Search results for "Operations Management and Data Analytics Modelling"
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Dimensions of Intelligent Analytics for Smart Digital Health Solutions
This title demystifies artificial intelligence (AI) and analytics, upskilling individuals (healthcare professionals, hospital managers, consultants, researchers, students, and the population at large) around analytics and AI as it applies to healthcare.This book shows how the tools, techniques, technologies, and tactics around analytics and AI can be best leveraged and utilised to realise a healthcare value proposition of better quality, better access and high value for everyone every day, everywhere. The book presents a triumvirate approach including technical, business and medical aspects of data and analytics and by so doing takes a responsible approach to this key area.This work serves to introduce the critical issues in AI and analytics for healthcare to students, practitioners, and researchers.
R 9,343.62
Data Science for Modeling Managerial and Socioeconomic Problems
This book leverages statistical analysis, data mining, and machine learning techniques to address managerial and socioeconomic problems. With the advent of modern technologies, massive amount of data, especially big data, proliferate from business transactions and users. Consequently, there is an ever-increasing demand for analyzing the data and gaining valuable insights. This book comprises 15 chapters: the first ten chapters cover methods from Statistics and Econometrics, while the next five chapters delve into selected Machine Learning techniques. By bringing together the expertise of eminent researchers from reputed universities worldwide, this volume provides a cohesive guide to understanding and applying data science methodologies to real-world problems.The book assumes basic knowledge of probability and statistics. Each chapter presents a blend of theoretical insights and practical case studies, ensuring that readers not only learn the techniques but also see their relevance and implementation in real-world scenarios. The chapters not only cover the theoretical underpinnings in a student-friendly language but also provide step-by-step guides for implementation using various software tools such as R, Python, Matlab, and SPSS. This is to instill confidence in the reader to apply such techniques to real-life problems. The book is designed for a broad spectrum of readership - empirical economists, business analysts, and post-graduate students aiming to learn and practice data science. Moreover, the book is designed in such a way that it can be used as a practical reference book for one semester-long Data Science course.
R 8,748.36
Management, Organization, and Childbirth
Management, Organization and Childbirth: Towards a New Model for the Birth Path explores the complex topic of the birth path with a multidisciplinary magnifying glass on the paradigms, languages, and tools critical to the organization, management, and clinical science.The work consists of five chapters. The first chapter provides a multidimensional analysis of childbirth. The second chapter presents an organizational analysis that moves in unison with different models of health. The third chapter studies the birth path in organizational and cynical terms by describing it in its core processes. The fourth chapter proposes a study conducted in the Italian context, which identifies some useful determinants for redesigning the birth path. The fifth chapter formulates a proposal for redesigning the birth path based on a new health paradigm.The proposed model offers useful insights for multiple categories of readers. To students of medicine and higher education tracks in healthcare management, it can offer opportunities to raise awareness not only regarding multi-professional practice but also regarding confrontation with complementary disciplines. To practitioners and policy makers, it can provide useful stimuli to promote rational and informed decisions around the childbirth. To researchers studying the health context within different disciplinary domains, the model can offer unexplored research spaces within the new business complex system.
R 7,551.62
Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning
By applying data analytics techniques and machine learning algorithms to predict disease, medical practitioners can more accurately diagnose and treat patients. However, researchers face problems in identifying suitable algorithms for pre-processing, transformations, and the integration of clinical data in a single module, as well as seeking different ways to build and evaluate models. The Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning is a pivotal reference source that explores the application of algorithms to making disease predictions through the identification of symptoms and information retrieval from images such as MRIs, ECGs, EEGs, etc. Highlighting a wide range of topics including clinical decision support systems, biomedical image analysis, and prediction models, this book is ideally designed for clinicians, physicians, programmers, computer engineers, IT specialists, data analysts, hospital administrators, researchers, academicians, and graduate and post-graduate students.
R 18,626.91
Strategic System Assurance and Business Analytics
This book systematically examines and quantifies industrial problems by assessing the complexity and safety of large systems. It includes chapters on system performance management, software reliability assessment, testing, quality management, analysis using soft computing techniques, management analytics, and business analytics, with a clear focus on exploring real-world business issues. Through contributions from researchers working in the area of performance, management, and business analytics, it explores the development of new methods and approaches to improve business by gaining knowledge from bulk data. With system performance analytics, companies are now able to drive performance and provide actionable insights for each level and for every role using key indicators, generate mobile-enabled scorecards, time series-based analysis using charts, and dashboards.In the current dynamic environment, a viable tool known as multi-criteria decision analysis (MCDA) is increasingly being adopted to deal with complex business decisions. MCDA is an important decision support tool for analyzing goals and providing optimal solutions and alternatives. It comprises several distinct techniques, which are implemented by specialized decision-making packages. This book addresses a number of important MCDA methods, such as DEMATEL, TOPSIS, AHP, MAUT, and Intuitionistic Fuzzy MCDM, which make it possible to derive maximum utility in the area of analytics. As such, it is a valuable resource for researchers and academicians, as well as practitioners and business experts.
R 6,840.96
Supply Chain Analytics
This reference text resolves problems of physical products, information, and financial transactions by using mathematical and statistical tools to provide robust and validated solutions to real-world problems of the supply chain. It further discusses important concepts such as supply chain segmentation, constrained and unconstrained optimization, time series analysis, double exponential smoothing, and demand probability distributions.This book:Discusses inventory replenishment models including economic order quantity (EOQ), and economic production quantity (EPQ).Explains different mathematical and analytical models applied frequently depending on the supply chain problem. Covers important topics such as linear, integer, and mixed integer programming. Demonstrates how the variation in demand with time follows certain patterns which can be seasonal, cyclical, trend, and random. Highlights forecasting methods, namely, cumulative, naive, moving averages, and exponential smoothing.It is primarily written for senior undergraduate, graduate students, and academic researchers in the fields of manufacturing engineering, industrial engineering, supply chain management, mechanical engineering, and production engineering.
R 9,335.54
Big Data and Business Analytics
"The chapters in this volume offer useful case studies, technical roadmaps, lessons learned, and a few prescriptions to ‘do 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 make sense of this data and information in a timely and effective way. That’s where analytics come into play. Research shows that organizations that use business analytics to guide their decision making are more productive and experience higher returns on equity. Big Data and Business Analytics helps you quickly grasp the trends and techniques of big data and business analytics to make your organization more competitive.Packed with case studies, this book assembles insights from some of the leading experts and organizations worldwide. Spanning industry, government, not-for-profit organizations, and academia, they share valuable perspectives on big data domains such as cybersecurity, marketing, emergency management, healthcare, finance, and transportation.Understand the trends, potential, and challenges associated with big data and business analyticsGet an overview of machine learning, advanced statistical techniques, and other predictive analytics that can help you solve big data issuesLearn from VPs of Big Data/Insights & Analytics via case studies of Fortune 100 companies, government agencies, universities, and not-for-profitsBig data problems are complex. This book shows you how to go from being data-rich to insight-rich, improving your decision making and creating competitive advantage.Author Jay Liebowitz recently had an article published in The World Financial Review. www.worldfinancialreview.com/?p=1904
R 4,460.32
Leveraging Digital Mixed Model Value Stream Maps
This guide serves as a roadmap for integrating digital value stream mapping (eVSM) into your Lean deployment to improve production processes. Lean, with its mapping component, value stream mapping (VSM), has endured as a process improvement methodology for more than 25 years. VSM is a metrics-based analytical mapping method with simple calculations for Lean metrics such as Takt Time, VA, NVA, Capacity, OEE, etc., however:The calculations and charting associated with value stream maps can be tediousIt’s hard to keep updating the calculations for “what-if” studiesManual calculations can be prone to error.Digital VSM was introduced as a complement to the initial paper-and-pencil map. The eVSM software has been used to create Digital VSMs for many years, but it has also encountered a couple of hurdles:Lean application has expanded and is applied to increasingly complex value streamsProduct variants (mixed models) have proliferated so that many variants are now made with shared resourcesCreating and analyzing mixed model value stream maps with only paper and pencil, however, is practically unfeasible. The eVSM program has been revamped for the mixed model production setting and is now accessible as eVSM Mix for creating and analyzing Digital Mix VSMs. The eVSM Mix software reintroduces VSM as a fundamental tool for lean practitioners working in mixed-model situations. This guide illustrates Digital Mix VSMs created with the eVSM Mix software, shows how to use them analytically for “what-if” studies, and highlights best practices for deployment.
R 3,107.45
Responsible Analytics and Data Mining in Education
Winner of two Outstanding Book Awards from the Association of Educational Communications and Technology (Culture, Learning, & Technology and Systems Thinking & Change divisions)! Rapid advancements in our ability to collect, process, and analyze massive amounts of data along with the widespread use of online and blended learning platforms have enabled educators at all levels to gain new insights into how people learn. Responsible Analytics and Data Mining in Education addresses the thoughtful and purposeful navigation, evaluation, and implementation of these emerging forms of educational data analysis. Chapter authors from around the world explore how data analytics can be used to improve course and program quality; how the data and its interpretations may inadvertently impact students, faculty, and institutions; the quality and reliability of data, as well as the accuracy of data-based decisions; ethical implications surrounding the collection, distribution, and use of student-generated data; and more. This volume unpacks and explores this complex issue through a systematic framework whose dimensions address the issues that must be considered before implementation of a new initiative or program.
R 9,322.60
Data Analytics and Management in Data Intensive Domains
This book constitutes the refereed proceedings of the 19th International Conference on Data Analytics and Management in Data Intensive Domains, DAMDID/RCDL 2017, held in Moscow, Russia, in October 2017.The 16 revised full papers presented together with three invited papers were carefully reviewed and selected from 75 submissions. The papers are organized in the following topical sections: data analytics; next generation genomic sequencing: challenges and solutions; novel approaches to analyzing and classifying of various astronomical entities and events; ontology population in data intensive domains; heterogeneous data integration issues; data curation and data provenance support; and temporal summaries generation.
R 2,173.59
Intelligent Data Analytics, IoT, and Blockchain
This book focuses on data analytics with machine learning using IoT and blockchain technology. Integrating these three fields by examining their interconnections, Intelligent Data Analytics, IoT, and Blockchain examines the opportunities and challenges of developing systems and applications exploiting these technologies. Written primarily for researchers who are working in this multi-disciplinary field, the book also benefits industry experts and technology executives who want to develop their organizations' decision-making capabilities. Highlights of the book include:Using image processing with machine learning techniquesA deep learning approach for facial recognitionA scalable system architecture for smart cities based on cognitive IoTSource authentication of videos shared on social mediaSurvey of blockchain in healthcareAccident prediction by vehicle trackingBig Data analytics in disaster managementAn exploration of blockchain technology from the perspective of applicability, limitations, and opportunitiesThe book presents novel ideas and insights on different aspects of data analytics, blockchain technology, and IoT. It views these technologies as interdisciplinary fields about processes and systems that extract knowledge and insights from data. Focusing on recent advancements, the book offers a variety of solutions to real-life challenges with an emphasis on security.
R 9,330.54
Modeling and Management of Fuzzy Semantic RDF Data
This book systemically presents the latest research findings in fuzzy RDF data modeling and management. Fuzziness widely exist in many data and knowledge intensive applications. With the increasing amount of metadata available, efficient and scalable management of massive semantic data with uncertainty is of crucial importance. This book goes to great depth concerning the fast-growing topic of technologies and approaches of modeling and managing fuzzy metadata with Resource Description Framework (RDF) format. Its major topics include representation of fuzzy RDF data, fuzzy RDF graph matching, query of fuzzy RDF data, and persistence of fuzzy RDF data in diverse databases. The objective of the book is to provide the state-of-the-art information to researchers, practitioners, and postgraduates students who work on the area of big data intelligence and at the same time serve as the uncertain data and knowledge engineering professional as a valuable real-world reference.
R 3,754.79
Analytics: Data Science, Data Analysis and Predictive Analytics for Business
SO MANY PEOPLE DREAM OF BECOMING THEIR OWN BOSS OR SUCCEEDING IN THEIR CHOSEN PROFESSION, AND WITH THE RESOURCES AVAILABLE TODAY, MORE ENTREPRENEURS AND PROFESSIONALS ARE ACHIEVING GREAT SUCCESS! HOWEVER, SUCCESS SHOULD BE DEFINED FOR THE LONG TERM, AND AS OPPORTUNITIES START TO GROW, SO DOES THE COMPETITION. Getting your business up and running or starting on your career path is one thing, but have a sustainable business or career is completely another. Many people make the mistake of making plans but having no follow-through. This is where analytics comes in. Don't you wish to have the power to know what your target consumers are thinking? Won't you want to have a preview of what future trends to expect in the market you are in? Well, this book is just the one you need. This book will teach you, in simple and easy-to-understand terms, how to take advantage of data from your daily operations and make such data a powerful tool that can influence how well your business does over time.
R 1,985.51
Handbook of Healthcare Operations Management
From the Preface:Collectively, the chapters in this book address application domains including inpatient and outpatient services, public health networks, supply chain management, and resource constrained settings in developing countries. Many of the chapters provide specific examples or case studies illustrating the applications of operations research methods across the globe, including Africa, Australia, Belgium, Canada, the United Kingdom, and the United States.Chapters 1-4 review operations research methods that are most commonly applied to health care operations management including: queuing, simulation, and mathematical programming. Chapters 5-7 address challenges related to inpatient services in hospitals such as surgery, intensive care units, and hospital wards. Chapters 8-10 cover outpatient services, the fastest growing part of many health systems, and describe operations research models for primary and specialty care services, and how to plan for patient no-shows. Chapters 12 – 16 cover topics related to the broader integration of health services in the context of public health, including optimizing the location of emergency vehicles, planning for mass vaccination events, and the coordination among different parts of a health system. Chapters 17-18 address supply chain management within hospitals, with a focus on pharmaceutical supply management, and the challenges of managing inventory for nursing units. Finally, Chapters 19-20 provide examples of important and emerging research in the realm of humanitarian logistics.
R 8,633.43
Operations Excellence Management System (OEMS)
Written for users, this book provides a structured approach with processes for implementing OEMS based on the learnings and experiences from companies who have implemented OEMS. The book leverages the knowledge of experienced OEMS personnel to provide a compelling sense of direction for organizations in the implementation of OEMS. The book includes sample templates and tools where necessary to ensure successful implementation and sustainment. The content of this book provides a testing methodology for implementing an OEMS across any organization while avoiding the pitfalls others have encountered along the way. The book: Provides a simple and easy process to follow for implementing an OEMS Offers organizations an opportunity to avoid the implementation errors of early adopters and provides them with the ability of learning from the experiences of others Equipped with tools and processes to make implementation and sustainment very effective, thereby avoiding false starts Designed to improve HSE, business reliability, efficiency, effectiveness, and performance on an ongoing basis Presents a simple pathway for helping organizations across all industries including those that operate within the various segments of the Oil and Gas business, to become more operationally disciplined in the way we do business and operate our assets in a high-risk operating environment
R 2,946.50
Data Analytics for Business
Interest in applying analytics, machine learning, and artificial intelligence to sales and marketing has grown dramatically, with no signs of slowing down. This book provides essential guidance to apply advanced analytics and data mining techniques to real-world business applications. The foundation of this text is the author’s 20-plus years of developing and delivering big data and artificial intelligence solutions across multiple industries: financial services, pharmaceuticals, consumer packaged goods, media, and retail. He provides guidelines and summarized cases for those studying or working in the fields of data science, data engineering, and business analytics. The book also offers a distinctive style: a series of essays, each of which summarizes a critical lesson or provides a step-by-step business process, with specific examples of successes and failures.Sales and marketing executives, project managers, business and engineering professionals, and graduate students will find this clear and comprehensive book the ideal companion when navigating the complex world of big data analytics.
R 2,465.36
BI Without the BS
Stop asking stakeholders what they want—start uncovering what they truly need. Many business intelligence (BI) projects fail—not because of bad technology, but because of poor requirements. Stakeholders often don't know what they need, and project teams assume too much. The result? Costly delays, endless rework, and disappointing solutions. BI Without the BS offers a practical, proven framework to turn that around. Drawing from decades of experience in telecom, finance, retail, and global organizations, Eva Polini and Karina Ghozali guide readers through the real-world challenges of gathering BI requirements—clearly, confidently, and effectively. Whether you're a business analyst, data visualization specialist, project manager, or BI leader, this book will help you: Understand the foundations of business analysis and requirement engineering—then how to apply them to BI, Go beyond surface-level requests to uncover core business needs, Deliver BI assets that are adopted, trusted, and aligned with strategic goals, Build stronger relationships with stakeholders while reducing friction and rework. Through relatable actionable steps, the authors empower analysts to become proactive change agents who design solutions that genuinely support decision-making. Clear, accessible, and rich with insight, this book is your roadmap to building BI solutions that work the first time—on time and on budget. If you're ready to go beyond gathering requirements—and start driving results—this book is for you.
R 1,041.47
Das Management-Handbuch für Chief Data Officer
Es lässt sich nicht leugnen, dass das 21. Jahrhundert datengesteuert ist, wobei viele digitale Branchen auf die sorgfältige Erfassung und Analyse großer Mengen von Informationen angewiesen sind. Ein Chief Data Officer (CDO) in einem Unternehmen ist der Leiter dieses Prozesses, was diese Position oft zu einer Herausforderung macht. Das Chief Data Officer Management Handbook soll hier Abhilfe schaffen.In diesem Buch gibt der Autor Martin Treder Ratschläge, wie CDOs besser auf ihren Verantwortungsbereich vorbereitet werden können, wie sie einen nachhaltigeren Ansatz entwickeln und wie sie die typischen Fallstricke vermeiden können. Basierend auf den positiven und negativen Erfahrungen aktueller CDOs hilft das Chief Data Officer Management Handbook dabei, die ideale Struktur eines Datenbüros zu entwerfen, sie zu implementieren und die richtigen Leute an Bord zu holen.Wichtige Themen wie die Datenversorgungskette, die Datenstrategie und die Data Governance werden von Treder sorgfältig behandelt. Als CDO ist es wichtig, dass Sie Ihre Position mit Ihrem gesamten Team effektiv nutzen. Das Chief Data Officer Management Handbook ermöglicht es allen Mitarbeitern, die Verantwortung für die Zusammenarbeit im Bereich Daten zu übernehmen. Daten sind die Grundlage heutiger und zukünftiger technologischer Innovationen, und Sie könnten die Führungspersönlichkeit sein, die die nächste große Veränderung bewirkt.Was Sie lernen werden:Wichtige Elemente eines effektiven Datenmanagements anwendenSie erhalten einen umfassenden Überblick über alle Datenbereiche (die oft unabhängig voneinander verwaltet werden) Arbeiten Sie mit der Datenversorgungskette, von der Datenerfassung bis zur Datennutzung, einer Überprüfung aller relevanten Interessengruppen, der Datenstrategie und der DatenverwaltungFür wen ist dieses Buch gedacht?CDOs, Datenverantwortliche, Datenberater und alle Fachleute, die verstehen wollen, wie ein Datenbüro in einer Organisation funktioniert.
R 684.00
Hands-on Introduction to Big Data Analytics
This practical textbook offers a hands-on introduction to big data analytics, helping you to develop the skills required to hit the ground running as a data professional.It complements theoretical foundations with an emphasis on the application of big data analytics, illustrated by real-life examples and datasets. Containing comprehensive coverage of all the key topics in this area, this book uses open-source technologies and examples in Python and Apache Spark.Learning features include: - Ethics by Design encourages you to consider data ethics at every stage. - Industry Insights facilitate a deeper understanding of the link between what you are studying and how it is applied in industry. - Datasets, questions, and exercises give you the opportunity to apply your learning.Dr Funmi Obembe is the Head of Technology at the Faculty of Arts, Science and Technology, University of Northampton.Dr Ofer Engel is a Data Scientist at the University of Groningen.
R 5,797.00
Long View of Research and Practice in Operations Research and Management Science
From the Foreword by Marshall Fisher, The Wharton School, University of Pennsylvania:As generation of academics and practitioners follows generation, it is worthwhile tocompile long views of the research and practice in the past to shed light on researchand practice going forward. This collection of peer-reviewed articles is intended toprovide such a long view.This book contains a collection of chapters written by leading scholars/practitionerswho have continued their efforts in developing and/or implementing innovativeOR/MS tools for solving real world problems. In this book, the contributors sharetheir perspectives about the past, present and future of OR/MS theoretical development, solution tools, modeling approaches, and applications. Specifically, this book collects chapters that offer insights about the following topics:• Survey articles taking a long view over the past two or more decades to arriveat the present state of the art while outlining ideas for future research. Surveysfocus on use of a particular OR/MS approach, e.g., mathematical programming(LP, MILP, etc.) and solution methods for particular family of application, e.g.,distribution system design, distribution planning system, health care.• Autobiographical or biographical accounts of how particular inventions (e.g.,Structured Modeling) were made. These could include personal experiences inearly development of OR/MS and an overview of what has happened since.• Development of OR/MS mathematical tools (e.g., stochastic programming, optimizationtheory).• Development of OR/MS in a particular industry sector such as global supplychain management.• Modeling systems for OR/MS and their development over time as well as speculationon future development (e.g., LINDO, LINGO, and What’sBest!)• New applications of OR/MS models (e.g., happiness)The target audience of this book is young researchers, graduate/advanced undergraduatestudents from OR/MS and related fields like computer science, engineering,and management as well as practitioners who want to understand how OR/MSmodeling came about over the past few decades and what research topics or modelingapproaches they could pursue in research or application.
R 3,041.00
Data Analytics in Football
Data Analytics in Football provides students, researchers and coaches with a firm grounding in the principles of modern performance analysis. It offers an insight into the use of positional data, exploring how it can be collected, modelled, analysed and interpreted. Introducing cutting-edge methods, the book challenges long-held assumptions and encourages a new way of thinking about football analysis. The book seeks to define the role of positional data in football match analysis by exploring topics such as: • What is positional data analysis and how did it emerge from conventional match analysis? • How can positional data be collected, and which technologies can be used? • What Key Performance Indicators based on positional data should be used? • How can traditional match analysis be complemented by using positional data and advanced KPIs? • How can these new methods evolve in the future? Based on data collected from active leagues and fully revised for the new second edition, the text now also examines data collection in relation to other sports, how a data-driven approach in decision-making is achieved by professional teams and how big is the potential of the new methods of data collection and how could they evolve in the future. Accessibly written, packed full of examples from elite football and supplemented with expert interviews, Data Analytics in Football is a thought- provoking, rigorously evidence-based guide to the use of data analytics in football performance analysis. As such, it is a vital resource for any student, researcher or coach interested in performance analysis and skill acquisition, or anyone interested in football more generally.
R 2,308.00
Real-Time Data Analytics for Large Scale Sensor Data
Real-Time Data Analytics for Large-Scale Sensor Data covers the theory and applications of hardware platforms and architectures, the development of software methods, techniques and tools, applications, governance and adoption strategies for the use of massive sensor data in real-time data analytics. It presents the leading-edge research in the field and identifies future challenges in this fledging research area. The book captures the essence of real-time IoT based solutions that require a multidisciplinary approach for catering to on-the-fly processing, including methods for high performance stream processing, adaptively streaming adjustment, uncertainty handling, latency handling, and more.
R 5,002.00
Soccer Analytics with Machine Learning
Struggling to grasp machine learning concepts or unsure how to apply them in the real world? This book aims to change that by using the world's most popular game—soccer—to illuminate key concepts in predictive modeling and data science. Whether you're a complete beginner or you're interested in entering the burgeoning field of sports analytics, you'll develop a solid foundation in machine learning through engaging examples that bridge academic principles with practical applications.Written by experts in both machine learning and sports analytics, this practical Python-focused guide introduces fundamental data science techniques using real soccer data. Ideal for students, analysts, and soccer fans alike, it offers instructions on models and techniques such as logistic regression, random forests, deep learning, simulations, and feature engineering. But instead of memorizing algorithms, you'll learn by building predictive models to analyze match outcomes, test betting strategies, run simulated game scenarios, and more.Understand machine learning concepts by working with real sports dataDevelop, refine, and evaluate machine learning models, using Python for data analysisCarry out detailed analyses and research on soccer game predictions and betting strategies to surface valuable insightsApply the skills you learn to predictive modeling scenarios in other industries
R 1,368.00
Data-Centric Artificial Intelligence for Multidisciplinary Applications
This book explores the need for a data‑centric AI approach and its application in the multidisciplinary domain, compared to a model‑centric approach. It examines the methodologies for data‑centric approaches, the use of data‑centric approaches in different domains, the need for edge AI and how it differs from cloud‑based AI. It discusses the new category of AI technology, "data‑centric AI" (DCAI), which focuses on comprehending, utilizing, and reaching conclusions from data. By adding machine learning and big data analytics tools, data‑centric AI modifies this by enabling it to learn from data rather than depending on algorithms. It can therefore make wiser choices and deliver more precise outcomes. Additionally, it has the potential to be significantly more scalable than conventional AI methods.• Includes a collection of case studies with experimentation results to adhere to the practical approaches• Examines challenges in dataset generation, synthetic datasets, analysis, and prediction algorithms in stochastic ways• Discusses methodologies to achieve accurate results by improving the quality of data• Comprises cases in healthcare and agriculture with implementation and impact of quality data in building AI applications
R 2,674.00
Big Data Analytics and Intelligent Techniques for Smart Cities
Big Data Analytics and Intelligent Techniques for Smart Cities covers fundamentals, advanced concepts, and applications of big data analytics for smart cities in a single volume. This comprehensive reference text discusses big data theory modeling and simulation for smart cities and examines case studies in a single volume. The text discusses how to develop a smart city and state-of-the-art system design, system verification, real-time control and adaptation, Internet of Things, and testbeds. It covers applications of smart cities as they relate to smart transportation/connected vehicle (CV) and intelligent transportation systems (ITS) for improved mobility, safety, and environmental protection. It will be useful as a reference text for graduate students in different areas including electrical engineering, computer science engineering, civil engineering, and electronics and communications engineering.Features: Technologies and algorithms associated with the application of big data for smart cities Discussions on big data theory modeling and simulation for smart cities Applications of smart cities as they relate to smart transportation and intelligent transportation systems (ITS) Discussions on concepts including smart education, smart culture, and smart transformation management for social and societal changes
R 2,761.18
Advanced Data Science and Analytics with Python
The second edition of Advanced Data Science and Analytics with Python reflects the rapid transformation of artificial intelligence in recent years. While preserving its practical, modular structure, this edition significantly expands coverage of the techniques shaping modern AI practice.The deep learning chapter has been substantially broadened to include reinforcement learning and generative adversarial networks, alongside a fully developed exploration of transformer architectures. Generative AI now takes centre stage, with dedicated coverage of self-attention, BERT, GPT, large language model evaluation and API-based interaction. Emerging agentic systems are introduced as part of the evolving AI landscape. Natural language processing has been enhanced with word embeddings, contextual representations and vector search, while network analysis now includes graph representation learning and embedding techniques. The chapter on data product deployment has been strengthened with modern Core ML workflows and new coverage of on-device Foundation Models, bridging experimentation and production.Fully updated for the contemporary Python ecosystem, this edition equips practitioners with the tools and architectural understanding required to design, build and deploy intelligent systems in today’s AI-driven world.
R 7,327.00
Data Analytics in Biomedical Engineering and Healthcare
Data Analytics in Biomedical Engineering and Healthcare explores key applications using data analytics, machine learning, and deep learning in health sciences and biomedical data. The book is useful for those working with big data analytics in biomedical research, medical industries, and medical research scientists. The book covers health analytics, data science, and machine and deep learning applications for biomedical data, covering areas such as predictive health analysis, electronic health records, medical image analysis, computational drug discovery, and genome structure prediction using predictive modeling. Case studies demonstrate big data applications in healthcare using the MapReduce and Hadoop frameworks.
R 4,414.00
Modelling, Computation and Optimization in Information Systems and Management Sciences
This proceedings set contains 85 selected full papers presented at the 3rd International Conference on Modelling, Computation and Optimization in Information Systems and Management Sciences - MCO 2015, held on May 11–13, 2015 at Lorraine University, France. The present part I of the 2 volume set includes articles devoted to Combinatorial optimization and applications, DC programming and DCA: thirty years of Developments, Dynamic Optimization, Modelling and Optimization in financial engineering, Multiobjective programming, Numerical Optimization, Spline Approximation and Optimization, as well as Variational Principles and Applications.
R 6,602.79
Cross-Disciplinary Models and Applications of Database Management
Cross-Disciplinary Models and Applications of Database Management: Advancing Approaches is an updated look at the latest tools and technology within the burgeoning field of database management. Perfect for the network administrator, technician, information technology specialist or consultant, or for academics and students, this volume presents the latest the field has to offer by way of cases and new research. As database languages, models, and systems change, it’s vital for practitioners within the field to stay abreast of the latest research and methods being used around the world, and this book offers the most current advances available.
R 7,611.34
Assessing COVID-19 and Other Pandemics and Epidemics using Computational Modelling and Data Analysis
This book comprehensively covers the topic of COVID-19 and other pandemics and epidemics data analytics using computational modelling. Biomedical and Health Informatics is an emerging field of research at the intersection of information science, computer science, and health care. The new era of pandemics and epidemics bring tremendous opportunities and challenges due to the plentiful and easily available medical data allowing for further analysis. The aim of pandemics and epidemics research is to ensure high-quality, efficient healthcare, better treatment and quality of life by efficiently analyzing the abundant medical, and healthcare data including patient’s data, electronic health records (EHRs) and lifestyle. In the past, it was a common requirement to have domain experts for developing models for biomedical or healthcare. However, recent advances in representation learning algorithms allow us to automatically learn the pattern and representation of the given data for the development of such models. Medical Image Mining, a novel research area (due to its large amount of medical images) are increasingly generated and stored digitally. These images are mainly in the form of: computed tomography (CT), X-ray, nuclear medicine imaging (PET, SPECT), magnetic resonance imaging (MRI) and ultrasound. Patients’ biomedical images can be digitized using data mining techniques and may help in answering several important and critical questions related to health care. Image mining in medicine can help to uncover new relationships between data and reveal new and useful information that can be helpful for scientists and biomedical practitioners. Assessing COVID-19 and Other Pandemics and Epidemics using Computational Modelling and Data Analysis will play a vital role in improving human life in response to pandemics and epidemics. The state-of-the-art approaches for data mining-based medical and health related applications will be of great value to researchers and practitioners working in biomedical, health informatics, and artificial intelligence..
R 4,406.07
Produktions- und Operations-Management
Quantitative Methoden zur Lösung aktueller Probleme der Produktionsplanung, -steuerung und -kontrolle stehen im Mittelpunkt dieses Lehrbuchs. Neben Methoden zur "traditionellen" Produktionsplanung werden neuere leistungsfähige Verfahren zur Planung einzelner Produktionssegmente behandelt. Im Unterschied zu den meisten deutschsprachigen Lehrbüchern der Produktionswirtschaft berücksichtigt das Buch - wie in der modernen angelsächsischen Literatur zum Thema üblich - auch Standort- und Layoutplanung sowie die Qualitätssicherung.
R 1,165.79
Simplify Big Data Analytics with Amazon EMR
Design scalable big data solutions using Hadoop, Spark, and AWS cloud native servicesKey FeaturesBuild data pipelines that require distributed processing capabilities on a large volume of dataDiscover the security features of EMR such as data protection and granular permission managementExplore best practices and optimization techniques for building data analytics solutions in Amazon EMRBook DescriptionAmazon EMR, formerly Amazon Elastic MapReduce, provides a managed Hadoop cluster in Amazon Web Services (AWS) that you can use to implement batch or streaming data pipelines. By gaining expertise in Amazon EMR, you can design and implement data analytics pipelines with persistent or transient EMR clusters in AWS. This book is a practical guide to Amazon EMR for building data pipelines. You'll start by understanding the Amazon EMR architecture, cluster nodes, features, and deployment options, along with their pricing. Next, the book covers the various big data applications that EMR supports. You'll then focus on the advanced configuration of EMR applications, hardware, networking, security, troubleshooting, logging, and the different SDKs and APIs it provides. Later chapters will show you how to implement common Amazon EMR use cases, including batch ETL with Spark, real-time streaming with Spark Streaming, and handling UPSERT in S3 Data Lake with Apache Hudi. Finally, you'll orchestrate your EMR jobs and strategize on-premises Hadoop cluster migration to EMR. In addition to this, you'll explore best practices and cost optimization techniques while implementing your data analytics pipeline in EMR. By the end of this book, you'll be able to build and deploy Hadoop- or Spark-based apps on Amazon EMR and also migrate your existing on-premises Hadoop workloads to AWS.What you will learnExplore Amazon EMR features, architecture, Hadoop interfaces, and EMR StudioConfigure, deploy, and orchestrate Hadoop or Spark jobs in productionImplement the security, data governance, and monitoring capabilities of EMRBuild applications for batch and real-time streaming data analytics solutionsPerform interactive development with a persistent EMR cluster and NotebookOrchestrate an EMR Spark job using AWS Step Functions and Apache AirflowWho this book is forThis book is for data engineers, data analysts, data scientists, and solution architects who are interested in building data analytics solutions with the Hadoop ecosystem services and Amazon EMR. Prior experience in either Python programming, Scala, or the Java programming language and a basic understanding of Hadoop and AWS will help you make the most out of this book.
R 1,661.00
Stochastic Modeling And Analytics In Healthcare Delivery Systems
In recent years, there has been an increased interest in the field of healthcare delivery systems. Scientists and practitioners are constantly searching for ways to improve the safety, quality and efficiency of these systems in order to achieve better patient outcome.This book focuses on the research and best practices in healthcare engineering and technology assessment. With contributions from researchers in the fields of healthcare system stochastic modeling, simulation, optimization and management, this is a valuable read.
R 5,072.65
Executive Data Quality Playbook
IBM research reveals that poor data quality costs the U.S. economy $3.1 trillion annually. Yet for most executives, this is an invisible problem—data looks reasonable, dashboards are polished, and trends appear logical. Until a quarterly projection is built on corrupted revenue data, a $50 million acquisition is approved on overstated retention numbers, or a client relationship collapses because portfolio performance figures were systematically wrong for three months.The Executive Data Quality Playbook solves this problem at its source: the executive level. Unlike IT-focused data quality guides that address schema validation and field-level cleansing, this book operates entirely from the C-suite perspective. Every chapter is designed to answer the question that actually matters to leaders: "Can I trust this data to make a decision that will define my organization's future?" The book introduces the Decision-Grade Data Classification System—a four-tier framework that matches data quality requirements to decision stakes, from Operational decisions (85% confidence threshold) to Bet-the-Company decisions (99.5%+ confidence threshold). This framework becomes the organizational standard for knowing which data to trust, when to trust it, and what to do when that trust breaks down.Executives receive the 24-Hour Data Quality Emergency Protocol, Executive Data Quality Dashboard templates, RACI accountability matrices, stakeholder trust-rebuilding roadmaps, vendor assessment frameworks, and technology ROI calculators. Each tool is field-tested—developed through 23+ years of managing high-stakes data quality at scale in one of the world's most complex financial institutions.The book targets a gap that no existing publication fills: practical, executive-level data quality management that requires no technical background, delivers immediate results, and builds sustainable organizational capability. For any leader who has ever made a strategic decision and quietly wondered if the data was actually telling the truth, this book is the system they have been missing.What You Will Learn: How to assess, contain, and communicate any data quality crisis using a proven 24-Hour Emergency Protocol — turning a potential career-defining disaster into a managed, recoverable situation. How to build an Executive Data Quality Dashboard that delivers early-warning signals before bad data corrupts a strategic decision — giving you real-time visibility into the trustworthiness of your most critical information. How to classify data by decision-grade confidence requirements — applying the right quality standard to the right decision, so quality-control effort lands where the stakes are highest instead of being spread thin across everything. How to protect the integrity of the KPIs your board, investors, and leadership team rely on — so the metrics that tell your company's story can withstand hard scrutiny. How to future-proof your organization against emerging risks like AI-generat
R 912.00
Computer Simulation in Operations Management
The management of production and service processes can be supported by microcomputer simulation models—effectively and inexpensively—if the techniques are presented in an understandable manner. Drs. Klafehn, Weinroth, and Boronico prove this and show how to do it—not only for the benefit of operations managers themselves, but for others with management responsibilities in a variety of businesses and industries. They will learn how important daily operations problems can be modeled on a microcomputer, gain understanding of overall simulation methodology, and learn the several forms of cost savings achievable through simulation. For teachers in business schools the book will also provide a link between general management and the management of engineering and R&D.The first chapter introduces the reader to the concepts and steps for undertaking a microcomputer simulation project. In addition, the benefits, drawbacks, and myths are reviewed in detail. Chapter two explores, in a conversational scenario, what is involved in taking a management operations problem involving a truck transfer depot from its point of inception to the formulation of a systems operation model, which in a later chapter is ultimately put into a computer simulation model and tested to, in a sense, come up with answers to the questions posed in the hypothetical conversation. Subsequent chapters in the book are oriented to a discussion of other operations management problems and the effort to seek insight and solutions through simulation modeling. A Just-in-Time manufacturing system is addressed, recognizing the push-pull concept as well as looking at the quality aspect. Attempting to determine the optimum levels for safety, stock, order points, and order quantity is investigated through computer simulation. These levels are predicated on balancing the costs associated with ordering and holding goods as well as the penalty costs of stocking out. Using a simulated environment enables the inclusion of the variability evidenced by the type of distribution. The remaining chapters also review alternative rules and what ifs as applied to machine configuration, facility location for a satellite EMS unit, and job shop operations. Each of the applications chapters provides a printout of the basic computer model, written in GPSS, that was then modified to investigate alternative scenarios.
R 3,664.00
Perspectives in Operations Management
In the fall of 1992 a conference honoring Elwood S. Buffa was held at the Anderson Graduate School of Management of the University of California, Los Angeles. This book is a collection of the work presented at that conference. The scholars who gathered to honor El are the prominent researchers in the field of Operations Management. Their collective work published in this book represents the richness of the field and provides the reader with valuable insights into its important issues and problems. While any grouping of the articles by these distinguished scholars will be arbitrary, I have organized the book in four sections. In the first section the articles dealing with the strategic issues in Operations Management are compiled. The articles deal with continuous improvement, quality, services, supply chain management, and creating value through operations. The articles that explore the interface of Operations Management with other functional areas, e.g. engineering and marketing, are grouped in the second section. The third section of the book contains articles that attempt to model some important planning problems that arise in the management of production and operations. Some of the papers in this section provide state of the art reviews of selected topic areas. Finally, the fourth section contains articles that deal with future directions for Operations Management. The authors offer several insights into the future evolution of the field. The book begins with the keynote address given by El Buffa at the start of the conference on November 2, 1991.
R 6,036.12
Application of Advanced Optimization Techniques for Healthcare Analytics
Application of Advanced Optimization Techniques for Healthcare Analytics, 1st Edition, is an excellent compilation of current and advanced optimization techniques which can readily be applied to solve different hospital management problems. The healthcare system is currently a topic of significant investigation to make life easier for those who are disabled, old, or sick, as well as for young children. The emphasis of the healthcare system has evolved throughout time due to several emerging beneficial technologies, such as personal digital assistants (PDAs), data mining, the internet of things, metaheuristics, fog computing, and cloud computing.Metaheuristics are strong technology for tackling several optimization problems in various fields, especially healthcare systems. The primary advantage of metaheuristic algorithms is their ability to find a better solution to a healthcare problem and their ability to consume as little time as possible. In addition, metaheuristics are more flexible compared to several other optimization techniques. These algorithms are not related to a specific optimization problem but could be applied to any optimization problem by making some small adaptations to become suitable to tackle it.The successful outcome of this book will enable a decision-maker or practitioner to pick a suitable optimization approach when making decisions to schedule patients under crowding environments with minimized human errors.
R 9,310.57
Management, Organization, and Childbirth
Management, Organization and Childbirth: Towards a New Model for the Birth Path explores the complex topic of the birth path with a multidisciplinary magnifying glass on the paradigms, languages, and tools critical to the organization, management, and clinical science.The work consists of five chapters. The first chapter provides a multidimensional analysis of childbirth. The second chapter presents an organizational analysis that moves in unison with different models of health. The third chapter studies the birth path in organizational and cynical terms by describing it in its core processes. The fourth chapter proposes a study conducted in the Italian context, which identifies some useful determinants for redesigning the birth path. The fifth chapter formulates a proposal for redesigning the birth path based on a new health paradigm.The proposed model offers useful insights for multiple categories of readers. To students of medicine and higher education tracks in healthcare management, it can offer opportunities to raise awareness not only regarding multi-professional practice but also regarding confrontation with complementary disciplines. To practitioners and policy makers, it can provide useful stimuli to promote rational and informed decisions around the childbirth. To researchers studying the health context within different disciplinary domains, the model can offer unexplored research spaces within the new business complex system.
R 2,455.51
Big Data Quantification for Complex Decision-Making
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. Additionally, it extends its reach to practitioners in data analytics, business intelligence, risk management, and strategic decision-making, offering pragmatic insights and methodologies. Graduate students in data science and decision analysis can also benefit from the book's potential as a textbook or supplementary reading material. By covering an array of topics, from big data analytics to deep learning models and game theory, this book positions itself as an indispensable resource, guiding readers towards mastering the fusion of big data and decision-making in an era defined by complexity and uncertainty.
R 12,141.03
Business Process Management Forum
This book constitutes the proceedings of the BPM Forum held at the 24th International Conference on Business Process Management, BPM 2026, which took place in Toronto, Canada, during September/October 2026. Contributions presented at the BPM Forum include novel applications, early-stage results, or creative perspectives on known problems. They cover a diverse and timely set of topics, reflecting the evolving socio-technical and AI-enhanced landscape of BPM. The topics explored include business process management; robotic process automation; process modeling-, execution-, monitoring-, and simulation; object-centric process mining; business intelligence; and large language models.The 24 full papers included in this book were carefully reviewed and selected from a total of 169 submissions. The papers were organized in topical sections as follows: Foundations; Engineering; and Management.
R 3,916.73
Long View of Research and Practice in Operations Research and Management Science
From the Foreword by Marshall Fisher, The Wharton School, University of Pennsylvania:As generation of academics and practitioners follows generation, it is worthwhile tocompile long views of the research and practice in the past to shed light on researchand practice going forward. This collection of peer-reviewed articles is intended toprovide such a long view.This book contains a collection of chapters written by leading scholars/practitionerswho have continued their efforts in developing and/or implementing innovativeOR/MS tools for solving real world problems. In this book, the contributors sharetheir perspectives about the past, present and future of OR/MS theoretical development, solution tools, modeling approaches, and applications. Specifically, this book collects chapters that offer insights about the following topics:• Survey articles taking a long view over the past two or more decades to arriveat the present state of the art while outlining ideas for future research. Surveysfocus on use of a particular OR/MS approach, e.g., mathematical programming(LP, MILP, etc.) and solution methods for particular family of application, e.g.,distribution system design, distribution planning system, health care.• Autobiographical or biographical accounts of how particular inventions (e.g.,Structured Modeling) were made. These could include personal experiences inearly development of OR/MS and an overview of what has happened since.• Development of OR/MS mathematical tools (e.g., stochastic programming, optimizationtheory).• Development of OR/MS in a particular industry sector such as global supplychain management.• Modeling systems for OR/MS and their development over time as well as speculationon future development (e.g., LINDO, LINGO, and What’sBest!)• New applications of OR/MS models (e.g., happiness)The target audience of this book is young researchers, graduate/advanced undergraduatestudents from OR/MS and related fields like computer science, engineering,and management as well as practitioners who want to understand how OR/MSmodeling came about over the past few decades and what research topics or modelingapproaches they could pursue in research or application.
R 3,850.09
Consumer-Driven Demand and Operations Management Models
To compete in today's volatile market with rapidly changing consumer tastes and erce competition, companies in the manufacturing and service industries are - ploying new mechanisms to increase sales, market shares, and pro ts. As an - fective mechanism to segment a market comprising of consumers with different needs, preferences, and willingness-to-pay, many rms have used product (or s- vice) variety with different price points to serve different segments of the market, see Ho (1998). Ideally, the price of each of these products (or services) targets a particular segment of customers. For example, airlines often use different terms of sales (refundable/non-refundable, upgradable/non-upgradable, direct/connecting ight, etc. ) to sell economy class tickets at different prices. Likewise, retailers - ten sell the same product at different prices in different channels (company's own web site, dealers' web sites, or company's physical stores) or at different times (- fore, during, and after the selling season), see Talluri and van Ryzin (2005). Ample academic literature in Operations Management and other areas considered these strategies.However, as consumers become more knowledgeable about the product, pricing, organizational and operational policies that the companies deploy for pr- ucts and services, their purchasing begins to change dramatically. In the academic Operations Management literature, consumer demand is often assumedtobe exogenous so that demand functions are usually modeled as well de- ned and exogenously speci ed functions of price and/or other product attributes such as quality.
R 5,497.18
Mathematical Modeling for Business Analytics
Mathematical Modeling for Business Analytics is written for decision makers at all levels. This book presents the latest tools and techniques available to help in the decision process. The interpretation and explanation of the results are crucial to understanding the strengths and limitations of modeling. This book emphasizes and focuses on the aspects of constructing a useful model formulation, as well as building the skills required for decision analysis. The book also focuses on sensitivity analysis.The author encourages readers to formally think about solving problems by using a thorough process. Many scenarios and illustrative examples are provided to help solve problems. Each chapter is also comprehensively arranged so that readers gain an in-depth understanding of the subject which includes introductions, background information and analysis. Both undergraduate and graduate students taking methods courses in methods and discrete mathematical modeling courses will greatly benefit from using this book.Boasts many illustrative examples to help solve problems Provides many solutions for each chapter Emphasizes model formulation and helps create model building skills for decision analysis Provides the tools to support analysis and interpretation
R 2,564.00
