Accurate forecasting of mortality causes is vital for healthcare planning, epidemiological research, and public health interventions. In this study, we employ a combination of regression models and moving averages to predict mortality causes. By analyzing historical data and leveraging statistical models, we aim to provide reliable insights into the likely causes of mortality over time. Our approach contributes to informed decision-making in healthcare, enabling proactive measures to address health challenges effectively.

The rapid expansion of digital retail ecosystems has significantly increased the complexity of enterprise data management. Modern retailers operate across multiple sales channels, including physical stores, e-commerce platforms, mobile applications, supplier portals, customer relationship management systems, inventory management platforms, and cloud-based analytics services. These heterogeneous environments generate continuous streams of transactional and operational data that must remain synchronized during infrastructure modernization and system migration initiatives. Traditional migration strategies frequently require scheduled downtime, resulting in interrupted customer experiences, financial losses, inventory inconsistencies, and operational risks. Consequently, zero-downtime enterprise data migration has emerged as a strategic objective for organizations seeking uninterrupted business continuity while upgrading legacy infrastructures.

This study investigates the design and implementation of dual-path streaming and batch architectures for achieving zero-downtime enterprise data migration within retail environments. The proposed framework integrates continuous real-time streaming mechanisms with scheduled batch synchronization processes to ensure data consistency, transactional integrity, operational resilience, and scalability throughout migration lifecycles. Streaming pipelines support immediate propagation of mission-critical transactions, whereas batch pipelines provide historical reconciliation, validation, recovery, and quality assurance. Together, these complementary approaches minimize migration risk while maintaining uninterrupted retail operations.

The research adopts a conceptual research methodology supported by critical synthesis of the provided literature and theoretical analysis. Existing architectural practices, migration strategies, and infrastructure modernization principles are examined to develop a comprehensive enterprise migration framework suitable for complex retail ecosystems. The findings demonstrate that combining streaming technologies with intelligent batch processing substantially improves migration reliability, minimizes service interruptions, enhances fault tolerance, and enables scalable modernization of enterprise information systems. The study contributes a practical architectural model capable of supporting future digital transformation initiatives in highly dynamic retail environments.

Data de-duplication, a vital component of data management and storage optimization, has raised concerns about data security and privacy. In response to these concerns, we present an innovative approach, "Efficient Content Discovery and Preservation for De-duplication (ECDPD)," designed to secure data during the de-duplication process. ECDPD introduces efficient content discovery mechanisms and robust preservation techniques, ensuring that sensitive data remains confidential while realizing the benefits of de-duplication. Our study addresses the pressing need for data security in an era of increasing data proliferation.