Authors: Hema Latha Boddupally

Abstract: Adaptive User Interfaces (AUIs) have become increasingly essential as modern software applications are expected to deliver seamless, intuitive, and personalized experiences across a broad spectrum of devices, screen dimensions, accessibility needs, and interaction contexts. With users frequently transitioning between desktops, tablets, mobile phones, and other emerging platforms, traditional fixed or solely responsive design approaches often fall short in addressing deeper adaptive requirements such as behavior-driven adjustments, contextual awareness, and user-specific personalization. This paper presents a hybrid model for AUI development that integrates ASP.NET MVC’s robust server-side rendering pipeline with React flexible, component-based client-side architecture, enabling interfaces that not only adapt visually but also evolve functionally based on user roles, preferences, device capabilities, and real-time interaction patterns. By leveraging server-side logic for initial content shaping and client-side React components for dynamic rendering and state-driven updates, the proposed model supports fine-grained adaptation, modular UI evolution, and scalable interface personalization. Building on established concepts in responsive design, adaptive graphical interfaces, and context-aware interaction models, the study outlines key architectural strategies, design principles, and implementation techniques that facilitate the development of maintainable, high-performance AUI systems. Furthermore, the paper examines practical challenges such as context modeling, synchronization between server and client layers, managing diverse user scenarios, and optimizing rendering performance, ultimately demonstrating how the synergy of MVC and React provides a powerful foundation for creating intelligent, user-centered adaptive interfaces capable of meeting the demands of modern digital ecosystems.

DOI: http://doi.org/10.5281/zenodo.18042587

Published by: vikaspatanker

Authors: Srujana Parepalli

Abstract: Data quality has emerged as a critical challenge in modern enterprise information systems, as rapid growth in data volume, velocity, and heterogeneity amplifies issues such as inconsistency, incompleteness, redundancy, and semantic ambiguity across distributed platforms. Traditional rule-based data validation techniques, including integrity constraints and handcrafted business rules, offer strong interpretability and auditability but often fail to scale or adapt in dynamic environments where schemas, data sources, and usage patterns continuously evolve. In contrast, purely statistical and machine-learning driven approaches excel at identifying latent patterns and anomalies in large datasets but frequently suffer from limited explainability, making governance, regulatory compliance, and root-cause analysis difficult. This article presents an integrated framework for Intelligent Data Quality Engineering that synergistically combines constraint-based validation, probabilistic modeling, and AI-driven anomaly detection to overcome these limitations. By grounding adaptive learning models in well-established research on conditional functional dependencies, probabilistic databases, and entity resolution, the framework enables predictive detection of quality issues and supports self-healing data pipelines capable of learning from historical errors and feedback. This hybrid approach effectively bridges deterministic data rules with adaptive intelligence, delivering scalable, transparent, and governance-aligned data quality solutions suitable for enterprise-grade analytics and decision systems.

DOI: http://doi.org/10.5281/zenodo.17987694

Published by: vikaspatanker

Authors: Kranthi Kumar Routhu

Abstract: The rapid evolution of information technology over the past two decades has fundamentally transformed the way organizations manage their human capital. Human Resource (HR) systems, once confined to on-premise infrastructures, have steadily progressed toward cloud-based Human Capital Management (HCM) ecosystems that integrate data, analytics, and user experience into a unified digital platform. This transformation reflects not merely a technological shift, but a strategic reorientation of HR’s role from administrative recordkeeping to value-driven talent management. Traditional on-premise HR systems such as Oracle E-Business Suite and PeopleSoft provided strong control, customization, and data security but required heavy maintenance, complex upgrades, and significant capital expenditure. In contrast, cloud-based HCM platforms introduced a service-oriented model that offers agility, scalability, and continuous innovation through subscription-based delivery. Oracle’s HCM Cloud represents a culmination of this digital evolution, combining the reliability and maturity of legacy systems with the adaptability of cloud-native architecture. The purpose of this study is to examine how this migration reshaped enterprise HR strategy and infrastructure. It explores the transformation journey from on-premise deployment to Oracle Cloud HCM, focusing on the organizational, technical, and regulatory challenges encountered during migration. Furthermore, it evaluates the strategic opportunities created through the adoption of Oracle’s cloud-driven HR ecosystem, particularly in enhancing workforce analytics, compliance automation, and employee engagement. Through this lens, the study demonstrates how Oracle’s Cloud HCM framework serves as both a technological and organizational enabler of modern HR excellence.

DOI: https://doi.org/10.5281/zenodo.17669776

Published by: vikaspatanker

Authors: Harish Govinda Gowda

Abstract: Containers have become the backbone of modern enterprise IT, providing portability, agility, and consistency across environments. However, scaling containers across hybrid and multi-cloud infrastructures requires more than orchestration—it demands governance, security, and resilience. This article explores how Kubernetes, Helm, and OpenShift can be harmonized to achieve container intelligence at scale. Kubernetes provides orchestration, Helm simplifies application deployment and lifecycle management, and OpenShift delivers governance, compliance, and enterprise-grade security. By layering these tools together, organizations can create resilient, scalable ecosystems that balance agility with trust. The discussion highlights key challenges in scaling containers, the role of each tool, and best practices for enterprise adoption, emphasizing that true resilience comes from harmonizing orchestration, management, and governance into one cohesive framework.

DOI: http://doi.org/10.5281/zenodo.16992539

Published by: vikaspatanker