLO management. The methods combine source analysis, comparative synthesis, typologization, and analytical generalization. The analytical part derives three pattern groups: correlation-first telemetry design, economically bounded trace selection, and decision-oriented observability loops. The proposed interpretation applies to rating, charging, invoicing, reconciliation, and settlement pipelines, where monetary correctness, auditability, and rapid incident isolation shape system design.
subject: Articles
  topic: section
subject: distributed billing systems
  topic: keywords
subject: observability
  topic: keywords
subject: distributed tracing
  topic: keywords
subject: cloud-native architecture
  topic: keywords
subject: telemetry design
  topic: keywords
subject: anomaly detection
  topic: keywords
subject: root cause analysis
  topic: keywords
subject: trace sampling
  topic: keywords
subject: SLO management
  topic: keywords
subject: enterprise systems
  topic: keywords
creator: Ankit Rawat
rights: Copyright (c) 2026 Ankit Rawat https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-06-25
publication date: 2026-06-25
starting page: 473
ending page: 481

Organizational Models for SAP BW Project Teams in International Offshore and Onshore Delivery Structures
type: item
description: This article examines the link between team organization, reporting stability, data reliability, and post-go-live trust in SAP BW environments affected by SAP S/4HANA transformation. Migration programs now unfold under budget pressure, legacy complexity, fragmented ownership, and narrow testing windows. These conditions shift part of project risk from technical conversion into coordination, verification, and decision control across delivery units. The study explains which delivery structures retain control over source extraction, transformation logic, reconciliation, and reporting validation across international offshore and onshore settings. The analytical base combines recent scholarly publications with industry benchmark reports selected for their relevance to ERP post-implementation stability, distributed software collaboration, data governance, and S/4HANA migration pressure. The article develops a conceptual reading of centralized, hybrid, and fragmented team models and formulates practical principles for team design, escalation governance, testing sequence, and monitoring. The proposed interpretation connects delivery structure with the durability of reporting outcomes after go-live.
subject: Articles
  topic: section
subject: SAP BW
  topic: keywords
subject: SAP S/4HANA
  topic: keywords
subject: offshore delivery
  topic: keywords
subject: onshore delivery
  topic: keywords
subject: distributed teams
  topic: keywords
subject: reporting reproducibility
  topic: keywords
subject: data quality
  topic: keywords
subject: ERP transformation
  topic: keywords
subject: testing governance
  topic: keywords
subject: delivery model
  topic: keywords
creator: Baris Ozcan
rights: Copyright (c) 2026 Baris Ozcan https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-07-05
publication date: 2026-07-05
starting page: 503
ending page: 513

Personnel Strategies for the Formation of Engineering Teams for High-Load Projects
type: item
description: The article is devoted to the study and development of personnel strategies for the formation and management of engineering teams working on projects with high loads. The relevance is determined by the increasing complexity of IT products and the importance of system stability and performance. The scientific novelty lies in the proposed integrated model that combines predictive analysis of staffing needs, a competency matrix for high-load systems, and adaptive methodologies for team management. The work describes traditional and modern approaches to recruiting and developing engineers and examines cases of successful team scaling in technology companies. Particular attention within the study is paid to the role of technical leadership and the creation of a culture conducive to innovation and stress resilience. The aim of the work is to develop a comprehensive strategy that enables the formation of effective and resilient engineering teams. To achieve this, methods of scientific literature analysis, synthesis, modeling, and the study of practical experience are used. Sources devoted to talent management, organizational psychology, and agile development methodologies are examined. The conclusion describes the proposed model and provides practical recommendations for technical directors and HR specialists. The information presented in the article will be of interest to heads of IT departments, project managers, and human resource management specialists in the technology sector.
subject: Articles
  topic: section
subject: personnel strategies
  topic: keywords
subject: engineering teams
  topic: keywords
subject: high loads
  topic: keywords
subject: talent management
  topic: keywords
subject: technical leadership
  topic: keywords
subject: team scaling
  topic: keywords
subject: Agile
  topic: keywords
subject: engineer competencies
  topic: keywords
subject: IT recruiting
  topic: keywords
subject: organizational structure
  topic: keywords
creator: Shatukha Ivan
rights: Copyright (c) 2026 Shatukha Ivan https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-01-29
publication date: 2026-01-29
starting page: 1
ending page: 10

Comparative Analysis of Leader Election Algorithms In Distributed Systems
type: item
description: Leader election is a fundamental problem in distributed computing, requiring processes to agree on a single coordinator to manage critical operations such as mutual exclusion, transaction coordination, and state machine replication. This paper presents a comprehensive comparative analysis of leader election algorithms across multiple dimensions: message complexity, time complexity, fault tolerance, and practical applicability. We examine classical ring-based algorithms (Chang-Roberts, Hirschberg-Sinclair), complete network algorithms (Bully), and modern consensus-based approaches (Paxos, Raft, ZAB). Through systematic evaluation using both theoretical analysis and empirical experimental simulation, we identify trade-offs between algorithm simplicity, efficiency, and robustness. Our results indicate that while ring-based algorithms offer optimal message complexity of O(N log N ), consensus-based algorithms such as Raft provide superior fault tolerance and practical implementation characteristics for modern distributed systems. We synthesize these findings into a decision framework for practitioners selecting leader election mechanisms based on system requirements and operational constraints.
subject: Articles
  topic: section
subject: Leader election
  topic: keywords
subject: distributed systems
  topic: keywords
subject: consensus algorithm
  topic: keywords
subject: Raft
  topic: keywords
subject: Paxos
  topic: keywords
subject: fault tolerance
  topic: keywords
subject: ring networks
  topic: keywords
subject: message complexity
  topic: keywords
creator: Yevhen Piotrovskyi
rights: Copyright (c) 2026 Yevhen Piotrovskyi https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-02-01
publication date: 2026-02-01
starting page: 11
ending page: 26

Strategies for Handling Barge-in Interruptions in Conversational AI Interfaces
type: item
description: The article addresses the problem of handling interruptions (barge-in) in voice-based ordering interfaces operating on top of large language models. The aim of the study is, based on a review of solutions for streaming speech recognition, voice activity detection, turn-taking, and end-to-end dialogue architectures, to synthesise an integrated set of barge-in handling strategies for high-load QSR scenarios. The relevance of the research is determined by the growing share of voice orders in noisy environments and their relationship to transaction accuracy, service speed, and customer satisfaction. The novelty of the article lies in treating interruption as an end-to-end intention marker that imposes coherent requirements on stack architecture, dialogue policy, utterance design, and the transactional logic of the cart. It is demonstrated that effective barge-in handling relies on prioritising user speech over system synthesis, streaming recognition with interim hypotheses, separating stable dialogue state from the instantaneous response plan inside the language model, and phase-dependent policies for interpreting identical acoustic patterns at the stages of greeting, order configuration, confirmation, and payment. It is argued that short, interruptible system utterances, explicit invitations to interrupt, and conservative interpretation of interventions at payment steps transform interruption from a source of errors into a controlled mechanism for order correction and failure mitigation. The article is intended for researchers in dialogue systems, voice interface engineers, and product teams developing voice solutions for large-scale ordering scenarios.
subject: Articles
  topic: section
subject: interruptions
  topic: keywords
subject: barge-in
  topic: keywords
subject: voice interfaces
  topic: keywords
subject: dialogue systems
  topic: keywords
subject: large language models
  topic: keywords
subject: voice ordering
  topic: keywords
creator: Dhaval Hemant Shah
rights: Copyright (c) 2026 Dhaval Hemant Shah https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-02-01
publication date: 2026-02-01
starting page: 27
ending page: 37

An Econometric Framework for Estimating the Joint Elasticity of Advertising and Promotions on Retail Sales
type: item
description: Contemporary media fragmentation and heightened marketing accountability necessitate quantifying the contribution of each marketing dollar to sales, including the joint effects of advertising and price promotions. Standard Marketing Mix Models (MMM), estimated on observational time series, remain correlational and leave a causal gap, especially when campaigns and discounts are executed concurrently. The intended audience for this framework includes both academic researchers and industry practitioners, with particular emphasis on data scientists and analytics professionals working in e-commerce, retail media, and digital marketing. For these readers, the model is designed to be readily implementable within contemporary data science pipelines, leveraging familiar tools such as regression-based modeling, regularization, hyperparameter search, and out-of-sample validation to support scalable, reproducible, and decision-relevant MMM applications. A log-log model is specified in which weekly log sales are regressed on channel-specific adstocked spends (Koyck formulation) and promotions, augmented with multiplicative interactions Adstock×Promo. Decay parameters are constrained by funnel theory (Video: 0.6–0.8; Display: 0.4–0.6; SP/SB/SD: 0.3–0.5) and selected via grid search using AIC/BIC and out-of-sample RMSE under a 70/30 chronological split. Controls capture seasonality and trading peaks. Diagnostics include VIF (<10), White’s test, and DW/ACF with AR(1) errors when indicated; robustness is assessed via Ridge/Lasso. Elasticities are computed at sample means. The empirical setting is a retailer case study spanning five channels (Video, Display, Sponsored Products, Sponsored Brands, Sponsored Display) plus total promotional discounts. Elasticities along the funnel for each of the owned channels are also large and meaningful: Promo 0.28-0.36, SP 0.22-0.28, SB 0.20-0.26, Display 0.15-0.20, Video 0.10-0.14, SD 0.08-0.12. All interaction point estimates are meaningful showin…
subject: Articles
  topic: section
subject: marketing mix modeling
  topic: keywords
subject: advertising elasticity
  topic: keywords
subject: promotion elasticity
  topic: keywords
subject: interaction effects
  topic: keywords
subject: econometric modeling
  topic: keywords
subject: adstock
  topic: keywords
subject: causal analysis
  topic: keywords
subject: budget optimization
  topic: keywords
creator: Shruti Dash
rights: Copyright (c) 2026 Shruti Dash https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-02-01
publication date: 2026-02-01
starting page: 38
ending page: 49

Reducing “Unknown Certificate” Risk at Scale: A Comparative Evaluation of TLS Certificate Discovery Architectures
type: item
description: TLS/SSL certificates are a foundational trust primitive in modern digital infrastructures, yet certificate-related outages persist despite the widespread adoption of monitoring tools and enterprise certificate lifecycle management platforms. This paper addresses this puzzle by (1) classifying and analyzing existing certificate monitoring and management approaches to identify their systematic architectural limitations; (2) designing an alternative automated monitoring method that targets these gaps by rethinking certificate discovery; and (3) evaluating its effectiveness relative to conventional approaches using controlled cyber-threat modeling in a virtualized environment. We develop a human-error-aware simulation that applies identical, time-indexed infrastructure change events to multiple monitoring architectures across legacy and cloud scenarios, enabling direct comparison of inventory completeness, omission risk, and discovery latency. The results show that configuration-bound monitoring approaches degrade with infrastructure scale and dynamism, whereas an auto-discovery model grounded in L7 load balancer and front-end state maintains substantially higher visibility and lower omission rates, particularly in cloud environments. These findings suggest that reducing “unknown certificate” risk requires architectural innovation in discovery mechanisms rather than incremental improvements to configuration discipline, with important implications for organizational risk management and national cybersecurity resilience.
subject: Articles
  topic: section
subject: certificate lifecycle management
  topic: keywords
subject: auto-discovery
  topic: keywords
subject: cloud security
  topic: keywords
subject: infrastructure monitoring
  topic: keywords
subject: cybersecurity risk
  topic: keywords
creator: Vasilii Turuntaev
rights: Copyright (c) 2026 Vasilii Turuntaev https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-02-07
publication date: 2026-02-07
starting page: 50
ending page: 64

Implementation of Zero-Trust Architecture in Mobile Applications of the Financial Sector
type: item
description: This article examines the rethinking of zero-trust architecture for mobile applications in the financial sector as a crystallization of the contemporary model of cyber-resilience amid explosive growth in mobile banking and the rise of specialized smartphone attacks. It is demonstrated that traditional perimeter-based security models do not provide the required level of protection in the context of a high density of vulnerabilities in client applications and the exponential increase in malicious activity, which predetermines the relevance of a transition to an approach in which every request and every action is treated as potentially untrusted. The purpose of the study is to conceptualize and specify the principles of zero-trust architecture at the mobile client level. The scientific novelty lies in shifting the center of gravity from an abstract trusted perimeter to a set of explicitly defined decision points across the interface, domain logic, and infrastructural library layers, as well as in introducing an application trust map, standardized interface components as carriers of security policy, and a unified software kit for authentication, session management, and authorization checks. The proposed roadmap, from constructing a trust map to the phased implementation of multi-step, risk-oriented authentication, enables transforming the zero-trust architecture from a one-time initiative into a continuous process embedded in the life cycle of mobile financial product development. The article is intended for researchers, architects, and practitioners engaged in the design and evolution of mobile solutions in the banking and fintech sectors.
subject: Articles
  topic: section
subject: zero-trust architecture
  topic: keywords
subject: mobile banking
  topic: keywords
subject: fintech
  topic: keywords
subject: mobile applications
  topic: keywords
subject: cyber-resilience
  topic: keywords
creator: Pankiv Oleg
rights: Copyright (c) 2026 Pankiv Oleg https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-02-09
publication date: 2026-02-09
starting page: 65
ending page: 74

Evolution of Cloud LMS Architecture: From Local Spreadsheet Workflows to a Distributed White-Label System
type: item
description: The article examines the architectural evolution of a commercial cloud-based learning management system that originated as a set of Google Sheets used to coordinate operations in a Ukrainian IT school and later evolved into the white-label platform DrivEd, adopted by networks of schools in the USA, Australia, and Europe. The study focuses on the transition from fragile spreadsheet-centred workflows to a dedicated LMS, and then to a multi-tenant, cloud-native solution that supports branded deployments for hundreds of tenant schools without code forks. The objective is to reconstruct the main architectural decisions that enabled this trajectory and correlate them with current research on cloud LMS and multi-tenant SaaS. The analysis relies on published work on LMS design, SaaS multi-tenancy, and serverless platforms, and employs an analytical case-study method. The conclusions highlight architectural patterns that are reproducible in other education-technology products facing international scaling demands.
subject: Articles
  topic: section
subject: cloud learning management system
  topic: keywords
subject: SaaS
  topic: keywords
subject: multi-tenant architecture
  topic: keywords
subject: white-label platform
  topic: keywords
subject: Google Sheets migration
  topic: keywords
subject: EdTech scalability
  topic: keywords
subject: microservices
  topic: keywords
subject: asynchronous data processing
  topic: keywords
subject: international deployment
  topic: keywords
creator: Burmistrov Aleksandr
rights: Copyright (c) 2026 Burmistrov Aleksandr https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-02-20
publication date: 2026-02-20
starting page: 75
ending page: 86

Conceptual Approaches to Organizing Feature Stores in High-Load Machine Learning Systems
type: item
description: The paper examines conceptual approaches to organizing feature stores in high-load machine learning systems. The growth of production ML has exposed persistent problems, including duplicated feature logic, training–serving skew, and brittle pipelines, particularly under low-latency inference and heavy batch training workloads. This paper proposes an original analytical framework for organizing feature stores under high-load conditions. The framework links internal architectural decisions (registry design, transformation semantics, offline/online materialization, synchronization, geo-distribution, and maintenance control) to operational objectives (latency, point-in-time correctness, reproducibility, governance, and cost discipline), enabling structured design selection rather than a descriptive survey of existing systems. The study characterizes functional blocks of a feature store (feature registry, transformation layer, offline and online materializations, and serving interfaces), then analyzes consistency guarantees, point-in-time correctness, refresh strategies, and geo-distribution patterns. Methods rely on a comparative analysis of recent systems and surveys, with a focus on scalability bottlenecks in joins, cache design, and maintenance scheduling. The conclusion formulates design recommendations for selecting storage layouts, synchronization policies, and operational controls across the ML lifecycle. The paper targets engineers and researchers working on production MLOps and data infrastructure. Implications for feature reuse and compliance are discussed.
subject: Articles
  topic: section
subject: feature store
  topic: keywords
subject: MLOps
  topic: keywords
subject: high-load systems
  topic: keywords
subject: online inference
  topic: keywords
subject: offline training
  topic: keywords
subject: point-in-time correctness
  topic: keywords
subject: feature freshness
  topic: keywords
subject: data consistency
  topic: keywords
subject: geo-distribution
  topic: keywords
subject: maintenance scheduling
  topic: keywords
creator: Artem Korkhov
rights: Copyright (c) 2026 Artem Korkhov https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-02-20
publication date: 2026-02-20
starting page: 87
ending page: 96

Vehicle Feature VQA: Visual Question Answering for Vehicle Feature
type: item
description: Visual Question Answering (VQA) can automatically produce the predict answers for questions and real-world images. In this paper, we propose the VQA dataset for Vehicle Feature to know the knowledge of Vehicle. We develop the VQA model using RestNet50 in Convolutional Neural Networks (CNN) for feature extraction of images and Long Short-Term Memory (LSTM) for question feature extraction and answer generation. The experimental result describes the training loss, evaluation loss, Blue Score, and VQA accuracy for epochs 20 and epochs 30. In epochs 20, after VQA model generated the training loss 1.1949 , evaluation loss 1.7953, Blue Score 0.6180, and VQA accuracy 0.0493, this model predicted the one correct answer for question and image. In epochs 30, the VQA model predicted the five correct answers in fifteen test data for vehicle feature questions and image according to generate the training loss 0.8780, evaluation loss 1.6634, Blue Score 0.6775 and VQA accuracy 0.0627.
subject: Articles
  topic: section
subject: Visual Question Answering
  topic: keywords
subject: Vehicle Feature
  topic: keywords
subject: RestNet50
  topic: keywords
subject: Convolutional Neural Networks
  topic: keywords
subject: feature extraction
  topic: keywords
subject: Long Short-Term Memory
  topic: keywords
subject: Blue Score
  topic: keywords
creator: Pa Pa Tun
creator: Khin Mar Soe
rights: Copyright (c) 2026 Pa Pa Tun, Khin Mar Soe https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-02-21
publication date: 2026-02-21
starting page: 97
ending page: 105

Powering the Future of AI – Leveraging Mukkudam SHP's Clean Energy for Sustainable Computational Intelligence
type: item
description: The present work elucidates the engineering advantages of run-of-river generation schemes, which are characterized by predictability of the generation regime and lower dependence on large reservoirs, and analyzes architectural solutions that ensure co-location of computing capacity with hydrogeneration facilities. A particular emphasis is placed on ensuring base-load stability, which is fundamentally important for continuous computing processes, and on application of the Circular Energy Hub concept, oriented toward beneficial utilization of secondary heat from compute nodes within coupled local energy-consumption circuits. The objective of the study is to substantiate the effectiveness of small hydropower plants as a dedicated energy-supply source for the Green AI paradigm. To achieve the stated objective, methods of system analysis and synthesis of relevant international literature are employed; publications related to sustainable information-technology infrastructure and hydropower are examined. In the concluding part, the potential of the Mukkudam project is defined as a reproducible model capable of supporting implementation of ESG-oriented goals at a global scale, and the practical significance of the results for the domains of artificial intelligence, energy, and environmental management is documented. The article will be of interest to engineers in artificial intelligence and data center infrastructure, specialists in energy and renewable energy sources (especially small/run-of-river hydropower), ESG/sustainability and climate-policy managers, as well as stakeholders in regional development seeking practical models for implementation of green AI through co-location of edge computing capacity and reuse of waste heat within a closed-loop cycle.
subject: Articles
  topic: section
subject: artificial intelligence
  topic: keywords
subject: small hydropower
  topic: keywords
subject: Mukkudam SHP
  topic: keywords
subject: sustainable development
  topic: keywords
subject: Edge Data Centers
  topic: keywords
subject: base load
  topic: keywords
subject: decarbonization
  topic: keywords
subject: green energy
  topic: keywords
subject: immersion cooling
  topic: keywords
subject: Kerala
  topic: keywords
creator: Unni Siva Sankar
rights: Copyright (c) 2026 Unni Siva Sankar https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-03-14
publication date: 2026-03-14
starting page: 106
ending page: 118

AI-Driven Predictive Maintenance in Retail IT Systems Using DevOps: A Review
type: item
description: Retail IT infrastructure needs to be extremely reliable and highly available to provide seamless operations, best customer experience, and effective resource utilization. Predictive maintenance driven by Artificial Intelligence (AI) and natively integrated with DevOps practices has become a game-changing solution in this regard. This paper provides an extensive review of 40 recent research articles on AI-DevOps convergence for predictive maintenance in retail IT infrastructures. A systematic literature review methodology was adopted, involving structured search, selection, and thematic analysis of peer-reviewed studies from 2019 to 2026. Some of the prime subjects discussed include common DevOps practices in retail IT, using AI for predictive analytics assistance, and merging machine learning models with failure prediction. Techniques for anomaly detection and pattern recognition in favour of early detection of possible problems are given special attention. Also, merging AI insights with DevOps pipelines is discussed in terms of automated feedback loops, CI/CD optimization, and real-time monitoring of system health. The review also addresses open issues like data quality, model drift, and integration complexity, and explores growing trends in self-healing systems and AIOps. This research aims to offer researchers and practitioners an extensive review of state-of-the-art techniques and their probable contribution to increased operational resilience and cost-effectiveness in retail IT systems through smart, automated maintenance practices.
subject: Articles
  topic: section
subject: Predictive Maintenance
  topic: keywords
subject: Retail IT Systems
  topic: keywords
subject: AI
  topic: keywords
subject: DevOps
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: AIOps
  topic: keywords
subject: Machine Learning Models
  topic: keywords
subject: Monitoring
  topic: keywords
creator: Suresh Gangula
creator: Satyanarayana Gudimetla
rights: Copyright (c) 2026 Suresh Gangula, Satyanarayana Gudimetla https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-03-14
publication date: 2026-03-14
starting page: 119
ending page: 130

Applying Machine Learning for Network Security Monitoring in Kubernetes Environments
type: item
description: This article presents a systematic analysis of methodological approaches to applying machine learning for network security monitoring in Kubernetes environments, where the dynamic nature of containerized workloads, microservice architectures, and distributed observability significantly complicate attack detection and interpretation. The study is conducted as a review-and-analytical synthesis of peer-reviewed publications, summarizing architectural models, telemetry types, algorithmic approaches, and operational constraints without quantitative aggregation of results due to methodological heterogeneity of the sources. Particular attention is paid to the impact of Kubernetes network infrastructure on the properties of observed data, the role of non-identical and imbalanced distributions in distributed environments, and the limitations of classical centralized training schemes. The analysis shows that the prevailing practice of using ML-based intrusion detection systems is oriented toward isolated event detection and does not account for systemic telemetry distortions introduced by container network interfaces and the control plane. It is established that the greatest practical robustness is demonstrated by architectures based on hybrid data sources and federated learning algorithms, as well as two-tier schemes that separate anomaly detection from semantic interpretation. It is shown that the effectiveness of ML monitoring in Kubernetes is determined not so much by model complexity as by the degree of architectural integration into a managed security control loop encompassing observation, analysis, interpretation, and controlled response. The article will be useful for cybersecurity researchers, cloud platform architects, container security engineers, and specialists in operating distributed systems.
subject: Articles
  topic: section
subject: Kubernetes
  topic: keywords
subject: network security
  topic: keywords
subject: machine learning
  topic: keywords
subject: intrusion detection
  topic: keywords
subject: federated learning
  topic: keywords
subject: container networks
  topic: keywords
subject: managed security control loop
  topic: keywords
creator: Matvii Horskyi
rights: Copyright (c) 2026 Matvii Horskyi https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-03-15
publication date: 2026-03-15
starting page: 131
ending page: 151

Development of Quality Assurance Standards for the Industrialization of Software Applications Prototyped Using Intelligent Assistants
type: item
description: The article is dedicated to the analysis of quality assurance transformation in the context of industrializing software applications prototyped using intelligent assistants. The relevance of the study is determined by the rapid diffusion of large language models into software development workflows and the growing mismatch between accelerated prototyping and delayed quality degradation in production environments. Scientific novelty lies in the analytical reinterpretation of quality assurance as a layered, distributed regulatory system rather than a terminal verification phase. The work describes structural shifts in assurance logic, including constraint-driven generation, embedded testing, knowledge grounding, and orchestration-level governance. Special attention is paid to the temporal displacement of quality signals and the limitations of metric-centered evaluation in AI-assisted development. The work sets itself the goal of conceptualizing quality assurance standards suitable for the industrialization of AI-prototyped software. To achieve this goal, analytical synthesis and comparative analysis are used. A corpus of recent studies on generative AI, program repair, knowledge-enhanced systems, autonomous agents, and software quality tools is examined. The conclusions demonstrate that no single assurance paradigm stabilizes quality under AI augmentation, and layered standards emerge as a structural necessity. The article will be useful for researchers, software architects, quality engineers, and technology managers.
subject: Articles
  topic: section
subject: quality assurance
  topic: keywords
subject: generative AI
  topic: keywords
subject: software industrialization
  topic: keywords
subject: intelligent assistants
  topic: keywords
subject: test-driven generation
  topic: keywords
subject: knowledge grounding
  topic: keywords
subject: program repair
  topic: keywords
subject: autonomous agents
  topic: keywords
subject: software governance
  topic: keywords
creator: Andrii Shaliev
rights: Copyright (c) 2026 Andrii Shaliev https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-03-24
publication date: 2026-03-24
starting page: 152
ending page: 162

Distributed Caching and Real-Time Sync in Active-Active Environments for Critical Cloud Native Systems
type: item
description: Active–active cloud-native architectures are widely adopted in mission-critical domains such as railway operations, healthcare platforms, and large-scale enterprise systems to achieve near-zero downtime, high availability, and fault tolerance. Distributed caching and real-time synchronization are foundational enablers of these architectures, ensuring low-latency data access and consistency across geographically distributed deployments. However, the combination of active–active execution, event-driven microservices, and distributed state introduces substantial challenges related to consistency, observability, and operational complexity. This paper presents a comprehensive architectural and empirical study of distributed caching and real-time synchronization in active–active cloud-native systems. We propose a reference architecture integrating event-driven communication, distributed in-memory data grids, and AI-driven observability. Experimental results demonstrate significant improvements in latency, availability, and mean time to recovery, providing practical guidance for designing resilient mission-critical platforms.
subject: Articles
  topic: section
subject: Active–Active Architecture
  topic: keywords
subject: Distributed Caching
  topic: keywords
subject: Real-Time Synchronization
  topic: keywords
subject: Cloud-Native Systems
  topic: keywords
subject: Microservices
  topic: keywords
subject: Event-Driven Architecture
  topic: keywords
subject: AI Observability
  topic: keywords
creator: Rahul Ganta
creator: Rajkumar shevagani
creator: Suchitra Masiragani
rights: Copyright (c) 2026 Rahul Ganta, Rajkumar shevagani, Suchitra Masiragani https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-04-06
publication date: 2026-04-06
starting page: 163
ending page: 181

Methodological Aspects of Integrating Security Processes into Continuous Development Pipelines (CI/CD Security)
type: item
description: The article presents a theoretical and methodological analysis of integrating security processes into continuous integration and continuous delivery pipelines. The study consolidates DevSecOps models, software supply chain protection approaches, and multi-layer container security into a unified CI/CD Security framework. The pipeline is structured by stages of source code management, build, artifact storage, test automation, and deployment, with corresponding typical vulnerabilities and integrated security processes. This allows the pipeline to be interpreted not as a set of isolated practices but as a coherent trust chain. A multi-layer protection model is proposed, combining image control, orchestrator security, host operating system hardening, runtime protection, and artifact provenance. Within this model, the container platform and traceability mechanisms are viewed as complementary components of a single methodological foundation. The key methodological challenges of integrating security into CI/CD are identified, and it is shown that ignoring them leads to underestimated risk assessments and overstated expectations regarding adopted practices. Promising directions for advancing CI/CD Security are substantiated, including the use of intelligent telemetry analysis, self-healing mechanisms, cryptographically reinforced trust boundaries, and formalized maturity metrics. The article may be useful for architects, engineers, and researchers involved in designing secure continuous delivery pipelines in highly automated and regulated environments.
subject: Articles
  topic: section
subject: software supply chain security
  topic: keywords
subject: container security
  topic: keywords
subject: provenance metadata
  topic: keywords
subject: security automation
  topic: keywords
subject: process maturity
  topic: keywords
creator: Praveen Ravula
rights: Copyright (c) 2026 Praveen Ravula https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-03-14
publication date: 2026-03-14
starting page: 190
ending page: 199