Authors: Leela Sundari

Abstract: Optimizing performance in QlikView dashboards is critical for ensuring fast, responsive, and actionable business intelligence. As organizations increasingly rely on interactive and data-driven decision-making, performance bottlenecks due to large datasets, complex calculations, and suboptimal dashboard design can hinder operational efficiency and user adoption. This review article examines essential strategies and techniques for enhancing QlikView performance, focusing on data modeling, dashboard design, scripting optimization, server tuning, and advanced analytical integration. Key areas include implementing star and snowflake schemas, managing synthetic keys and circular references, leveraging QVDs and incremental loading, and optimizing expressions using set analysis and pre-aggregated measures. Additionally, server and environment considerations—such as memory allocation, load balancing, multi-threading, and monitoring—are discussed to maintain responsiveness under high concurrency. The article also highlights industry-specific applications in finance, healthcare, and retail, demonstrating practical implementation of optimization strategies in real-world scenarios. Emerging trends, including AI-assisted performance tuning, cloud and hybrid deployments, real-time analytics, and integration with advanced predictive and prescriptive analytics tools, are explored to illustrate the evolving landscape of QlikView performance management. By adopting these best practices, organizations can ensure that dashboards remain scalable, accurate, and efficient, enabling users to derive actionable insights quickly. This comprehensive review serves as a practical guide for BI developers, architects, and enterprise decision-makers seeking to maintain high-performance QlikView environments and maximize the value of their data-driven initiatives.

DOI: http://doi.org/

Published by: vikaspatanker

Authors: Azar Djamali

Abstract: Advances in AI, robotics and construction technology are making in‑situ extraterrestrial building practicable. Demonstrations such as Mars Dune Alpha (NASA/ICON), Lunar Habitat via Contour Crafting (NASA/ICON), and the Autonomous Self‑Growing Structures research (Jin et al. 2023) show technical feasibility, but economic barriers remain the principal threat to long‑term habitation. This paper proposes a concise techno‑economic framework that treats economic sustainability as a primary design parameter, integrating financial intelligence—data‑driven financial planning and decision support, typically aided by AI—with architectural and robotic development. The framework shows how coordinated design, automated fabrication and targeted financial analysis can reduce lifecycle costs and improve value creation. Drawing on a broad literature synthesis and analysis of over 20 government, commercial and student projects (estimates compiled from public sources and open repositories and used here as indicative rather than fully verified), the study offers practical guidance for architects, students and industry stakeholders to mitigate financial risk, structure resilient financing and move space architecture toward commercial viability.

DOI: http://doi.org/10.5281/zenodo.17277057

Published by: vikaspatanker

Authors: Nwonye Charles A, Ezema. E. E, Abba. M.O

Abstract: This project is primarily designed to ensure real time oil leakage detection and localization using flow rates and echo principles. Flow station A has a microcontroller as the central control unit with start button and flow meter as inputs while time domain reflectometer (TDR), Sound alarm and Pump as outputs. It also has a modem with which it communicates to other flow stations. Flow station B has the same arrangement as flow station A except that it has only sound alarm as its output. Also, the pipeline that links the two flow stations has copper cables aligned along its length so that any attempt to break the pipeline must first cut any of the copper cables which are the basis to detect exact point of leakage using the TDR. Here, fluid flow between two flow stations is considered and the mass flow rates at both ends are measured and compared to check for possible leakage. For a given length of pipeline (L), Cross sectional area (A) and fluid density (ρ) over a given time (t), there is a threshold maximum for mass flow rate difference between flow station A and flow station B. Once, the difference between the two mass flow rates at the two flow stations A and B becomes higher than the given threshold, leakage has occurred, alarm will be activated at both flow stations to alert the workers and the time domain reflectometer (TDR) will be activated at the flow station A. The TDR will send a pulse over the copper cables aligned along the length of the pipeline since for leakage to occur one of the copper cables aligned along the length of the pipeline must have been tampered with. When a pulse is sent across the copper cables, the pulse will be reflected back at a point where the cable is cut. Hence, the TDR measures the time interval between when the pulse is sent to when it was reflected back from the point of leakage as time (t). Then using Echo Equation V=2X/t, where V= Velocity of the sent pulse (3 x 108m/s), hence X=Vt/2 where X is the exact point of leakage as measured from flow station A. The developed system recorded a very low error rate of 0.22% with very high precision.

DOI: http://doi.org/10.5281/zenodo.17276591

Published by: vikaspatanker

Authors: Kavya Menon

 

Abstract: Modern enterprises increasingly rely on business intelligence (BI) platforms to transform raw data into actionable insights. Tableau, as a leading BI tool, offers sophisticated visualization, analytics, and reporting capabilities. However, the underlying operating environment significantly impacts performance, scalability, security, and cost efficiency. This review explores the strategic advantages of deploying Tableau on Linux-based systems, specifically Red Hat Enterprise Linux (RHEL) and CentOS, for enterprise-grade BI implementations. It examines the role of Linux in enhancing system stability, providing robust security frameworks, supporting modular and automated workflows, and enabling high availability and scalability. The article analyzes data integration strategies, ETL pipelines, and dashboard optimization practices tailored to Linux environments, emphasizing both operational efficiency and user experience. Case studies across healthcare, finance, and retail illustrate real-world applications, demonstrating how Linux-based Tableau deployments support secure, high-performance analytics, regulatory compliance, and business agility. Furthermore, the review addresses monitoring, maintenance, and performance tuning, highlighting best practices for sustained system reliability. Future trends, including AI integration, real-time streaming, hybrid cloud architectures, and advanced automation, are discussed to illustrate the evolving landscape of enterprise BI. By combining Tableau’s visualization capabilities with Linux’s reliability and flexibility, organizations can achieve cost-effective, scalable, and secure BI solutions. This article underscores the importance of selecting an appropriate operating environment to maximize Tableau’s potential and provides a comprehensive guide for IT professionals, analysts, and business leaders seeking to optimize their BI infrastructure.

DOI: https://doi.org/10.5281/zenodo.17275816

 

Published by: vikaspatanker

Authors: Rani Kumari

Abstract: The transition from traditional reporting methods to interactive, data-driven dashboards has transformed how organizations interpret and act upon information. This review examines the role of Tableau in the Business Intelligence (BI) lifecycle, focusing on its ability to convert raw data into actionable insights that support both strategic and operational decision-making. Tableau’s integration capabilities, including connections to diverse data sources and support for live or extracted datasets, enable organizations to streamline data preparation, cleansing, and transformation. Its visual analytics and interactive dashboards allow stakeholders to explore trends, perform what-if analyses, and monitor key performance indicators (KPIs) in real time. Advanced features, such as calculated fields, predictive modeling, and integration with AI/ML frameworks, enhance the depth and accuracy of insights, while collaborative and cloud-enabled solutions facilitate enterprise-wide adoption. Case studies from retail, healthcare, and finance illustrate Tableau’s practical impact in improving operational efficiency, forecasting, and decision support. The review also addresses challenges, including data quality management, user adoption barriers, and performance scaling, highlighting best practices to overcome these limitations. Looking forward, the integration of AI-driven analytics, real-time data streams, and embedded BI promises to expand Tableau’s influence in decision-making workflows. By adopting Tableau strategically, organizations can foster a culture of data literacy, enhance agility, and ensure that insights are actionable, timely, and aligned with business objectives. Overall, Tableau represents a bridge between complex datasets and operational intelligence, providing organizations with a robust, flexible, and scalable BI platform.

DOI: https://doi.org/10.5281/zenodo.17275764

 

Published by: vikaspatanker

Authors: Joseph Fernandes

Abstract: Power BI has established itself as a versatile and comprehensive platform for the business intelligence (BI) lifecycle, supporting data integration, development, visualization, collaboration, and ongoing maintenance. This review article examines Power BI’s capabilities in consolidating heterogeneous data sources, performing robust ETL transformations, and delivering interactive dashboards that provide actionable insights for enterprise decision-making. The discussion explores key aspects of implementation, including agile development methodologies, data governance, role-based access controls, and performance optimization techniques. Case studies across healthcare, retail, and finance demonstrate the platform’s practical impact, highlighting efficiency gains, improved reporting accuracy, real-time analytics, and enhanced regulatory compliance. Additionally, the article addresses common challenges such as integration complexity, technical skill requirements, and governance concerns, providing recommendations for mitigation. Emerging trends such as AI-driven analytics, predictive modeling, real-time streaming data, and cloud-native architectures are analyzed, illustrating the evolving role of Power BI in enabling intelligent decision-support systems. The review emphasizes the strategic advantages of Power BI, including democratization of analytics, scalability, and adaptability to diverse organizational requirements. By synthesizing current practices, technological capabilities, and future innovations, this article provides a roadmap for leveraging Power BI effectively to drive operational efficiency, data-driven decision-making, and organizational agility in dynamic business environments.

DOI: https://doi.org/10.5281/zenodo.17275705

 

Published by: vikaspatanker

Authors: Ayush Pravin Kudale

Abstract: This study offers a repeatable, Python-based framework for unified social media analytics that uses open APIs to connect disparate platforms like YouTube, Reddit, and Twitter. The strategy promotes transparency, explainability, and real-time engagement by emphasizing cross-platform integration, user-centric sentiment analysis, and graph-based pattern recognition for actionable insights. The framework's adaptability solves the research problems of data heterogeneity, scalability, and ethical stewardship while opening up new possibilities in marketing, crisis management, public opinion tracking, and policy-making. The massive, dynamic, and diverse statistics generated by social media platforms offer enormous possibilities for examining sentiment, public opinion, trending patterns, and the spread of information

DOI: http://doi.org/10.5281/zenodo.17275671

Published by: vikaspatanker

Authors: Vivek Sharma

 

Abstract: In contemporary enterprises, data fragmentation across multiple systems, departments, and formats poses significant challenges to decision-making, reporting accuracy, and operational efficiency. A Single Source of Truth (SSOT) addresses these challenges by consolidating heterogeneous data into a centralized, authoritative repository. This review examines the implementation of SSOT using SQL databases, robust ETL pipelines, and Power BI for visualization and governance. It explores the principles of data governance, including data ownership, quality control, role-based security, and regulatory compliance, emphasizing their critical role in maintaining data integrity and trustworthiness. The review also details best practices for relational database design, performance optimization, and ETL automation to ensure timely and accurate data delivery. Case studies across healthcare, financial services, and retail illustrate practical applications, demonstrating improved reporting efficiency, operational responsiveness, and decision-making capabilities. Furthermore, the integration of SSOT across enterprise workflows, combined with monitoring, audit trails, and automated alerts, underscores the value of a governed, centralized data ecosystem. The article highlights current challenges, including system complexity, adoption barriers, and legacy integration, and offers strategies for mitigation. Looking forward, emerging trends such as cloud-native architectures, real-time streaming, AI-enhanced analytics, and hybrid or federated data models suggest new avenues for enhancing SSOT utility and scalability. By providing a comprehensive framework, this review underscores the strategic, operational, and compliance benefits of SSOT, positioning it as a cornerstone for modern, data-driven enterprises seeking reliability, agility, and insight-driven decision-making.

DOI: https://doi.org/10.5281/zenodo.17275623

 

Published by: vikaspatanker

Authors: Anjali Thomas

 

Abstract: In today’s competitive business landscape, data-driven decision-making has become a strategic imperative. Organizations are increasingly turning to business intelligence (BI) platforms to transform raw data into actionable insights that guide growth, efficiency, and innovation. Among these platforms, Power BI stands out as a versatile solution that bridges the gap between technical complexity and user accessibility. This review article presents a comprehensive framework for successful Power BI adoption, emphasizing the interplay between governance, integration, scalability, and organizational readiness. The paper begins by outlining the challenges enterprises face when shifting from intuition-based management to data-centric practices, highlighting issues of data silos, inconsistent reporting, and resistance to cultural change. It then explores how Power BI’s architecture—spanning ETL processes, SQL integration, cloud deployment, and security mechanisms—can serve as the backbone for a sustainable BI strategy. The review further examines practical use cases across industries, DevOps-driven automation, and the role of training programs in fostering a self-service analytics culture. Through a critical discussion of opportunities and limitations, the article underscores that successful Power BI adoption requires more than technology; it demands alignment between people, processes, and platforms. By providing a structured roadmap, this study offers organizations a pragmatic guide to embedding Power BI within their BI lifecycle. The conclusion reaffirms that Power BI is not simply a reporting tool but a catalyst for building data-driven cultures that enhance agility, competitiveness, and long-term decision-making excellence.

DOI: https://doi.org/10.5281/zenodo.17275574

 

Published by: vikaspatanker

Authors: Prof. Mayanka Roy Mandal, Prof. Shraddha Tiwari, Prof. Ankita Fouzdar

Abstract: The rapid growth of electric vehicles (EVs) has created a strong demand for compact, reliable, and high-efficiency DC–DC converters to ensure effective power management and extended driving range. This study focuses on the development of high-efficiency DC–DC converters specifically designed for EV applications, addressing challenges such as wide input voltage variations, high power density, and stringent thermal constraints. Advanced topologies including interleaved, resonant, and soft-switching techniques are explored to minimize switching losses and improve overall efficiency. Furthermore, integration of digital control strategies and advanced semiconductor devices such as SiC and GaN MOSFETs enhances performance while reducing converter size and weight. Simulation and experimental results demonstrate improved efficiency, voltage regulation, and transient response under dynamic load conditions. The proposed converters are shown to meet the critical requirements of modern EV powertrains, offering a sustainable solution for future electric mobility.

Published by: vikaspatanker