Worldwide Journal of Creative Research and Thoughts
type: channel
description: Worldwide Journal of Creative Research and Thoughts (WJCRT) Journal Particulars Particulars Details Title Proper Worldwide Journal of Creative Research and Thoughts Title Variant WJCRT Publisher Wissira Research Lab LLC Publisher Address 1743 Kenmare Drive, Indiana, Carmel, United States – 46074 Journal Email editor@wjcrt.org Publisher Email editor@wissira.us ISSN (Online) 3143-7850 Starting Year 2025 Language English Publication Model International Peer-Reviewed Open Access Journal Publication Format Online Peer Review Double-Blind Peer Review About the Journal The Worldwide Journal of Creative Research and Thoughts (WJCRT) is an international, peer-reviewed, open-access scholarly journal established to promote original, innovative, interdisciplinary, and impactful research across a broad range of academic and professional disciplines. The journal serves as a global platform where researchers, academicians, scientists, educators, innovators, policymakers, industry professionals, artists, entrepreneurs, and practitioners can publish high-quality scholarly work that advances knowledge through creativity, critical thinking, and evidence-based research. In today's rapidly evolving global environment, innovation has become the driving force behind scientific discovery, technological advancement, educational transformation, economic development, and social progress. WJCRT recognizes that meaningful solutions to contemporary global challenges often emerge from creative thinking combined with rigorous academic investigation. Therefore, the journal actively encourages research that transcends traditional disciplinary boundaries and promotes collaboration among diverse fields of knowledge. The journal welcomes theoretical, empirical, conceptual, experimental, methodological, and practice-oriented studies that contribute to the advancement of scholarship while addressing real-world problems through innovative approaches. WJCRT believes that interdisciplinary collaboration enh…
publisher: Wissira Research Lab LLC
language: en
publication name: Worldwide Journal of Creative Research and Thoughts
issn: 3143-7850
items: Public Discourse Around NEP 2020 Draft: Media Analysis
items: IoT-Based Smart Environmental Monitoring for Sustainable Governance and Regulatory Compliance
items: AI for Audience Targeting and Segmentation: Emerging Trends in Data Analytics
items: Data-Driven Decision Making in Backend Engineering: Tools and Frameworks for Modern Startups
items: Use of Digital Libraries in Enhancing Postgraduate Research Output
items: Generative AI Adoption in Commerce Education: Its Impact on Students' Learning Effectiveness, Critical Thinking, and Career Readiness
items: Using Apache Camel’s REST DSL for Building RESTful Services
items: Security Best Practices for Middleware Applications in JBoss Fuse
items: Strategies for Managing Large-Scale Integration Projects with Apache Camel
items: Using Red Hat Decision Manager with JBoss Fuse for Business Rules Management
items: Advanced Techniques in Apache Camel for Throttling and Rate Limiting
items: Using Decision Trees for Targeted Marketing in Vending Machine Upgrades
items: Text Analytics for Solving Employee Helpdesk Challenges
items: The Role of Data Analytics in Shaping Internal Communication Strategies
items: Regression-Based Insights for Warehouse Customer Optimization
items: Enhancing Market Share Through Advanced Location Analytics
items: Building End-to-End Analytics Pipelines for Corporate Decision Support
items: Evaluating the Role of Data Science in Organizational Productivity
items: Leveraging Python and SQL for Comprehensive Business Data Analysis
items: Predictive Salary Increment Models Using R
items: Designing Interactive Dashboards for Real-Time Business Intelligence
items: Future Trends in Business Intelligence: Cloud Integration with SAP and Snowflake
items: Data Governance in Cloud-Based BI Systems: Strategies and Frameworks
items: Bridging Business and Technology Through Effective SAP BI Solutions
items: Leveraging Data Insights to Drive Strategic Decision Making in Enterprises
items: Innovations in HDMI Validation Using Display Emulators
items: Scaling Data Warehousing Solutions for Global Enterprise Needs
items: Improving User Engagement in BI Projects Through Custom Dashboards
items: Advanced Data Modeling for Multi-Tenant Business Environments
items: Cross-Platform Data Integration with SAP BW and Modern Cloud Tools

Public Discourse Around NEP 2020 Draft: Media Analysis
type: item
description: India’s National Education Policy (NEP) 2020 represented a landmark reform, aimed at overhauling a system in place since 1986 and aligning educational outcomes with 21st‑century needs. This study investigates the public discourse around the NEP 2020 draft during the consultation period from January 2019 to July 2020, focusing on how leading English‑language print outlets and major digital news platforms framed, critiqued, and disseminated the policy’s key provisions. Through a two‑stage mixed‑methods design—quantitative content analysis of 1,200 news articles and qualitative thematic and sentiment analysis of a stratified sample—this research uncovers dominant narrative patterns, stakeholder representation, and tonal orientation. Findings reveal that the most prevalent discourse themes were Language and Curriculum Reform, Access and Equity, and Institutional Autonomy. Sentiment analysis indicates a generally positive media outlook on governance decentralization but highlights significant skepticism regarding financial and privatization aspects. Government voices predominated, while grassroots perspectives were noticeably underrepresented. These insights underscore the pivotal role of media framing in shaping educational policy debates and suggest that a more inclusive consultation process—amplifying voices of parents, students, and civil society—could foster broader public buy‑in.
subject: Articles
  topic: section
subject: NEP 2020
  topic: keywords
subject: Public Discourse
  topic: keywords
subject: Media Analysis
  topic: keywords
subject: Content Analysis
  topic: keywords
subject: Policy Framing
  topic: keywords
creator: Anil Kumar
rights: Copyright (c) 2026
date: 2026-08-10
publication date: 2026-08-10
starting page: (33
ending page: 40)

IoT-Based Smart Environmental Monitoring for Sustainable Governance and Regulatory Compliance
type: item
description: Environmental degradation, unchecked industrial emissions, and inconsistent enforcement of pollution-control norms remain among the most persistent challenges facing regulatory bodies across the world. Traditional environmental monitoring relies on periodic manual sampling, which is slow, spatially sparse, and vulnerable to manipulation, leaving regulators with incomplete pictures of real-world conditions. This paper proposes an IoT-based Smart Environmental Monitoring (IoT-SEM) framework designed to support sustainable governance and strengthen regulatory compliance. The proposed system integrates low-cost multi-parameter sensor nodes, edge-level preprocessing, wireless communication, and cloud-based analytics to deliver continuous, tamper-resistant, and high-resolution environmental data to regulators, industries, and citizens alike. The architecture is organized into four layers: sensing, edge and communication, cloud analytics, and governance-facing dashboards, with automated alerting and audit-trail generation built in to support legal admissibility of compliance data. A simulated deployment across five monitoring zones demonstrates the framework's capacity to detect threshold breaches in near real time, reduce reporting latency compared to manual inspection cycles, and generate standardized compliance reports aligned with regulatory reporting formats. The discussion situates the framework within existing e-governance and smart-city literature, highlighting how continuous IoT-based data streams can shift environmental regulation from a reactive, complaint-driven model toward a proactive, evidence-based one. Challenges of sensor drift, interoperability, cybersecurity, and institutional readiness are examined, along with policy recommendations for standardizing IoT-derived data as admissible regulatory evidence. The paper concludes that IoT-SEM systems, when paired with appropriate governance structures, can meaningfully advance sustainable development goals whil…
subject: Articles
  topic: section
subject: Internet of Things
  topic: keywords
subject: environmental monitoring
  topic: keywords
subject: sustainable governance
  topic: keywords
subject: regulatory compliance
  topic: keywords
subject: smart sensors
  topic: keywords
subject: e-governance
  topic: keywords
subject: pollution control
  topic: keywords
creator: Dr Jyoti Janardhan Reddy
rights: Copyright (c) 2026
date: 2026-08-08
publication date: 2026-08-08
starting page: (26
ending page: 32)

AI for Audience Targeting and Segmentation: Emerging Trends in Data Analytics
type: item
description: The proliferation of digital touchpoints has produced volumes of behavioral, transactional, and contextual data that traditional rule-based marketing segmentation methods are increasingly unable to process effectively. Artificial intelligence has emerged as the dominant paradigm for transforming this data deluge into actionable audience insight, enabling marketers to move from static demographic segments toward dynamic, behavior-driven clusters that update continuously as new signals arrive. This paper examines emerging trends in AI-based audience targeting and segmentation, synthesizing findings from recent literature on machine learning clustering techniques, predictive analytics, natural language processing, and autonomous agentic systems applied to marketing. The paper presents a structured framework that categorizes AI-driven segmentation approaches into descriptive, predictive, and prescriptive layers, and discusses how unsupervised algorithms such as K-means and DBSCAN are combined with supervised predictive models to generate segments that are both statistically coherent and commercially actionable. A comparative analysis of AI-driven and traditional segmentation approaches, informed by patterns reported across the reviewed studies, indicates measurable gains in targeting precision and campaign conversion outcomes. The discussion addresses data quality, algorithmic bias, interpretability, and privacy regulation as recurring constraints on responsible deployment, and situates the rise of autonomous AI agents within a broader trajectory toward hyper-personalized, real-time marketing systems. The paper concludes that while AI substantially improves the precision and adaptability of audience targeting, its long-term value depends on organizations pairing technical capability with interpretability safeguards and privacy-conscious data governance.
subject: Articles
  topic: section
subject: artificial intelligence
  topic: keywords
subject: audience segmentation
  topic: keywords
subject: targeted marketing
  topic: keywords
subject: predictive analytics
  topic: keywords
subject: machine learning
  topic: keywords
subject: data analytics
  topic: keywords
subject: personalization
  topic: keywords
creator: Dr Kalpesh Rasiklal Rakholia
rights: Copyright (c) 2026
date: 2026-08-07
publication date: 2026-08-07
starting page: (20
ending page: 25)

Data-Driven Decision Making in Backend Engineering: Tools and Frameworks for Modern Startups
type: item
description: Data-driven decision making (DDDM) has become a defining characteristic of successful modern startups, shaping not only business strategy but also the architecture and operation of backend engineering systems. This manuscript examines how startups leverage data engineering pipelines, observability stacks, and backend frameworks to support informed, real-time decision making. Drawing on verifiable industry sources and technical literature, the study reviews the tools and frameworks most commonly adopted by startups—including data pipeline platforms, backend frameworks such as Django, FastAPI, and Spring Boot, and observability tools such as Prometheus, Grafana, and OpenTelemetry—and evaluates their contribution to operational efficiency and strategic decision quality. The paper finds that while data-driven organizations demonstrate measurable advantages in customer acquisition and profitability, the effective use of data in backend engineering requires careful attention to data quality, tool integration, and organizational discipline, alongside a balanced approach that combines quantitative insight with contextual judgment.
subject: Articles
  topic: section
subject: data-driven decision making
  topic: keywords
subject: backend engineering
  topic: keywords
subject: observability
  topic: keywords
subject: data pipelines
  topic: keywords
subject: startup technology
  topic: keywords
subject: microservices
  topic: keywords
subject: monitoring tools
  topic: keywords
creator: Sachin Sangal
rights: Copyright (c) 2026
date: 2026-08-06
publication date: 2026-08-06
starting page: (14
ending page: 19)

Use of Digital Libraries in Enhancing Postgraduate Research Output
type: item
description: Digital libraries have become central to postgraduate education, offering round-the-clock access to journals, e-books, theses, and institutional repositories that were previously constrained by physical location and operating hours. This manuscript examines the role of digital libraries in enhancing postgraduate research output, drawing on verifiable empirical studies across diverse academic contexts, including Kenya, Nigeria, Ghana, Ethiopia, India, Nepal, and the United Kingdom. The review synthesizes findings on usage patterns, perceived benefits, and persistent barriers such as infrastructural deficits, digital literacy gaps, and the digital divide. The study finds that while digital libraries and institutional repositories demonstrably improve access to scholarly resources and support research productivity, their effectiveness is frequently undermined by inadequate ICT infrastructure, limited awareness, and uneven institutional investment, particularly in developing-country contexts. The paper concludes with recommendations for strengthening digital library services to maximize their contribution to postgraduate research productivity.
subject: Articles
  topic: section
subject: digital libraries
  topic: keywords
subject: postgraduate research
  topic: keywords
subject: institutional repositories
  topic: keywords
subject: research productivity
  topic: keywords
subject: information literacy
  topic: keywords
subject: academic libraries
  topic: keywords
subject: digital divide
  topic: keywords
creator: Dr Anand Kumar Dwivedi
rights: Copyright (c) 2026
date: 2026-08-06
publication date: 2026-08-06
starting page: (8
ending page: 13)

Generative AI Adoption in Commerce Education: Its Impact on Students' Learning Effectiveness, Critical Thinking, and Career Readiness
type: item
description: Generative Artificial Intelligence (GenAI) tools have proliferated rapidly in recent years and are now starting to revolutionize the way teaching and learning happens in higher education institutions. However, there still exists a dearth of empirical studies on the use of GenAI tools among commerce students in India. The current study focuses on evaluating the effects of GenAI adoption among commerce students on their learning effectiveness, critical thinking ability, and career-readiness preparedness, based on the Technology Acceptance Model, UTAUT2, and Bloom’s Taxonomy. Using a descriptive and survey-oriented research methodology, this study explores GenAI tool adoption patterns and their relationships with academic and professional aspects using correlations, regressions, and structural equation modeling. The results reveal that GenAI tool adoption has a significant positive effect on students' perceived learning effectiveness and career-readiness preparedness, and their effect on critical thinking ability is minimal and limited to lower order thinking skills only. Facilitating conditions make an impactful contribution to these relationships. Overall, the study proves that the value of GenAI tools in education is tied to systematic adoption and highlights the importance of designing a commerce curriculum that supports NEP 2020 goals.
subject: Articles
  topic: section
subject: Generative AI
  topic: keywords
subject: Commerce Education
  topic: keywords
subject: Learning Effectiveness
  topic: keywords
subject: Critical Thinking
  topic: keywords
subject: Career Readiness
  topic: keywords
subject: NEP 2020
  topic: keywords
creator: Dr. Ankur Aggarwal
rights: Copyright (c) 2026 Worldwide Journal of Creative Research and Thoughts
date: 2026-08-05
publication date: 2026-08-05
starting page: (1
ending page: 7)
doi: 10.63345/wjcrt.v2.i3.301

Using Apache Camel’s REST DSL for Building RESTful Services
type: item
description: With the growing adoption of microservices architectures, RESTful services have become the backbone of modern web-based communication. Apache Camel, a popular open-source integration framework, provides a powerful and flexible approach for building RESTful services. Camel’s REST DSL (Domain Specific Language) simplifies the creation, configuration, and deployment of REST APIs by abstracting the underlying complexities and offering seamless integration with multiple data formats and transport protocols. This paper explores the use of Apache Camel’s REST DSL for building RESTful services, focusing on its ease of use, flexibility, and scalability in modern application architectures. We discuss the benefits of using Camel for REST API development, provide examples of its capabilities, and examine how it can be integrated into enterprise systems to simplify service orchestration and communication. The study includes an overview of RESTful service development using Camel’s REST DSL, a step-by-step methodology, and real-world use cases to highlight its advantages in building maintainable, scalable, and efficient services.
subject: Articles
  topic: section
subject: Apache Camel
  topic: keywords
subject: REST DSL
  topic: keywords
subject: RESTful Services
  topic: keywords
subject: Microservices
  topic: keywords
subject: Web Services
  topic: keywords
subject: Integration
  topic: keywords
subject: API Development
  topic: keywords
subject: Service Orchestration
  topic: keywords
subject: Author Name
  topic: keywords
subject: Scopus
  topic: keywords
subject: Springer
  topic: keywords
subject: Journal Name
  topic: keywords
subject: Wissira
  topic: keywords
subject: Journal Short Form
  topic: keywords
subject: Wissira Press
  topic: keywords
subject: Wissira Research Lab
  topic: keywords
subject: SSRN
  topic: keywords
subject: ISSN
  topic: keywords
subject: Academia
  topic: keywords
subject: UGC Care
  topic: keywords
subject: PubMed
  topic: keywords
subject: WOS
  topic: keywords
creator: Er. Shubham Jain
rights: Copyright (c) 2026
date: 2026-07-07
publication date: 2026-07-07
starting page: (29
ending page: 35)

Security Best Practices for Middleware Applications in JBoss Fuse
type: item
description: Middleware applications are essential for enabling communication and data exchange between different software components in enterprise systems. As organizations increasingly rely on these applications for integrating services, ensuring their security is critical. JBoss Fuse, an open-source enterprise service bus (ESB), is a widely used platform for integrating various systems, but like any middleware solution, it presents unique security challenges. This paper explores security best practices for securing middleware applications deployed in JBoss Fuse. We examine various security concerns such as data protection, authentication, authorization, auditing, and threat mitigation within the JBoss Fuse environment. By analyzing the security features provided by JBoss Fuse, as well as incorporating industry-standard practices, this paper provides a comprehensive guide to securing middleware applications in JBoss Fuse. A statistical analysis is included to compare the effectiveness of different security practices in ensuring the safety of data and systems within the middleware.
subject: Articles
  topic: section
subject: JBoss Fuse
  topic: keywords
subject: Middleware Security
  topic: keywords
subject: Authentication
  topic: keywords
subject: Authorization
  topic: keywords
subject: Data Protection
  topic: keywords
subject: Threat Mitigation
  topic: keywords
subject: ESB Security
  topic: keywords
subject: Enterprise Integration
  topic: keywords
subject: Security Best Practices
  topic: keywords
subject: Author Name
  topic: keywords
subject: Scopus
  topic: keywords
subject: Springer
  topic: keywords
subject: Journal Name
  topic: keywords
subject: Wissira
  topic: keywords
subject: Journal Short Form
  topic: keywords
subject: Wissira Press
  topic: keywords
subject: Wissia Research Lab
  topic: keywords
subject: Research Gate
  topic: keywords
subject: SSRN
  topic: keywords
subject: ISSN
  topic: keywords
subject: Academia
  topic: keywords
subject: UGC Care
  topic: keywords
subject: PubMed
  topic: keywords
subject: WOS
  topic: keywords
creator: Huang Bo
rights: Copyright (c) 2026
date: 2026-07-07
publication date: 2026-07-07
starting page: (22
ending page: 28)

Strategies for Managing Large-Scale Integration Projects with Apache Camel
type: item
description: In the era of enterprise integration, the ability to seamlessly connect a wide variety of systems, applications, and data sources is crucial for maintaining business efficiency. Apache Camel, an open-source integration framework, is widely recognized for its versatility and scalability in managing complex integration projects. However, as integration projects scale up, managing them becomes increasingly challenging. This paper explores strategies for managing large-scale integration projects using Apache Camel, focusing on best practices, architectural considerations, and the tools available for optimizing performance, maintaining system reliability, and ensuring successful project outcomes. We discuss the key elements involved in such projects, including design principles, modularity, error handling, testing, and monitoring, while also considering the role of Camel’s extensive component library. Through real-world case studies and empirical analysis, this paper demonstrates the practical implementation of these strategies in large-scale projects, and provides guidance for organizations looking to leverage Apache Camel to manage and optimize complex integration environments.
subject: Articles
  topic: section
subject: Apache Camel
  topic: keywords
subject: Integration Projects
  topic: keywords
subject: Large-Scale Integration
  topic: keywords
subject: Enterprise Integration
  topic: keywords
subject: System Architecture
  topic: keywords
subject: Error Handling
  topic: keywords
subject: Modularity
  topic: keywords
subject: Testing
  topic: keywords
subject: Performance Optimization
  topic: keywords
subject: Author Name
  topic: keywords
subject: Scopus
  topic: keywords
subject: Springer
  topic: keywords
subject: Wissira
  topic: keywords
subject: Journal Name
  topic: keywords
subject: Journal Short Name
  topic: keywords
subject: Wissira Press
  topic: keywords
subject: SSRN
  topic: keywords
subject: ISSN
  topic: keywords
subject: Academia
  topic: keywords
subject: PubMed
  topic: keywords
subject: UGC Care
  topic: keywords
subject: WOS
  topic: keywords
creator: Prof. (Dr) MSR Prasad
rights: Copyright (c) 2026
date: 2026-07-06
publication date: 2026-07-06
starting page: (15
ending page: 21)

Using Red Hat Decision Manager with JBoss Fuse for Business Rules Management
type: item
description: Business rules management plays a crucial role in enterprise systems, providing a mechanism to separate business logic from application code, allowing organizations to adapt to changing business requirements with greater agility. Red Hat Decision Manager (RHDM), combined with JBoss Fuse, offers a robust solution for implementing business rules within integration workflows. RHDM is a comprehensive platform for managing, executing, and monitoring business rules, while JBoss Fuse serves as an enterprise service bus (ESB) that facilitates the integration of different applications, data sources, and services. This paper explores the integration of Red Hat Decision Manager with JBoss Fuse for effective business rules management. We examine how this combination can help streamline business process management, automate decision-making, and enhance operational efficiency. The study also provides a detailed analysis of the architecture, implementation strategies, and benefits of using RHDM with JBoss Fuse, supported by real-world examples and case studies. Furthermore, we explore the potential challenges and limitations associated with this integration.
subject: Articles
  topic: section
subject: Red Hat Decision Manager
  topic: keywords
subject: JBoss Fuse
  topic: keywords
subject: Business Rules Management
  topic: keywords
subject: Business Process Management
  topic: keywords
subject: Decision Automation
  topic: keywords
subject: Integration
  topic: keywords
subject: Enterprise Architecture
  topic: keywords
subject: SOA
  topic: keywords
subject: Author Name
  topic: keywords
subject: Scopus
  topic: keywords
subject: Springer
  topic: keywords
subject: Journal Name
  topic: keywords
subject: Wissira
  topic: keywords
subject: Journal Short Form
  topic: keywords
subject: Wissira Press
  topic: keywords
subject: Wissira Research Lab
  topic: keywords
subject: Research Gate
  topic: keywords
subject: SSRN
  topic: keywords
subject: ISSN
  topic: keywords
subject: Academia
  topic: keywords
subject: UGC Care
  topic: keywords
subject: PubMed
  topic: keywords
subject: WOS
  topic: keywords
creator: Li Na
rights: Copyright (c) 2026
date: 2026-07-04
publication date: 2026-07-04
starting page: (8
ending page: 14)

Advanced Techniques in Apache Camel for Throttling and Rate Limiting
type: item
description: In the context of distributed systems, managing traffic flow and ensuring the proper handling of high volumes of messages are critical for maintaining system stability and ensuring optimal performance. Throttling and rate limiting are common techniques used to control the rate at which requests are processed, thereby preventing system overloads and ensuring fair resource usage. Apache Camel, a widely used open-source integration framework, offers advanced techniques for throttling and rate limiting that can be seamlessly integrated into enterprise applications. This paper explores the implementation of these techniques using Apache Camel, focusing on advanced features such as dynamic throttling, custom rate-limiting strategies, and the integration of external systems for traffic control. The paper presents a detailed review of Apache Camel’s built-in features for throttling and rate limiting, followed by a comprehensive statistical analysis of their performance in real-world scenarios. The findings demonstrate how these techniques can significantly improve the efficiency and reliability of integration flows within Apache Camel applications.
subject: Articles
  topic: section
subject: Apache Camel
  topic: keywords
subject: Throttling
  topic: keywords
subject: Rate Limiting
  topic: keywords
subject: Traffic Control
  topic: keywords
subject: Integration
  topic: keywords
subject: Enterprise Applications
  topic: keywords
subject: Performance Optimization
  topic: keywords
subject: Scalability
  topic: keywords
subject: Author Name
  topic: keywords
subject: Scopus
  topic: keywords
subject: Springer
  topic: keywords
subject: Journal Name
  topic: keywords
subject: Wissira
  topic: keywords
subject: Journal Short Form
  topic: keywords
subject: Wissira Press
  topic: keywords
subject: Wissira Research Lab
  topic: keywords
subject: Research Press
  topic: keywords
subject: SSRN
  topic: keywords
subject: ISSN
  topic: keywords
subject: Academia
  topic: keywords
subject: UGC Care
  topic: keywords
subject: PubMed
  topic: keywords
subject: WOS
  topic: keywords
creator: Suganya V
rights: Copyright (c) 2026
date: 2026-07-03
publication date: 2026-07-03
starting page: (1
ending page: 7)

Using Decision Trees for Targeted Marketing in Vending Machine Upgrades
type: item
description: The vending machine industry is experiencing rapid technological advancements, with smart vending machines offering features like cashless payments, remote monitoring, and AI-enabled functionalities. Effectively marketing these upgrades requires precise targeting to ensure relevance and cost-efficiency. This study investigates the use of decision trees, a machine learning technique, to identify and target customer segments most likely to adopt vending machine upgrades. Leveraging real-world data on customer demographics, purchase history, and usage patterns, the study demonstrates the efficacy of decision trees in optimizing marketing campaigns. The findings suggest that targeted marketing using decision trees increases conversion rates and enhances overall customer satisfaction while reducing marketing expenditures.
subject: Articles
  topic: section
subject: Decision Trees, Machine Learning, Targeted Marketing, Vending Machine Upgrades, Customer Segmentation, Data Analytics.
  topic: keywords
creator: Prof (Dr) Ajay Shriram Kushwaha
rights: Copyright (c) 2026
date: 2026-04-05
publication date: 2026-04-05
starting page: (58
ending page: 71)

Text Analytics for Solving Employee Helpdesk Challenges
type: item
description: Employee helpdesks are a vital component of modern organizations, addressing concerns ranging from IT issues to HR-related queries. However, inefficiencies such as delayed responses, misclassification of tickets, and high operational costs often plague these systems. This study investigates the use of text analytics, particularly Natural Language Processing (NLP) and machine learning (ML), to address these challenges. By leveraging these techniques, organizations can automate ticket classification, analyze employee sentiment, and predict recurring issues. This manuscript explores the application of text analytics in helpdesk systems through a literature review, a methodological framework, and empirical results from a case study. Findings indicate that text analytics significantly improves efficiency, accuracy, and employee satisfaction, paving the way for smarter and more responsive helpdesk operations.
subject: Articles
  topic: section
subject: Text analytics, Employee helpdesk, Natural Language Processing, Ticket classification, Sentiment analysis, Machine learning, Workflow optimization, Organizational efficiency.
  topic: keywords
creator: A Renuka
rights: Copyright (c) 2026
date: 2026-04-05
publication date: 2026-04-05
starting page: (43
ending page: 57)

The Role of Data Analytics in Shaping Internal Communication Strategies
type: item
description: In the contemporary business environment, internal communication is pivotal in fostering organizational cohesion, employee engagement, and strategic alignment. The integration of data analytics into internal communication strategies has transformed traditional methods, enabling organizations to gain actionable insights, improve communication efficiency, and enhance overall performance. This paper explores how data analytics aids in understanding employee behavior, evaluating communication effectiveness, and driving decision-making in crafting impactful internal communication strategies. It also examines methodologies, tools, and case studies that illustrate the transformative power of data analytics in improving organizational transparency, inclusivity, and innovation.
subject: Articles
  topic: section
subject: Data Analytics, Internal Communication, Employee Engagement, Organizational Strategy, Business Intelligence, Communication Effectiveness
  topic: keywords
creator: Shalu Jain
rights: Copyright (c) 2026
date: 2026-04-01
publication date: 2026-04-01
starting page: (29
ending page: 42)

Regression-Based Insights for Warehouse Customer Optimization
type: item
description: The efficient management of warehouse operations is a cornerstone of successful supply chain management. Customer optimization, defined as aligning warehouse processes with customer preferences and demand, is critical in achieving cost savings and maintaining customer satisfaction. Regression analysis, a robust statistical method, has proven to be instrumental in extracting meaningful insights from large datasets. This paper explores how regression models can optimize warehouse processes by identifying key drivers of customer satisfaction, predicting demand patterns, and streamlining operations. Through a detailed case study and analysis, we demonstrate the practicality of regression-based approaches in transforming data into actionable strategies. The study concludes with recommendations for warehouse managers and highlights future research opportunities in data-driven logistics optimization.
subject: Articles
  topic: section
subject: Warehouse optimization, customer satisfaction, regression analysis, supply chain efficiency, inventory management.
  topic: keywords
creator: Prof. (Dr) Punit Goel
rights: Copyright (c) 2026
date: 2026-04-01
publication date: 2026-04-01
starting page: (14
ending page: 28)

Enhancing Market Share Through Advanced Location Analytics
type: item
description: In an increasingly competitive marketplace, businesses must leverage every available tool to gain an edge. Location analytics, which integrates geospatial data with advanced analytical techniques, offers a powerful approach for identifying new market opportunities, enhancing operational efficiency, and delivering superior customer experiences. This paper explores the strategic use of advanced location analytics in enhancing market share, supported by a comprehensive review of the existing literature, detailed methodologies, and empirical findings. Our analysis demonstrates how location intelligence can optimize decision-making in retail, logistics, and urban planning, among other domains. Key findings indicate that businesses employing location analytics experience notable improvements in revenue, customer retention, and market penetration. This study concludes by emphasizing the necessity of integrating location analytics into core business strategies for sustained growth and competitive advantage.
subject: Articles
  topic: section
subject: Location analytics, geospatial data, market share, business intelligence, geographic information systems (GIS), customer experience, data-driven decision-making.
  topic: keywords
creator: Er. Lagan Goel
rights: Copyright (c) 2026
date: 2026-04-01
publication date: 2026-04-01
starting page: (1
ending page: 13)

Building End-to-End Analytics Pipelines for Corporate Decision Support
type: item
description: In today’s competitive business landscape, organizations require effective analytics pipelines to transform raw data into actionable insights that can guide decision-making. An end-to-end analytics pipeline integrates various stages of data processing, from data collection and cleaning to analysis and visualization. This paper explores the design, development, and implementation of such pipelines, focusing on how they can enhance corporate decision support. The research aims to demonstrate how organizations can leverage these pipelines to drive data-driven decision-making, increase operational efficiency, and foster innovation. The methodology involves a comprehensive review of existing frameworks, followed by the design and implementation of a prototype pipeline for decision support in a corporate environment. Finally, the results show that the implementation of these pipelines can significantly enhance decision-making by providing real-time insights and predictive analytics.
subject: Articles
  topic: section
subject:
  topic: keywords
  value: End-to-End Analytics Pipeline, Corporate Decision Support, Data Integration, Predictive Analytics, Data Visualization, Data Processing, Business Intelligence, Real-Time Decision Making, Big Data, Decision Support Systems (DSS)
creator: Er. Niharika Singh
rights: Copyright (c) 2026
date: 2026-01-05
publication date: 2026-01-05
starting page: (59
ending page: 73)

Evaluating the Role of Data Science in Organizational Productivity
type: item
description: The integration of data science into organizational operations has emerged as a powerful tool for improving productivity. Through the application of advanced analytical methods, organizations can derive valuable insights, optimize decision-making, and enhance operational efficiency. This manuscript explores the role of data science in boosting organizational productivity by analyzing existing literature, methodologies, and results from case studies. It presents a framework that organizations can adopt to effectively utilize data science techniques for driving performance improvements. Key challenges and opportunities for the integration of data science are also discussed. Finally, the paper provides recommendations for organizations seeking to leverage data science as a strategic asset for productivity enhancement.
subject: Articles
  topic: section
subject:
  topic: keywords
  value: Data Science, Organizational Productivity, Decision-Making, Machine Learning, Big Data, Predictive Analytics, Performance Optimization, Operational Efficiency, DataDriven Decision-Making, Data Analytics.
creator: Dr S P Singh
rights: Copyright (c) 2026
date: 2026-01-05
publication date: 2026-01-05
starting page: (43
ending page: 58)

Leveraging Python and SQL for Comprehensive Business Data Analysis
type: item
description: In today's data-driven business environment, organizations rely heavily on data analysis for strategic decision-making. Python and SQL, with their complementary capabilities, form a robust framework for extracting, analyzing, and visualizing data to derive actionable insights. Python's versatility in data manipulation and visualization, coupled with SQL's power in querying structured data, makes this combination indispensable for comprehensive business data analysis. This manuscript explores the integration of Python and SQL in business analytics, detailing their applications in various industries. A structured methodology is presented, followed by a case study to demonstrate the results of leveraging these technologies. The findings highlight how Python and SQL can transform raw data into valuable insights, enabling businesses to optimize processes and improve profitability.
subject: Articles
  topic: section
subject: Python, SQL, Data Analysis, Business Intelligence, Data Visualization, Analytics, Machine Learning
  topic: keywords
creator: Raghav Jindal
rights: Copyright (c) 2026
date: 2026-01-02
publication date: 2026-01-02
starting page: (30
ending page: 42)

Predictive Salary Increment Models Using R
type: item
description: Predictive salary increment models offer a quantitative basis for determining salary adjustments, promoting transparency and fairness in human resource management. This study presents strategies for developing such models using R, a versatile programming language for statistical computing and machine learning. The paper emphasizes data preprocessing, feature engineering, and algorithm selection to predict salary increments accurately. The methodology includes data analysis, regression modeling, and machine learning techniques like decision trees and ensemble methods. Results from sample datasets demonstrate the feasibility and accuracy of the proposed approach. The study concludes with recommendations for practical implementation in organizational settings.
subject: Articles
  topic: section
subject: Predictive modeling, salary prediction, R programming, human resource analytics, machine learning, salary increment models.
  topic: keywords
creator: Palak Gupta
rights: Copyright (c) 2026
date: 2026-01-02
publication date: 2026-01-02
starting page: (16
ending page: 29)

Designing Interactive Dashboards for Real-Time Business Intelligence
type: item
description: Interactive dashboards have become indispensable tools in modern business intelligence (BI), enabling organizations to monitor performance, uncover insights, and make data-driven decisions in real time. This study explores the design, development, and evaluation of interactive dashboards for realtime BI. Drawing from user-centric design principles, advanced visualization techniques, and real-time data integration methodologies, this manuscript offers a comprehensive guide to creating dashboards that enhance organizational decision-making. Through a systematic literature review and an empirical methodology, the study evaluates the effectiveness of such dashboards across different business sectors. The findings demonstrate how well-designed dashboards improve operational efficiency, foster collaboration, and adapt to evolving business environments. Additionally, the study identifies key challenges, proposes solutions, and outlines future research directions in the field.
subject: Articles
  topic: section
subject: Interactive dashboards, real-time business intelligence, data visualization, user experience design, data-driven decision-making, real-time analytics.
  topic: keywords
creator: Er Akshun Chhapola
rights: Copyright (c) 2026
date: 2026-01-02
publication date: 2026-01-02
starting page: (1
ending page: 15)

Future Trends in Business Intelligence: Cloud Integration with SAP and Snowflake
type: item
description: Business Intelligence (BI) continues to evolve, shaped by advancements in cloud technologies and seamless integration between platforms. SAP and Snowflake represent leading solutions that enable organizations to process, analyze, and derive insights from large datasets. This study explores future trends in cloud BI, focusing on the integration of SAP and Snowflake. Emphasizing the benefits, challenges, and innovations, the paper offers insights into how these integrations optimize decisionmaking processes, reduce operational costs, and enhance agility. Key findings suggest that as organizations transition to hybrid cloud models, SAP and Snowflake will play pivotal roles in driving innovation in BI.
subject: Articles
  topic: section
subject: Business Intelligence, Cloud Integration, SAP, Snowflake, Data Analytics, Hybrid Cloud, Data Management.
  topic: keywords
creator: Drishti Chaudhary
rights: Copyright (c) 2026
date: 2025-10-06
publication date: 2025-10-06
starting page: (53
ending page: 65)

Data Governance in Cloud-Based BI Systems: Strategies and Frameworks
type: item
description: The emergence of cloud-based business intelligence (BI) systems has revolutionized how organizations manage and analyze data. However, this transformation has introduced unique challenges in data governance, including data security, compliance, and management complexities. This paper explores the strategies and frameworks for effective data governance in cloud-based BI systems. It highlights best practices, discusses key frameworks, and provides insights into successful implementation. The study integrates findings from existing literature with practical methodologies, concluding with actionable recommendations and identifying areas for future research.
subject: Articles
  topic: section
subject: Data Governance , Cloud-Based Business Intelligence , Data Security , Compliance , Data Management , Frameworks , Strategies
  topic: keywords
creator: Prof. Dr. Sanjay Kumar Bahl
rights: Copyright (c) 2026
date: 2025-10-06
publication date: 2025-10-06
starting page: (40
ending page: 52)

Bridging Business and Technology Through Effective SAP BI Solutions
type: item
description: In the era of digital transformation, organizations increasingly rely on robust Business Intelligence (BI) tools to bridge the gap between business needs and technological capabilities. SAP BI solutions have emerged as a pivotal enabler in optimizing data-driven decisionmaking processes. This study investigates the role of SAP BI in integrating business and technology, examining its benefits, challenges, and best practices for implementation. The research employs a qualitative and quantitative methodology to explore how SAP BI aligns organizational goals with technological advancements. Results indicate significant improvements in operational efficiency, data accuracy, and strategic planning. The study concludes with recommendations for maximizing SAP BI's potential and outlines future research directions.
subject: Articles
  topic: section
subject: SAP Business Intelligence, digital transformation, datadriven decision-making, technology integration, business optimization, strategic planning.
  topic: keywords
creator: Dr T. Aswini
rights: Copyright (c) 2026
date: 2025-10-03
publication date: 2025-10-03
starting page: (27
ending page: 39)

Leveraging Data Insights to Drive Strategic Decision Making in Enterprises
type: item
description: In the rapidly evolving business environment, leveraging data insights has emerged as a pivotal strategy for informed decision-making in enterprises. This study explores the integration of data analytics into strategic decision-making processes, emphasizing its role in optimizing operations, improving customer engagement, and fostering innovation. Using a comprehensive analysis of existing literature, we identify key trends, challenges, and best practices in utilizing data insights. Our findings demonstrate that data-driven approaches significantly enhance organizational agility and competitiveness. The study concludes by proposing a framework for future research and practical implementation of data-driven strategies.
subject: Articles
  topic: section
subject: Data analytics, Strategic decision-making, Business intelligence, Data-driven strategies, Enterprise innovation, Organizational agility, Big data.
  topic: keywords
creator: Dr. Sandeep Kumar
rights: Copyright (c) 2026
date: 2025-10-03
publication date: 2025-10-03
starting page: (13
ending page: 26)

Innovations in HDMI Validation Using Display Emulators
type: item
description: High-Definition Multimedia Interface (HDMI) validation is crucial for ensuring seamless interoperability between devices in the consumer electronics ecosystem. Display emulators offer a robust and cost-effective method to enhance HDMI testing by mimicking various display configurations, resolutions, and refresh rates. This study explores recent innovations in using display emulators for HDMI validation, emphasizing their impact on reducing testing costs, increasing test coverage, and accelerating product development cycles. Key advancements, such as programmable emulators and automated testing frameworks, are reviewed, along with their implications for the electronics industry.
subject: Articles
  topic: section
subject: HDMI validation, display emulators, automated testing, interoperability, consumer electronics, test coverage, display simulation, programmable emulators
  topic: keywords
creator: Dr. Lalit Kumar
rights: Copyright (c) 2026
date: 2025-10-03
publication date: 2025-10-03
starting page: (1
ending page: 12)

Scaling Data Warehousing Solutions for Global Enterprise Needs
type: item
description: As enterprises expand globally, the need for scalable and efficient data warehousing solutions becomes critical. This study explores strategies to scale data warehousing systems, ensuring optimal performance, reliability, and cost-efficiency across geographically distributed environments. The research evaluates architectural frameworks, cloud-native technologies, and big data integration methodologies to address scalability challenges. Empirical results demonstrate the potential of hybrid cloud architectures and distributed data processing frameworks in achieving desired scalability levels. The findings are aimed at aiding enterprises in designing robust data warehousing systems that align with global operational requirements.
subject: Articles
  topic: section
subject: Data Warehousing, Scalability, Global Enterprises, Cloud Computing, Distributed Systems, Big Data, Hybrid Cloud, Performance Optimization.
  topic: keywords
creator: Er. Shubham Jain
rights: Copyright (c) 2026
date: 2025-07-06
publication date: 2025-07-06
starting page: (52
ending page: 63)

Improving User Engagement in BI Projects Through Custom Dashboards
type: item
description: Business Intelligence (BI) projects are pivotal in transforming data into actionable insights for organizations. However, user engagement in these projects often remains a challenge, undermining their potential impact. This study explores the role of custom dashboards in enhancing user engagement within BI projects. By tailoring dashboards to user needs, the research highlights how personalized interfaces, intuitive designs, and relevant metrics contribute to better decision-making and increased interaction with BI tools. Employing a mixed-methods approach, the study analyzes feedback from BI users and evaluates the impact of customized dashboards on engagement metrics. Results indicate that user-centric designs significantly improve engagement, offering practical guidelines for organizations aiming to optimize their BI systems.
subject: Articles
  topic: section
creator: Er. Raghav Agarwal
rights: Copyright (c) 2026
date: 2025-07-06
publication date: 2025-07-06
starting page: (39
ending page: 51)

Advanced Data Modeling for Multi-Tenant Business Environments
type: item
description: In multi-tenant business environments, data modeling is a critical aspect to ensure efficient resource sharing, scalability, and secure isolation of tenants. This study investigates advanced data modeling techniques tailored for multi-tenant architectures, including shared and hybrid database models. The research highlights innovative solutions for scalability, tenant isolation, and dynamic schema management, leveraging technologies such as sharding, partitioning, and metadata-driven modeling. Results indicate that hybrid models outperform traditional approaches in adaptability and cost-efficiency. This paper concludes with practical recommendations for implementing robust data models in multi-tenant environments while addressing inherent limitations.
subject: Articles
  topic: section
subject: Multi-Tenant Architecture, Data Modeling, Scalability, Tenant Isolation, Hybrid Database Model, Dynamic Schema, Sharding, Metadata-Driven Design
  topic: keywords
creator: Dr Munish Kumar
rights: Copyright (c) 2026
date: 2025-07-01
publication date: 2025-07-01
starting page: (26
ending page: 38)

Cross-Platform Data Integration with SAP BW and Modern Cloud Tools
type: item
description: The proliferation of data across organizations has necessitated robust integration methods to harmonize disparate data sources. SAP Business Warehouse (SAP BW) has been a cornerstone for enterprise data management. However, with the rise of cloud-based platforms, integrating SAP BW with modern cloud tools has become essential for real-time analytics, scalability, and operational efficiency. This study explores methodologies for cross-platform data integration, evaluates modern tools, and proposes a framework for seamless integration between SAP BW and cloud-based solutions such as Snowflake, Azure Synapse, and Google BigQuery. The results demonstrate enhanced data synchronization, cost efficiency, and performance improvements in analytics workflows.
subject: Articles
  topic: section
subject: SAP BW, Cloud Integration, Data Analytics, Modern Cloud Tools, Real-Time Data, Data Synchronization, Enterprise Data Management
  topic: keywords
creator: Dr. A.H Khan
rights: Copyright (c) 2026
date: 2025-07-01
publication date: 2025-07-01
starting page: (14
ending page: 25)