Category Archives: Uncategorized

Optimizing Healthcare Data Warehouses for Future Scalability: Big Data and Cloud Strategies
Authors:Srinivasa Chakravarthy Seethala

Abstract:Healthcare organizations generate vast amounts of data, driven by regulatory compliance, patient care needs, and advances in medical technology. Legacy data warehouses, while central to healthcare data management, often struggle to accommodate escalating data volumes, new data types, and real-time processing demands. This paper presents strategic insights into leveraging Big Data and cloud computing to modernize healthcare data warehouses for future scalability. We examine technical approaches, review cloud and Big Data integration techniques, and propose a roadmap for healthcare data scalability, addressing concerns of security, compliance, and data interoperability.

DOI: 10.61137/ijsret.vol.3.issue6.201

Published by: Kajal Tripathi

Authors: Sudhir Vishnubhatla

Abstract: Legacy Information Management Systems (IMS) remain central to operations in banking, healthcare, public sector, and media, yet their monolithic design, proprietary data formats, and brittle integrations have become barriers to agility and intelligent analytics. Earlier literature identified the persistent costs and risks of legacy IMS and proposed incremental modernization through wrapping, service extraction, and reengineering, while empirical studies in the early 2010s established the feasibility and business value of infrastructure-as-a-service (IaaS) rehosting. As of late 2017, however, the migration decision space has widened: enterprises are not merely choosing whether to move to the cloud, but how to distribute workloads across multiple hyperscalers, primarily Amazon Web Services (AWS) and Google Cloud Platform (GCP). This article synthesizes more than a decade of academic and industrial work on legacy migration and proposes a Dual-Cloud Readiness Playbook tailored to IMS modernization. The playbook comprises a readiness scorecard, a five-phase migration lifecycle, and a layered hybrid architecture that balances compliance, cost, and capability while reducing vendor lock-in. The result is a pragmatic framework that aligns with the realities of petabyte-scale content archives, metadata-heavy workflows, and emerging regulatory constraints, offering a credible path from monolithic legacy platforms to modern, cloud-native information management

DOI: https://zenodo.org/records/17298069

Published by: vikaspatanker

Optimizing Material Management through Advanced System Integration, Control Bus, and Scalable Architecture
Authors:RamaKrishna Manchana

Abstract:This paper presents an advanced approach to material management by leveraging modern system integration techniques and scalable microservices architecture. The proposed solution addresses the limitations of traditional monolithic systems by introducing microservices, control bus mechanisms, and event-driven designs that enhance operational efficiency and scalability. By utilizing advanced integration patterns, including synchronous and asynchronous services, the system improves real-time data processing and decision-making capabilities. This document outlines the technical architecture, key components, integration designs, and implementation strategies that underpin a robust and adaptable material management system, demonstrating significant improvements in scalability, performance, and responsiveness.

DOI: 10.61137/ijsret.vol.3.issue6.200

Published by: Kajal Tripathi

Authors: Prabal Kumar Joshi

Abstract: IoT (internet of things) is defined as a worldwide framework for information world, which provides advanced services by connecting physical and virtual things through information technology and adoptable communication available in the evolution process. IoT is just applied to overcome the concerns related to safety. Sharing information among different devices can affect the private information of users. Therefore, a proper approach mechanism is required to prevent the risk of malicious and vulnerable failures. Multi-services IoT is vulnerable to many types of malicious attacks. Trust management provides a potential solution for safety issues of distributed networks. In this article, an algorithm is designed in order to calculate trust, which does not calculate the amount of trust for all neighbors and just calculates it for suspicious neighbors located in the list of suspicious nodes. Subsequently, energy consumption in comparison to the approach of basis article has so much decrease.

DOI: http://doi.org/10.5281/zenodo.17034568

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

Increasing Students’ Interest in Mathematics: A Teaching Quality That Connects Mathematics to Real-Life Problems
Authors:-Abhijit Sadashiv Patil

Abstract-Students’ enthusiasm in mathematics is largely influenced by their math professors’ capacity to relate the subject to real-world issues. In order to increase students’ interest in mathematics, the current study aims to determine what aspects they would want to see their math teachers enhance. Using a random sample technique, the study chose ten (10) high schools and 1,263 students from different subject areas to answer a structured questionnaire about factors that influence students’ interest in mathematics. The study examined the impact of teachers’ capacity to relate mathematics to real-world issues on students’ interest in the subject using principal component analysis and multiple linear regression analysis. According to the study, 57.4% of students’ interest in mathematics is predicted by teachers’ capacity to relate mathematics to real-world problems, which may be divided into two main components (p<0.001). The components utilized to connect mathematics to real-world problems were evaluated and their relative importance index was calculated. According to the study, when math teachers set aside quality time for pupils to do class exercises, they would be more engaged in the subject. The study also discovered that students' interest in mathematics is largely influenced by the teacher's capacity to connect mathematics to other topic areas. According to the study's findings, teachers' capacity to relate mathematics to real-world issues is crucial to the growth of students' interest in the subject.

DOI: 10.61137/ijsret.vol.3.issue1.144

Published by: Kajal Tripathi

AI-Driven Data Warehouse Modernization in the Healthcare Sector: A Blueprint for Efficiency Modernizing Legacy Data Warehouses with AI-Enhanced Workflows/strong>
Authors:-Srinivasa Chakravarthy Seethala

Abstract-The healthcare industry is at a pivotal moment in terms of data management. Legacy data warehouses, which once served the sector’s needs, are now proving inefficient in an era of rapid technological advancements. This article proposes a framework for modernizing these legacy systems with Artificial Intelligence (AI) technologies, particularly AI-enhanced Extract, Transform, Load (ETL) workflows. These technologies have the potential to significantly improve data quality, operational efficiency, and scalability, especially in key areas such as Electronic Health Records (EHRs), Medical Imaging, Hospital Management, and Medical Research. Additionally, AI enables predictive analytics, offering healthcare organizations the ability to anticipate patient needs and optimize resource allocation. This paper explores the challenges healthcare organizations face, the benefits of AI-driven solutions, and best practices for implementation.

DOI: 10.61137/ijsret.vol.2.issue6.135
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Published by: Kajal Tripathi