Daily Archives: March 19, 2026

Authors: Dr. Matteo Rinaldi, Hiroshi Nakamura, Elena Petrova, Daniel Sørensen, Ananya Kulkarni

Abstract: This paper explores how machine learning–driven query optimization can elevate the performance, scalability, and operational resilience of SQL and NoSQL database systems deployed in high-volume financial and healthcare environments. Conventional rule-based and cost-based optimizers frequently encounter limitations when confronted with volatile workloads, uneven data distributions, and rapidly shifting access behaviors that define contemporary transaction processing and clinical data infrastructures. The central inquiry of this study examines whether adaptive, data-aware optimization models—trained on historical execution traces, telemetry signals, and workload metadata—can deliver superior efficiency and stability in such dynamic contexts. The research employs a blended methodological approach that integrates architectural framework design, algorithmic prototyping, and comparative benchmarking across representative relational and non-relational database platforms operating under large-scale transactional and analytical loads. Empirical evaluation indicates that learning-enabled optimizers meaningfully lower query response times, improve compute and memory utilization, and enhance predictability during peak data surges when compared to traditional strategies. Core contributions include the development of predictive cost estimation models, context-aware index adaptation mechanisms, and real-time execution plan adjustments powered by supervised and reinforcement learning paradigms. Collectively, the study advances the theoretical foundations of intelligent data management by embedding adaptive learning into optimization workflows, while offering practical guidance for engineering robust, high-throughput database infrastructures capable of sustaining accuracy, compliance, and responsiveness in mission-critical financial and healthcare systems.

DOI: https://doi.org/10.5281/zenodo.19104981

Published by: vikaspatanker

Authors: Sebastian Moreau, Yuki Matsumoto, Adrian Kovalenko, Matteo Ricci, Ananya Kulkarni

Abstract: Digital transformation in human capital management has created complex, distributed ecosystems in which employee data originates from connected devices, cloud platforms, transactional systems, and external intelligence services. Fragmented architectures limit the ability to sense patterns, contextualize signals, and coordinate timely action across SAP SuccessFactors and heterogeneous cloud HCM landscapes. This study introduces a digital nervous system architecture that integrates Internet of Things telemetry, scalable big data infrastructures, and artificial intelligence driven cognition into a unified sensing and response framework. The proposed model organizes system design into sensing layers for real time signal acquisition, transmission layers for streaming and synchronization, cognitive layers for predictive and prescriptive analytics, and response layers for coordinated orchestration across talent, payroll, performance, and compliance domains. A formal Enterprise Signal Latency Index is developed to quantify responsiveness across distributed platforms, alongside a Neural Stability Metric that measures adaptive coherence within the integrated HCM ecosystem. Through architectural modeling and scenario based evaluation, the research demonstrates reductions in signal propagation delay, improved anomaly detection accuracy, enhanced decision synchronization across platforms, and strengthened systemic resilience. The findings establish a scalable blueprint for constructing intelligent, continuously learning digital infrastructures that unify IoT, big data, and artificial intelligence within multi cloud human capital environments.

DOI: https://doi.org/10.5281/zenodo.19104930

Published by: vikaspatanker

Authors: Camila Duarte, Romain Delacroix, Arjun Mehta, Tobias Lindgren, Ananya Kulkarni

Abstract: Workforce systems are increasingly expected to operate as adaptive intelligence infrastructures rather than static repositories of employee transactions. Conventional ERP based human capital platforms were engineered to ensure data accuracy, compliance integrity, and standardized administrative workflows; however, their batch oriented processing logic and retrospective analytics limit organizational capacity to detect emerging workforce risks and coordinate timely interventions. This study proposes an event-driven, machine learning enabled ERP architecture that transforms human capital systems into continuously responsive ecosystems capable of real time sensing, predictive modeling, and automated orchestration. The framework integrates streaming event pipelines, dynamic feature engineering, embedded predictive and prescriptive models, and governance aligned control mechanisms within a unified enterprise environment. A novel Continuous Workforce Optimization Index is introduced to quantify adaptive stability across engagement momentum, performance variability, capacity distribution, and compliance consistency. Through architectural modeling and scenario based simulation, the research demonstrates measurable reductions in decision latency, improvements in prediction accuracy, enhanced intervention precision, and strengthened systemic resilience when compared with traditional batch driven ERP configurations. The findings establish a scalable blueprint for designing next generation human capital architectures that enable sustained, continuous workforce optimization in complex and rapidly evolving enterprise contexts.

DOI: https://doi.org/10.5281/zenodo.19104880

Published by: vikaspatanker