Data Warehousing And Data Mining Question Bank With Answers A Comprehensive Handbook
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DATA WAREHOUSING AND DATA MINING QUESTION BANK WITH ANSWERS: A COMPREHENSIVE HANDBOOK
Author: S. R. Jena
language: en
Publisher: Independently Published by Amazon KDP
Release Date: 2025-03-19
In the ever-evolving landscape of data management and analytics, the fields of Data Warehousing and Data Mining have become crucial for organizations and researchers alike. Data warehousing facilitates efficient storage, retrieval, and analysis of vast amounts of structured data, while data mining uncovers hidden patterns, relationships, and insights that drive decision-making. With the growing importance of big data, artificial intelligence, and business intelligence solutions, mastering these concepts is essential for students, professionals, and academicians. Recognizing the need for a structured and comprehensive resource, we, the authors, have meticulously designed this book, "Data Warehousing and Data Mining Question Bank with Answers: A Comprehensive Handbook", to serve as a one-stop solution for learners preparing for academic examinations, competitive tests, and professional certifications. This book aims to provide well-organized, concept-driven, and exam-oriented content in the form of a question bank, making it a valuable asset for anyone looking to gain expertise in these domains. Why This Book? • Concise yet Comprehensive – Covers theoretical concepts, practical applications, and industry trends in a question-answer format. • Exam-Oriented Approach – Ideal for university exams, technical interviews, and competitive exams. • Industry-Relevant Examples – Demonstrates real-world applications of data warehousing and data mining techniques. • Simplified Language – Ensures clarity, making it accessible for both beginners and advanced learners. Who Should Read This Book? This book is tailored for: • Undergraduate and Postgraduate Students in Computer Science, Information Technology, and Data Analytics. • Competitive Exam Aspirants preparing for GATE, UGC-NET, and other certification exams. • Data Analysts, Data Engineers, and IT Professionals looking to strengthen their understanding of data warehousing and mining techniques. • Academicians and Researchers exploring emerging trends and practical applications in the field. Acknowledgements: We, the authors, are deeply thankful to our Director, NIET, NIMS University, Prof. Dr. Ashutosh Tripathi, Head of the Department, Prof. Dr. Vineet Mehan, and all faculty members for their constant encouragement and support. We also extend our sincere gratitude to the management of NIMS University for their invaluable assistance.
Intelligent Computing and Innovation on Data Science
This book covers both basic and high-level concepts relating to the intelligent computing paradigm and data sciences in the context of distributed computing, big data, data sciences, high-performance computing and Internet of Things. It is becoming increasingly important to develop adaptive, intelligent computing-centric, energy-aware, secure and privacy-aware systems in high-performance computing and IoT applications. In this context, the book serves as a useful guide for industry practitioners, and also offers beginners a comprehensive introduction to basic and advanced areas of intelligent computing. Further, it provides a platform for researchers, engineers, academics and industrial professionals around the globe to showcase their recent research concerning recent trends. Presenting novel ideas and stimulating interesting discussions, the book appeals to researchers and practitioners working in the field of information technology and computer science.
Data Mining and Knowledge Discovery for Geoscientists
Currently there are major challenges in data mining applications in the geosciences. This is due primarily to the fact that there is a wealth of available mining data amid an absence of the knowledge and expertise necessary to analyze and accurately interpret the same data. Most geoscientists have no practical knowledge or experience using data mining techniques. For the few that do, they typically lack expertise in using data mining software and in selecting the most appropriate algorithms for a given application. This leads to a paradoxical scenario of "rich data but poor knowledge". The true solution is to apply data mining techniques in geosciences databases and to modify these techniques for practical applications. Authored by a global thought leader in data mining, Data Mining and Knowledge Discovery for Geoscientists addresses these challenges by summarizing the latest developments in geosciences data mining and arming scientists with the ability to apply key concepts to effectively analyze and interpret vast amounts of critical information. - Focuses on 22 of data mining's most practical algorithms and popular application samples - Features 36 case studies and end-of-chapter exercises unique to the geosciences to underscore key data mining applications - Presents a practical and integrated system of data mining and knowledge discovery for geoscientists - Rigorous yet broadly accessible to geoscientists, engineers, researchers and programmers in data mining - Introduces widely used algorithms, their basic principles and conditions of applications, diverse case studies, and suggests algorithms that may be suitable for specific applications