Big Data Quantification For Complex Decision Making


Big Data Quantification For Complex Decision Making
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Big Data Quantification For Complex Decision Making


Big Data Quantification For Complex Decision Making
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Author : Zhang, Chao
language : en
Publisher: IGI Global
Release Date : 2024-04-16

Big Data Quantification For Complex Decision Making written by Zhang, Chao and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-16 with Business & Economics categories.


Many professionals are facing a monumental challenge: navigating the intricate landscape of information to make impactful choices. The sheer volume and complexity of big data have ushered in a shift, demanding innovative methodologies and frameworks. Big Data Quantification for Complex Decision-Making tackles this challenge head-on, offering a comprehensive exploration of the tools necessary to distill valuable insights from datasets. This book serves as a tool for professionals, researchers, and students, empowering them to not only comprehend the significance of big data in decision-making but also to translate this understanding into real-world decision making. The central objective of the book is to examine the relationship between big data and decision-making. It strives to address multiple objectives, including understanding the intricacies of big data in decision-making, navigating methodological nuances, managing uncertainty adeptly, and bridging theoretical foundations with real-world applications. The book's core aspiration is to provide readers with a comprehensive toolbox, seamlessly integrating theoretical frameworks, practical applications, and forward-thinking perspectives. This equips readers with the means to effectively navigate the data-rich landscape of modern decision-making, fostering a heightened comprehension of strategic big data utilization. Tailored for a diverse audience, this book caters to researchers and academics in data science, decision science, machine learning, artificial intelligence, and related domains.



Real World Data Mining


Real World Data Mining
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Author : Dursun Delen
language : en
Publisher: FT Press
Release Date : 2014-12-16

Real World Data Mining written by Dursun Delen and has been published by FT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-12-16 with Business & Economics categories.


Use the latest data mining best practices to enable timely, actionable, evidence-based decision making throughout your organization! Real-World Data Mining demystifies current best practices, showing how to use data mining to uncover hidden patterns and correlations, and leverage these to improve all aspects of business performance. Drawing on extensive experience as a researcher, practitioner, and instructor, Dr. Dursun Delen delivers an optimal balance of concepts, techniques and applications. Without compromising either simplicity or clarity, he provides enough technical depth to help readers truly understand how data mining technologies work. Coverage includes: processes, methods, techniques, tools, and metrics; the role and management of data; text and web mining; sentiment analysis; and Big Data integration. Throughout, Delen's conceptual coverage is complemented with application case studies (examples of both successes and failures), as well as simple, hands-on tutorials. Real-World Data Mining will be valuable to professionals on analytics teams; professionals seeking certification in the field; and undergraduate or graduate students in any analytics program: concentrations, certificate-based, or degree-based.



How Will Big Data Change The Way Managers Make Decisions Artificial Intelligence And Its Impact On Managerial Decision Making


How Will Big Data Change The Way Managers Make Decisions Artificial Intelligence And Its Impact On Managerial Decision Making
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Author : Carolin Nothof
language : en
Publisher: GRIN Verlag
Release Date : 2019-03-22

How Will Big Data Change The Way Managers Make Decisions Artificial Intelligence And Its Impact On Managerial Decision Making written by Carolin Nothof and has been published by GRIN Verlag this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-22 with Business & Economics categories.


Today’s most precious raw material is not gold, but Big Data: Each one of us generates a huge amount of information every single day, rendering thus both ourselves and our choices transparent. But in addition to that, Big Data helps companies to improve their decision-making. Since managers have to address highly complex issues in an ever more complicated world, they cannot do without Big Data and Artificial Intelligence, as Carolin Nothof explains. By taking into account various external factors, their algorithms predict right entrepreneurial choices. These choices can be made in areas such as retail, Human Resources, the Internet of Things, and marketing. Nothof’s publication is not only rich in theoretical explanations, but also gives examples of the practical use of Big Data in various industries. Machines are a man’s best co-workers. In this book: - Big Data; - decision-making; - AI; - Behavorial Economics; - Machine Learning; - algorithms



Big Data Analytics Using Multiple Criteria Decision Making Models


Big Data Analytics Using Multiple Criteria Decision Making Models
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Author : Ramakrishnan Ramanathan
language : en
Publisher: CRC Press
Release Date : 2017-07-12

Big Data Analytics Using Multiple Criteria Decision Making Models written by Ramakrishnan Ramanathan and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-12 with Computers categories.


Multiple Criteria Decision Making (MCDM) is a subfield of Operations Research, dealing with decision making problems. A decision-making problem is characterized by the need to choose one or a few among a number of alternatives. The field of MCDM assumes special importance in this era of Big Data and Business Analytics. In this volume, the focus will be on modelling-based tools for Business Analytics (BA), with exclusive focus on the sub-field of MCDM within the domain of operations research. The book will include an Introduction to Big Data and Business Analytics, and challenges and opportunities for developing MCDM models in the era of Big Data.



Management Decision Making Big Data And Analytics


Management Decision Making Big Data And Analytics
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Author : Simone Gressel
language : en
Publisher: SAGE
Release Date : 2020-10-12

Management Decision Making Big Data And Analytics written by Simone Gressel and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-12 with Business & Economics categories.


Accessible and concise, this exciting new textbook examines data analytics from a managerial and organizational perspective and looks at how they can help managers become more effective decision-makers. The book successfully combines theory with practical application, featuring case studies, examples and a ‘critical incidents’ feature that make these topics engaging and relevant for students of business and management. The book features chapters on cutting-edge topics, including: • Big data • Analytics • Managing emerging technologies and decision-making • Managing the ethics, security, privacy and legal aspects of data-driven decision-making The book is accompanied by an Instructor’s Manual, PowerPoint slides and access to journal articles. Suitable for management students studying business analytics and decision-making at undergraduate, postgraduate and MBA levels.



Big Data And Business Analytics


Big Data And Business Analytics
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Author : Jay Liebowitz
language : en
Publisher: CRC Press
Release Date : 2016-04-19

Big Data And Business Analytics written by Jay Liebowitz and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-04-19 with Business & Economics categories.


"The chapters in this volume offer useful case studies, technical roadmaps, lessons learned, and a few prescriptions todo this, avoid that.'"-From the Foreword by Joe LaCugna, Ph.D., Enterprise Analytics and Business Intelligence, Starbucks Coffee CompanyWith the growing barrage of "big data," it becomes vitally important for organizations to mak



Decision Analytics Applications In Industry


Decision Analytics Applications In Industry
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Author : P. K. Kapur
language : en
Publisher: Springer Nature
Release Date : 2020-05-27

Decision Analytics Applications In Industry written by P. K. Kapur and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-27 with Business & Economics categories.


This book presents a range of qualitative and quantitative analyses in areas such as cybersecurity, sustainability, multivariate analysis, customer satisfaction, parametric programming, software reliability growth modeling, and blockchain technology, to name but a few. It also highlights integrated methods and practices in the areas of machine learning and genetic algorithms. After discussing applications in supply chains and logistics, cloud computing, six sigma, production management, big data analysis, satellite imaging, game theory, biometric systems, quality, and system performance, the book examines the latest developments and breakthroughs in the field of science and technology, and provides novel problem-solving methods. The themes discussed in the book link contributions by researchers and practitioners from different branches of engineering and management, and hailing from around the globe. These contributions provide scholars with a platform to derive maximum utility in the area of analytics by subscribing to the idea of managing business through system sciences, operations, and management. Managers and decision-makers can learn a great deal from the respective chapters, which will help them devise their own business strategies and find real-world solutions to complex industrial problems.



Application Of Decision Science In Business And Management


Application Of Decision Science In Business And Management
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Author : Fausto Pedro García Márquez
language : en
Publisher: BoD – Books on Demand
Release Date : 2020-03-04

Application Of Decision Science In Business And Management written by Fausto Pedro García Márquez and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-04 with Business & Economics categories.


Application of Decision Science in Business and Management is a book where each chapter has been contributed by a different author(s). The chapters introduce and demonstrate a decision-making theory to practice case studies. It demonstrates key results for each sector with diverse real-world case studies. Theory is accompanied by relevant analysis techniques, with a progressive approach building from simple theory to complex and dynamic decisions with multiple data points, including big data, lot of data, etc. Computational techniques, dynamic analysis, probabilistic methods, and mathematical optimization techniques are expertly blended to support analysis of multi-criteria decision-making problems with defined constraints and requirements. The book provides an interface between the main disciplines of engineering/technology and the organizational, administrative, and planning abilities of decision making. It is complementary to other sub-disciplines such as economics, finance, marketing, decision and risk analysis, etc.



Decision Making


Decision Making
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Author : Fausto Pedro García Márquez
language : en
Publisher: BoD – Books on Demand
Release Date : 2018-09-19

Decision Making written by Fausto Pedro García Márquez and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-19 with Psychology categories.


Decision Making is a book where each chapter has been contributed to by a different author(s). The book synthesizes the analytical principles with business practice of Decision Making. Specifically, the book provides an interface between the main disciplines of engineering/technology and the organizational, administrative, and planning abilities of decision making. It is complementary to other sub-disciplines such as economics, finance, marketing, decision and risk analysis, etc. The chapters introduce and demonstrate decision making theory in practical case studies. It demonstrates key results for each sector with diverse real-world case studies. The theory is accompanied by relevant analysis techniques, with a progressional approach building from simple theory to complex and dynamic decisions with multiple data points, including big data, etc. Computational techniques, dynamic analysis, probabilistic methods, and mathematical optimization techniques are expertly blended to support analysis of multi-criteria decision-making problems with defined constraints and requirements.



Big Data In Complex Systems


Big Data In Complex Systems
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Author : Aboul Ella Hassanien
language : en
Publisher: Springer
Release Date : 2015-01-02

Big Data In Complex Systems written by Aboul Ella Hassanien and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-01-02 with Technology & Engineering categories.


This volume provides challenges and Opportunities with updated, in-depth material on the application of Big data to complex systems in order to find solutions for the challenges and problems facing big data sets applications. Much data today is not natively in structured format; for example, tweets and blogs are weakly structured pieces of text, while images and video are structured for storage and display, but not for semantic content and search. Therefore transforming such content into a structured format for later analysis is a major challenge. Data analysis, organization, retrieval, and modeling are other foundational challenges treated in this book. The material of this book will be useful for researchers and practitioners in the field of big data as well as advanced undergraduate and graduate students. Each of the 17 chapters in the book opens with a chapter abstract and key terms list. The chapters are organized along the lines of problem description, related works, and analysis of the results and comparisons are provided whenever feasible.