Guardado en:
| Autor principal: | |
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| Formato: | Recurso digital |
| Lenguaje: | inglés |
| Publicado: |
Zenodo
2026
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| Acceso en línea: | https://doi.org/10.5281/zenodo.19282720 |
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Tabla de Contenidos:
- <p><span>The rapid evolution of data-driven technologies has transformed the way organizations operate, compete, and innovate in the modern digital era. The exponential growth of data generated from diverse sources such as enterprise systems, social media, IoT devices, and cloud platforms has necessitated the development of advanced techniques for data storage, processing, and analysis. In this context, <em><span>Data Warehousing and Mining Techniques for Business Intelligence</span></em> has been conceptualized as a comprehensive academic resource that bridges foundational concepts with contemporary advancements in data management and analytics.</span></p> <p><span>This multi-author volume brings together the collective expertise of academicians, researchers, and industry practitioners to provide a holistic understanding of data warehousing, data mining, and business intelligence. The collaborative nature of this work reflects the interdisciplinary essence of the subject, integrating perspectives from computer science, information systems, statistics, and artificial intelligence. Each contributor has carefully crafted their respective chapters to ensure clarity, depth, and academic rigor, making this book suitable for undergraduate and postgraduate students, as well as professionals seeking to enhance their knowledge in this domain.</span></p> <p><span>The primary objective of this book is to present a structured and systematic exploration of key concepts, methodologies, and applications related to data warehousing and data mining. The content begins with foundational topics such as data, information, and knowledge, followed by an introduction to business intelligence and data warehousing architectures. Subsequent chapters delve into essential components such as ETL processes, data integration, and data quality management. Advanced topics including OLAP operations, multidimensional data models, and query optimization techniques are also discussed in detail.</span></p> <p><span>A significant portion of the book is dedicated to data mining techniques, covering classification, clustering, association rule mining, regression analysis, and outlier detection. These topics are presented with both theoretical foundations and practical applications, enabling readers to understand their relevance in real-world scenarios. Furthermore, the book explores emerging areas such as text mining, web mining, big data analytics, and predictive analytics, highlighting the evolving landscape of business intelligence.</span></p> <p><span>The inclusion of business intelligence tools and case studies further enhances the practical orientation of this book. By examining real-world applications, readers can gain insights into how organizations leverage data to drive strategic decision-making and innovation. The case studies presented in this book illustrate the transformative impact of business intelligence across various sectors, including retail, healthcare, finance, and governance.</span></p> <p><span>This book has been designed with a pedagogical approach, ensuring a logical progression of topics and a coherent flow of concepts. Each chapter is structured to facilitate learning, with clear explanations, illustrative examples, and relevant applications. The contributors have made a concerted effort to maintain consistency in style and terminology, ensuring a seamless reading experience for the audience.</span></p> <p><span>We would like to express our sincere gratitude to all the contributing authors for their dedication, expertise, and commitment to this project. Their collaborative efforts have made this book a valuable academic resource. We also extend our appreciation to the reviewers and editors who have provided insightful feedback and helped improve the quality of the content.</span></p> <p><span>We hope that this book will serve as a useful reference for students, educators, researchers, and professionals in the field of data warehousing, data mining, and business intelligence. It is our expectation that this work will not only enhance understanding but also inspire further research and innovation in this rapidly evolving domain.</span></p>