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Proceedings of Data Analytics and Management
This book includes original unpublished contributions presented at the International Conference on Data Analytics and Management (ICDAM 2025), held at London Metropolitan University, London, UK, during June 2025. The book covers the topics in data analytics, data management, big data, computational intelligence, and communication networks. The book presents innovative work by leading academics, researchers, and experts from industry which is useful for young researchers and students. The book is divided into ten volumes.
R 10,257.00
Data Analysis and Related Applications, Volume 5
This book is a collective work by several leading scientists, analysts, engineers, mathematicians and statisticians, who have been working at the forefront of data analysis and related applications, arising from data science, operations research, engineering, machine learning or statistics. Data Analysis and Related Applications 5 represents a cross-section of current concerns and research interests in the above scientific areas. The collected material has been divided into appropriate sections to provide the reader with both theoretical and applied information on data analysis methods, models and techniques, along with appropriate applications.
R 5,373.00
Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations
This three volume set (CCIS 853-855) constitutes the proceedings of the 17th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2017, held in Cádiz, Spain, in June 2018.The 193 revised full papers were carefully reviewed and selected from 383 submissions. The papers are organized in topical sections on advances on explainable artificial intelligence; aggregation operators, fuzzy metrics and applications; belief function theory and its applications; current techniques to model, process and describe time series; discrete models and computational intelligence; formal concept analysis and uncertainty; fuzzy implication functions; fuzzy logic and artificial intelligence problems; fuzzy mathematical analysis and applications; fuzzy methods in data mining and knowledge discovery; fuzzy transforms: theory and applications to data analysis and image processing; imprecise probabilities: foundations and applications; mathematical fuzzy logic, mathematical morphology; measures of comparison and entropies for fuzzy sets and their extensions; new trends in data aggregation; pre-aggregation functions and generalized forms of monotonicity; rough and fuzzy similarity modelling tools; soft computing for decision making in uncertainty; soft computing in information retrieval and sentiment analysis; tri-partitions and uncertainty; decision making modeling and applications; logical methods in mining knowledge from big data; metaheuristics and machine learning; optimization models for modern analytics; uncertainty in medicine; uncertainty in Video/Image Processing (UVIP).
R 4,351.72
Operations Research in Space and Air
Operations Research in Space and Air is a selection of papers reflecting the experience and expertise of international OR consulting companies and academic groups. The global market and competition play a crucial part in the decision making processes within the Space and Air industries and this book gives practical examples of how advanced applications can be used by Space and Air industry management. The material within the book provides both the basic background for the novice modeler and a useful reference for experienced modelers. Students, researchers and OR practitioners will appreciate the details of the modeling techniques, the processes that have been implemented and the computational results that demonstrate the benefits in applying OR in the Space and Airline industries. Advances in PC and Workstations technology, in optimiza tion engines and in modeling techniques now enable solving problems, never before attained by Operations Research. In recent years the Ital ian OR Society (AfRO, www. airo. org) has organized annual forums for researchers and practitioners to meet together to present and dis cuss the various scientific and technical OR achievements. The OR in Space 8 Air session of AfR02001 and AfR02002 Conferences, together with optimization tools' applications, presented recent results achieved by Alenia Spazio S. p. A. (Turin), Alitalia, Milan Polytechnic and Turin Polytechinc. With additional contributions from academia and indus try they have enabled us to capture, in print, today's 'state-of-the-art' optimization and data mining solutions.
R 8,183.81
Handbook of Big Data Analytics
Big Data analytics is the complex process of examining big data to uncover information such as correlations, hidden patterns, trends and user and customer preferences, to allow organizations and businesses to make more informed decisions. These methods and technologies have become ubiquitous in all fields of science, engineering, business and management due to the rise of data-driven models as well as data engineering developments using parallel and distributed computational analytics frameworks, data and algorithm parallelization, and GPGPU programming. However, there remain potential issues that need to be addressed to enable big data processing and analytics in real time. In the first volume of this comprehensive two-volume handbook, the authors present several methodologies to support Big Data analytics including database management, processing frameworks and architectures, data lakes, query optimization strategies, towards real-time data processing, data stream analytics, Fog and Edge computing, and Artificial Intelligence and Big Data. The second volume is dedicated to a wide range of applications in secure data storage, privacy-preserving, Software Defined Networks (SDN), Internet of Things (IoTs), behaviour analytics, traffic predictions, gender based classification on e-commerce data, recommender systems, Big Data regression with Apache Spark, visual sentiment analysis, wavelet Neural Network via GPU, stock market movement predictions, and financial reporting. The two-volume work is aimed at providing a unique platform for researchers, engineers, developers, educators and advanced students in the field of Big Data analytics.
R 4,702.00
Proceedings of Data Analytics and Management
This book includes original unpublished contributions presented at the International Conference on Data Analytics and Management (ICDAM 2025), held at London Metropolitan University, London, UK, during June 2025. The book covers the topics in data analytics, data management, big data, computational intelligence, and communication networks. The book presents innovative work by leading academics, researchers, and experts from industry which is useful for young researchers and students. The book is divided into ten volumes.
R 10,257.00
Logistics Information Systems
In today’s era of digital transformation, the logistics sector is one of the most technology-intensive industries. This book provides a comprehensive overview of the IT infrastructure required for company operations, the types of enterprise software used in logistics, and current data collection technologies. It addresses the terminology, information flows, and application contexts of the necessary software, helping readers to see the big picture without being overwhelmed by technical details.It explains principal methodologies for modelling and designing systems and describes the objectives of project management and system analysis, not to mention why they are so essential to developing information systems. It also defines critical terms before turning to sector-specific hardware and software solutions for logistics operations: data collection, data processing, and data analytics solutions. In addition, the book includes sections that introduce readers to programming and the core of the database, piquing their interest and guiding them to a higher level of specialization. Study questions are provided at the end of each chapter to test reader comprehension.This book will be a helpful resource for students in logistics or professionals working in the fields of business administration, foreign trade, industrial engineering, ERP, or MIS who want to advance their knowledge and skills in the logistics industry.
R 7,162.45
Operations Management for Healthcare
This fully updated edition of the bestselling textbook on Health Service Operations Management provides an invaluable reference for students and researchers in the fields of healthcare management, operations management and patient flow logistics. Featuring theoretical frameworks and a comprehensive set of practical case studies, this book also covers subjects such as hospital planning and supply chain management in healthcare, quality assurance and performance management. Healthcare managers work together with healthcare professionals in a multitude of challenging scenarios. Trade-offs have to be made between waiting times for customers and efficient use of scarce resources, between quality of care and quality of services, between the perspective of a single pathway and the total system, and between the perspective of a single provider and that of a network of providers working together in the chain of primary care, hospitals, nursing homes and home care. This book guides healthcare students and professionals through a set of practical tools and resources, ranging from simple queueing models to more complicated analytical models, to help address these issues. The book can be used at an undergraduate level by introducing concepts, definitions and approaches, and at a postgraduate level through the application of approaches to operations management problems in healthcare practice. It will serve as a primary textbook for a health service operations management course module in a Master's program on healthcare management.
R 1,663.08
AI-Driven Innovation in Healthcare Data Analytics
As the healthcare industry continues to rely on data to enhance patient outcomes and streamline operations, artificial intelligence (AI) becomes a powerful tool for complex dataset analysis using improved speed and accuracy. From predictive modeling in disease outbreak management to personalized treatment plans for individual patient profiles, AI technologies are reshaping clinical decision-making and resource allocation. Harnessing the potential of machine learning and advanced analytics may allow healthcare providers to uncover insights that drive innovation, improve patient care, and optimize operational efficiency. AI-Driven Innovation in Healthcare Data Analytics explores the intersection of AI and healthcare data analytics. It examines the application of AI-driven techniques, including machine learning, deep learning, and data mining, in addressing complex challenges in healthcare management. This book covers topics such as data science, medical diagnosis, and patient care, and is a useful resource for healthcare professionals, data scientists, computer engineers, business owners, academicians, and researchers.
R 13,740.03
Big Data and Analytics
This book presents and discusses the main strategic and organizational challenges posed by Big Data and analytics in a manner relevant to both practitioners and scholars. The first part of the book analyzes strategic issues relating to the growing relevance of Big Data and analytics for competitive advantage, which is also attributable to empowerment of activities such as consumer profiling, market segmentation, and development of new products or services. Detailed consideration is also given to the strategic impact of Big Data and analytics on innovation in domains such as government and education and to Big Data-driven business models. The second part of the book addresses the impact of Big Data and analytics on management and organizations, focusing on challenges for governance, evaluation, and change management, while the concluding part reviews real examples of Big Data and analytics innovation at the global level. The text is supported by informative illustrations and case studies, so that practitioners can use the book as a toolbox to improve understanding and exploit business opportunities related to Big Data and analytics.
R 1,540.91
Data Science and Big Data Analytics in Smart Environments
Most applications generate large datasets, like social networking and social influence programs, smart cities applications, smart house environments, Cloud applications, public web sites, scientific experiments and simulations, data warehouse, monitoring platforms, and e-government services. Data grows rapidly, since applications produce continuously increasing volumes of both unstructured and structured data. Large-scale interconnected systems aim to aggregate and efficiently exploit the power of widely distributed resources. In this context, major solutions for scalability, mobility, reliability, fault tolerance and security are required to achieve high performance and to create a smart environment. The impact on data processing, transfer and storage is the need to re-evaluate the approaches and solutions to better answer the user needs. A variety of solutions for specific applications and platforms exist so a thorough and systematic analysis of existing solutions for data science, data analytics, methods and algorithms used in Big Data processing and storage environments is significant in designing and implementing a smart environment.Fundamental issues pertaining to smart environments (smart cities, ambient assisted leaving, smart houses, green houses, cyber physical systems, etc.) are reviewed. Most of the current efforts still do not adequately address the heterogeneity of different distributed systems, the interoperability between them, and the systems resilience. This book will primarily encompass practical approaches that promote research in all aspects of data processing, data analytics, data processing in different type of systems: Cluster Computing, Grid Computing, Peer-to-Peer, Cloud/Edge/Fog Computing, all involving elements of heterogeneity, having a large variety of tools and software to manage them. The main role of resource management techniques in this domain is to create the suitable frameworks for development of applications and deployment in smart environments, with respect to high performance. The book focuses on topics covering algorithms, architectures, management models, high performance computing techniques and large-scale distributed systems.
R 10,601.02
Handbook of Stochastic Models and Analysis of Manufacturing System Operations
This handbook surveys important stochastic problems and models in manufacturing system operations and their stochastic analysis. Using analytical models to design and control manufacturing systems and their operations entail critical stochastic performance analysis as well as integrated optimization models of these systems. Topics deal with the areas of facilities planning, transportation, and material handling systems, logistics and supply chain management, and integrated productivity and quality models covering:• Stochastic modeling and analysis of manufacturing systems• Design, analysis, and optimization of manufacturing systems• Facilities planning, transportation, and material handling systems analysis• Production planning, scheduling systems, management, and control• Analytical approaches to logistics and supply chain management• Integrated productivity and quality models, and their analysis • Literature surveys of issues relevant in manufacturing systems• Case studies of manufacturing system operations and analysisToday’s manufacturing system operations are becoming increasingly complex. Advanced knowledge of best practices for treating these problems is not always well known. The purpose of the book is to create a foundation for the development of stochastic models and their analysis in manufacturing system operations. Given the handbook nature of the volume, introducing basic principles, concepts, and algorithms for treating these problems and their solutions is the main intent of this handbook. Readers unfamiliar with these research areas will be able to find a research foundation for studying these problems and systems.
R 10,068.45
Applications of Machine Learning and Data Analytics Models in Maritime Transportation
Machine learning and data analytics can be used to inform technical, commercial and financial decisions in the maritime industry. Applications of Machine Learning and Data Analytics Models in Maritime Transportation explores the fundamental principles of analysing maritime transportation related practical problems using data-driven models, with a particular focus on machine learning and operations research models. Data-enabled methodologies, technologies, and applications in maritime transportation are clearly and concisely explained, and case studies of typical maritime challenges and solutions are also included. The authors begin with an introduction to maritime transportation, followed by chapters providing an overview of ship inspection by port state control, and the principles of data driven models. Further chapters cover linear regression models, Bayesian networks, support vector machines, artificial neural networks, tree-based models, association rule learning, cluster analysis, classic and emerging approaches to solving practical problems in maritime transport, incorporating shipping domain knowledge into data-driven models, explanation of black-box machine learning models in maritime transport, linear optimization, advanced linear optimization, and integer optimization. A concluding chapter provides an overview of coverage and explores future possibilities in the field. The book will be especially useful to researchers and professionals with expertise in maritime research who wish to learn how to apply data analytics and machine learning to their fields.
R 4,274.00
Proceedings of Data Analytics and Management
This book includes original unpublished contributions presented at the International Conference on Data Analytics and Management (ICDAM 2025), held at London Metropolitan University, London, UK, during June 2025. The book covers the topics in data analytics, data management, big data, computational intelligence, and communication networks. The book presents innovative work by leading academics, researchers, and experts from industry which is useful for young researchers and students. The book is divided into ten volumes.
R 10,257.00
Proceedings of Data Analytics and Management
This book includes original unpublished contributions presented at the International Conference on Data Analytics and Management (ICDAM 2025), held at London Metropolitan University, London, UK, during June 2025. The book covers the topics in data analytics, data management, big data, computational intelligence, and communication networks. The book presents innovative work by leading academics, researchers, and experts from industry which is useful for young researchers and students. The book is divided into ten volumes.
R 10,257.00
Data Analytics in the Era of the Industrial Internet of Things
This book presents the characteristics and benefits industrial organizations can reap from the Industrial Internet of Things (IIoT). These characteristics and benefits include enhanced competitiveness, increased proactive decision-making, improved creativity and innovation, augmented job creation, heightened agility to respond to continuously changing challenges, and intensified data-driven decision making. In a straightforward fashion, the book also helps readers understand complex concepts that are core to IIoT enterprises, such as Big Data, analytic architecture platforms, machine learning (ML) and data science algorithms, and the power of visualization to enrich the domains experts’ decision making. The book also guides the reader on how to think about ways to define new business paradigms that the IIoT facilitates, as well how to increase the probability of success in managing analytic projects that are the core engine of decision-making in the IIoT enterprise. The book starts by defining an IIoT enterprise and the framework used to efficiently operate. A description of the concepts of industrial analytics, which is a major engine for decision making in the IIoT enterprise, is provided. It then discusses how data and machine learning (ML) play an important role in increasing the competitiveness of industrial enterprises that operate using the IIoT technology and business concepts. Real world examples of data driven IIoT enterprises and various business models are presented and a discussion on how the use of ML and data science help address complex decision-making problems and generate new job opportunities. The book presents in an easy-to-understand manner how ML algorithms work and operate on data generated in the IIoT enterprise. Useful for any industry professional interested in advanced industrial software applications, including business managers and professionals interested in how data analytics can help industries and to develop innovative business solutions, as well as data and computer scientists who wish to bridge the analytics and computer science fields with the industrial world, and project managers interested in managing advanced analytic projects.
R 4,682.20
R 373.00
Operational Risk Modelling and Management
Taking into account the standards of the Basel Accord, Operational Risk Modelling and Management presents a simulation model for generating the loss distribution of operational risk. It also examines a multitude of management issues that must be considered when adjusting the quantitative results of a comprehensive model.The book emphasizes techniques that can be understood and applied by practitioners. In the quantitative portions of the text, the author supplies key concepts and definitions without stating theorems or delving into mathematical proofs. He also offers references for readers looking for further background information. In addition, the book includes a Monte Carlo simulation of risk capital in the form of a run-through example of risk calculations based on data from a quantitative impact study. Since the computations are too complicated for a scripting language, a prototypical software program can be downloaded from www.garrulus.comHelping you navigate the tricky world of risk calculation and management, this book presents two main building blocks for determining how much capital needs to be reserved for operational risk. It employs the loss distribution approach as a model for calculating the risk capital figure and explains risk mitigation through management and management’s actuations.
R 12,341.23
Emerging Frontiers in Operations and Supply Chain Management
This edited book addresses the challenges in managing the operations and supply chain of organizations in the era of internet of things and Industry 4.0. It presents cutting edge research on real world operations related problems, in-depth analyses, and relevant managerial implications. Wide variety of solution approaches such as quantitative, quantitative, and simulations are presented in the context of managing the operations and supply chains. Consisting of selected papers from the XXIII Annual International Conference of Society of Operations Management, this volume is part of a two volume series with the other book consisting of chapters on quantitative decision making. This edited book covers various quantitative models on operations and supply chain management such as inventory optimization, machine learning-operations research integrated model for healthcare systems, game-theoretic analysis of review strategies in truthful information sharing, design of contracts in supply chains, supply chain optimization, inventory routing, and shop floor scheduling. In addition to the quantitative models, several innovative heuristics are proposed for different problems. This book explores qualitative models on improving the performance of small and medium enterprises and petroleum industries and a simulation model for staff allocation in the information technology industry. Finally, this book provides review articles on vaccine supply chains and behavioral operations management.The book throws light on the emerging trends in the use of analytics, optimization, and simulation tools and empirical analysis to improve the performance of operations and supply chains of organizations. It will serve as an essential resource for practitioners, students, faculty members and scholars in operations management and related areas to gain knowledge and pursue high quality research on developments in areas such as managing the resource management and the solution methodology---innovative tools employed in addressing the real world problems and the different optimization techniques.
R 5,531.21
Measurement, Monitoring, Modelling and Control of Bioprocesses
Automated Measurement and Monitoring of Bioprocesses: Key Elements of the M3C Strategy, by Bernhard Sonnleitner Automatic Control of Bioprocesses, by Marc Stanke, Bernd Hitzmann An Advanced Monitoring Platform for Rational Design of Recombinant Processes, by G. Striedner, K. Bayer Modelling Approaches for Bio-Manufacturing Operations, by Sunil Chhatre Extreme Scale-Down Approaches for Rapid Chromatography Column Design and Scale-Up During Bioprocess Development, by Sunil Chhatre Applying Mechanistic Models in Bioprocess Development, by Rita Lencastre Fernandes, Vijaya Krishna Bodla, Magnus Carlquist, Anna-Lena Heins, Anna Eliasson Lantz, Gürkan Sin and Krist V. Gernaey Multivariate Data Analysis for Advancing the Interpretation of Bioprocess Measurement and Monitoring Data, by Jarka Glassey Design of Pathway-Level Bioprocess Monitoring and Control Strategies Supported by Metabolic Networks, by Inês A. Isidro, Ana R. Ferreira, João J. Clemente, António E. Cunha, João M. L. Dias, Rui Oliveira Knowledge Management and Process Monitoring of Pharmaceutical Processes in the Quality by Design Paradigm, by Anurag S Rathore, Anshuman Bansal, Jaspinder Hans The Choice of Suitable Online Analytical Techniques and Data Processing for Monitoring of Bioprocesses, by Ian Marison, Siobhán Hennessy, Róisín Foley, Moira Schuler, Senthilkumar Sivaprakasam, Brian Freeland
R 13,294.64
Emerging Frontiers in Operations and Supply Chain Management
This edited book addresses the challenges in managing the operations and supply chain of organizations in the era of internet of things and Industry 4.0. It presents cutting edge research on real world operations related problems, in-depth analyses, and relevant managerial implications. Wide variety of solution approaches such as quantitative, quantitative, and simulations are presented in the context of managing the operations and supply chains. Consisting of selected papers from the XXIII Annual International Conference of Society of Operations Management, this volume is part of a two volume series with the other book consisting of chapters on quantitative decision making. This edited book covers various quantitative models on operations and supply chain management such as inventory optimization, machine learning-operations research integrated model for healthcare systems, game-theoretic analysis of review strategies in truthful information sharing, design of contracts in supply chains, supply chain optimization, inventory routing, and shop floor scheduling. In addition to the quantitative models, several innovative heuristics are proposed for different problems. This book explores qualitative models on improving the performance of small and medium enterprises and petroleum industries and a simulation model for staff allocation in the information technology industry. Finally, this book provides review articles on vaccine supply chains and behavioral operations management.The book throws light on the emerging trends in the use of analytics, optimization, and simulation tools and empirical analysis to improve the performance of operations and supply chains of organizations. It will serve as an essential resource for practitioners, students, faculty members and scholars in operations management and related areas to gain knowledge and pursue high quality research on developments in areas such as managing the resource management and the solution methodology---innovative tools employed in addressing the real world problems and the different optimization techniques.
R 5,859.26
Recent Trends and Future Direction for Data Analytics
In an increasingly data-centric world, scholars and practitioners grapple with the complexities of harnessing data analytics effectively across various industries. The challenge lies in navigating the rapid evolution of methodologies, identifying emerging trends, and understanding the nuanced applications of data analytics in real-world scenarios. This gap between theory and practice inhibits academic progress. It hampers industry innovation, leaving stakeholders needing help to leverage data to its full potential. Recent Trends and Future Direction for Data Analytics presents a compelling solution. By delving into real-world case studies spanning supply chain management, marketing, healthcare, and finance, this book bridges the gap between theory and practice, offering invaluable insights into the practical applications of data analytics. A systematic exploration of fundamental concepts, advanced techniques, and specialized topics equips scholars, researchers, and industry professionals with the knowledge and tools needed to navigate the complexities of data analytics with confidence. This book is designed as a comprehensive resource for academia and industry alike. It serves as a roadmap for understanding the current state of data analytics and anticipating its future trajectories. By offering a blend of theoretical foundations and practical applications, it empowers readers to unlock the full potential of data analytics, driving innovation, informed decision-making, and transformative change across diverse sectors.
R 11,039.06
Supply Chain Analytics
Supply Chain Analytics, second edition, introduces the reader to data analytics and demonstrates the value of its effective use in the improvement of supply chain (SC) process performance. By describing four key SC processes and illustrating – through worked examples – how the descriptive, predictive, and prescriptive analytic methods can be applied to enhance those processes, this book presents a more comprehensive learning experience for the reader than has been offered previously.Key topics and issues are addressed, including the capriciousness of modern SC operating environments; the imperative of SC sustainability; the need for heightened SC risk management; the building of SC resilience; the pursuit of SC optimisation; and the use of big data, data mining, cloud computing, machine learning, artificial intelligence (AI), and importantly the social issues confronting SC analysts in carrying out their work.The author identifies four core SC processes – strategy, design, execution, and people – to which the analytic techniques explained can be applied to ensure continuous performance improvement and the growth of competitive advantage.Pedagogy to aid learning is incorporated throughout, including an opening section for each chapter explaining the intended learning outcomes; worked examples illustrating how each analytic technique works, how it is applied, and what to be careful of; tables, diagrams, and equations to help ‘visualise’ the concepts and methods covered; end-of-chapter case studies; review questions; and assignment tasks.Providing both management expertise and technical skills, which are essential to decision-makers in the SC, this textbook is an essential reading for advanced undergraduate and postgraduate students of SC analytics, SC leaders, and SC operations management professionals. Its practice-based and applied approach also makes it valuable for teaching academics, organisational trainers and coaches, operating SC practitioners, and those pursuing professional qualifications.Online resources include chapter-by-chapter PowerPoint slides, tutorial exercises, written assignments, worked examples using Excel, and a test bank of exam questions.
R 6,997.73
Data Analytics
Good data analytics is the basis for effective decisions. Whoever has the data, has the ability to extract information promptly and effectively to make pertinent decisions. The premise of this handbook is to empower users and tool developers with the appropriate collection of formulas and techniques for data analytics and to serve as a quick reference to keep pertinent formulas within fingertip reach of readers. This handbook includes formulas that will appeal to mathematically inclined readers. It discusses how to use data analytics to improve decision-making and is ideal for those new to using data analytics to show how to expand their usage horizon. It provides quantitative techniques for modeling pandemics, such as COVID-19. It also adds to the suite of mathematical tools for emerging technical areas. This handbook is a handy reference for researchers, practitioners, educators, and students in areas such as industrial engineering, production engineering, project management, civil engineering, mechanical engineering, technology management, and business management worldwide.
R 3,824.03
Proceedings of Data Analytics and Management
This book includes original unpublished contributions presented at the International Conference on Data Analytics and Management (ICDAM 2025), held at London Metropolitan University, London, UK, during June 2025. The book covers the topics in data analytics, data management, big data, computational intelligence, and communication networks. The book presents innovative work by leading academics, researchers, and experts from industry which is useful for young researchers and students. The book is divided into ten volumes.
R 10,257.00
Airline Operations and Management
Airline Operations and Management: A Management Textbook presents a survey of the airline industry, with a strong managerial perspective. It integrates and applies the fundamentals of several management disciplines, particularly operations, marketing, economics and finance, to develop a comprehensive overview. It also provides readers with a solid historical background, and offers a global perspective of the industry, with examples drawn from airlines around the world. Updates for the second edition include:Fresh data and examplesA range of international case studies exploring real-life applicationsNew or increased coverage of key topics such as the COVID-19 pandemic, state aid, and new business modelsNew chapters on fleet management and labor relations and HRMLecture slides for instructorsThis textbook is for advanced undergraduate and graduate students of airline management, but it should also be useful to entry and junior-level airline managers and professionals seeking to expand their knowledge of the industry beyond their functional area.
R 8,390.06
Analytics for Managers
Analytics is one of a number of terms which are used to describe a data-driven more scientific approach to management. Ability in analytics is an essential management skill: knowledge of data and analytics helps the manager to analyze decision situations, prevent problem situations from arising, identify new opportunities, and often enables many millions of dollars to be added to the bottom line for the organization.The objective of this book is to introduce analytics from the perspective of the general manager of a corporation. Rather than examine the details or attempt an encyclopaedic review of the field, this text emphasizes the strategic role that analytics is playing in globally competitive corporations today.The chapters of this book are organized in two main parts. The first part introduces a problem area and presents some basic analytical concepts that have been successfully used to address the problem area. The objective of this material is to provide the student, the manager of the future, with a general understanding of the tools and techniques used by the analyst.
R 9,384.17
Financial Modeling and Reporting with Microsoft Power BI
Design powerful financial reports in Power BI by building models, measures, and dashboards tailored for real-world accounting and analyticsKey FeaturesBuild a complete financial data model from ledgers, journals, and budgetsMaster DAX for income statements, KPIs, and performance analysisLearn Power BI Paginated and AI tools for printable and predictive reportingPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionFinancial Modeling and Reporting with Microsoft Power BI is the definitive guide to designing high-performance, flexible, and insightful financial reports using Power BI. This Power BI book empowers finance and BI professionals to create everything from trial balances to enterprise-wide performance dashboards with ease and precision.The book starts by helping you define your reporting goals and data sources, mapping these needs to Power BI’s capabilities. You’ll then build a core financial data model—covering ledger transactions, charts of accounts, and multi-company support. As you proceed, you’ll integrate complex DAX measures, handle foreign exchange and journal entries, and extend your model with budgeting and inventory data.Each chapter builds toward a comprehensive suite of reports, complete with visual best practices and tested metrics. You’ll learn to streamline datasets using Power Query, test for data integrity, and generate printable reports via Power BI Paginated. The final chapters dive into using AI, predictive analytics, and Microsoft Fabric to future-proof your reporting.Whether you're consolidating data across systems or evolving your reports for changing business needs, this hands-on guide ensures you’re prepared to meet the demands of modern finance.What you will learnBuild core financial models from ledgers and accountsCreate Trial Balance and Income Statements using DAXOptimize Power BI with Power Query and data transformationAdd budgets, targets, and KPIs to performance dashboardsIntegrate inventory data for nuanced stock reportingProduce printable reports using Power BI PaginatedApply AI for report generation and predictive analyticsTest, tune, and evolve reports for secure, scalable useWho this book is forThis book is for finance professionals, accountants, financial analysts, and BI developers who want to leverage Power BI to improve, automate, and future-proof their financial reporting. Whether consolidating data from ERPs, building reports across entities, or exploring advanced Power BI features, this book equips readers with practical skills and strategic insight.
R 1,628.00
Data Analytics and Management in Data Intensive Domains
This book constitutes the refereed proceedings of the 28th International Conference on Data Analytics and Management in Data Intensive Domains, DAMDID/RCDL 2016, held in Ershovo, Moscow, Russia, in October 2016.The 16 revised full papers presented together with one invited talk and two keynote papers were carefully reviewed and selected from 57 submissions. The papers are organized in topical sections on semantic modeling in data intensive domains; knowledge and learning management; text mining; data infrastructures in astrophysics; data analysis; research infrastructures; position paper.
R 1,555.49
Data Analytics and Management in Data Intensive Domains
This book constitutes the post-conference proceedings of the 25th International Conference on Data Analytics and Management in Data Intensive Domains, DAMDID/RCDL 2023, held in Moscow, Russia, during 24-27 October 2023.The 21 papers presented here were carefully reviewed and selected from 75 submissions. These papers are organized in the following topical sections: Data Models and Knowledge Graphs; Databases in Data Intensive Domains; Machine learning methods and applications; Data Analysis in Astronomy & Information extraction from text. Papers from keynote talks have also been included in this book.
R 3,132.71
Data Analytics and Digital Transformation
Understanding the significance of data analytics is paramount for digital transformation but in many organisations they are separate units without fully aligned goals. As organisations are applying digital transformations to be adaptive and agile in a competitive environment, data analytics can play a critical role for their success. This book explores the crossroads between them and how to leverage their connection for improved business outcomes. The need to collaborate and share data is becoming an integral part of digital transformation. This creates new opportunities but also requires well considered and continuously assessed decision making as competitiveness is at stake. This book details approaches, concepts and frameworks, as well as actionable insights and good practices, including combined data management and agile concepts. Critical issues are discussed such as data quality and data governance, as well as compliance, privacy, and ethics. It also offers insights into how both private and public organisations can innovate and keep up with growing data volumes and increasing technological developments in the short, mid and long term. This book will be of direct appeal to global researchers and students across a range of business disciplines, including technology and innovation management, organisational studies and strategic management. It is also relevant for policy makers, regulators and executives of private and public organisations looking to implement successful transformation policies.
R 9,312.77
Heterogeneous Data Management, Polystores, and Analytics for Healthcare
This book constitutes revised selected papers from two VLDB workshops: The International Workshop on Polystore Systems for Heterogeneous Data in Multiple Databases with Privacy and Security Assurances, Poly 2020, and the 6th International Workshop on Data Management and Analytics for Medicine and Healthcare, DMAH 2020, which were held virtually on August 31 and September 4, 2020.For Poly 2020, 4 full and 3 short papers were accepted from 10 submissions; and for DMAH 2020, 7 full and 2 short papers were accepted from a total of 15 submissions. The papers were organized in topical sections as follows: Privacy, Security and/or Policy Issues for Heterogenous Data; COVID-19 Data Analytics and Visualization; Deep Learning based Biomedical Data Analytics; NLP based Learning from Unstructured Data; Biomedical Data Modelling and Prediction.
R 2,165.36
New Perspectives in Operations Research and Management Science
This book presents innovative operations research applications in business, specifically industrial engineering and its sub-disciplines. It investigates new perspectives in operations research and management science with regard to research methods, the research context, and industrial engineering, offering readers a broad range of new approaches to management problems. The book features the latest work of researchers who have worked with Professor Fusun Ulengin or built upon her work in their academic careers.Written in honor of Prof. Ulengin, this book was edited by her former Ph.D. students, who are now experts in operations research, multiple criteria decision making, competitiveness, logistics, and supply chain management.Prof. Ulengin’s impact in academia is visible in the range of topics and methodologies featured in this book: Location and transportation problems, competitiveness of nations, food supply chains, debt collection, mathematical modelling, multiple criteria decision making, data envelopment analysis, random forests, and Bayesian networks.
R 7,759.21
Data Analytics and Management in Data Intensive Domains
This book includes the revised selected papers from the 26th International Conference on Data Analytics and Management in Data Intensive Domains, DAMDID/RCDL 2024, held in Nizhny Novgorod, Russia, during October 22-25, 2024. The 15 full papers and 5 short papers presented in this volume were carefully reviewed and selected from 71 submissions. They focus on Conceptual Modeling and Ontologies; Generative and Transformer-Based Models; Machine Learning Methods and Applications and Statistical Methods and Applications.
R 3,132.57
Big Data Analytics in Smart Manufacturing
The significant objective of this edited book is to bridge the gap between smart manufacturing and big data by exploring the challenges and limitations. Companies employ big data technology in the manufacturing field to acquire data about the products. Manufacturing companies could gain a deep business insight by tracking customer details, monitoring fuel consumption, detecting product defects, and supply chain management. Moreover, the convergence of smart manufacturing and big data analytics currently suffers due to data privacy concern, short of qualified personnel, inadequate investment, long-term storage management of high-quality data. The technological advancement makes the data storage more accessible, cheaper and the convergence of these technologies seems to be more promising in the recent era. This book identified the innovative challenges in the industrial domains by integrating heterogeneous data sources such as structured data, semi-structures data, geo-spatial data, textual information, multimedia data, social networking data, etc. It promotes data-driven business modelling processes by adopting big data technologies in the manufacturing industry. Big data analytics is emerging as a promising discipline in the manufacturing industry to build the rigid industrial data platforms. Moreover, big data facilitates process automation in the complete lifecycle of product design and tracking. This book is an essential guide and reference since it synthesizes interdisciplinary theoretical concepts, definitions, and models, involved in smart manufacturing domain. It also provides real-world scenarios and applications, making it accessible to a wider interdisciplinary audience.FeaturesThe readers will get an overview about the smart manufacturing system which enables optimized manufacturing processes and benefits the users by increasing overall profitThe researchers will get insight about how the big data technology leverages in finding new associations, factors and patterns through data stream observations in real time smart manufacturing systemsThe industrialist can get an overview about the detection of defects in design, rapid response to market, innovative products to meet the customer requirement which can benefit their per capita income in better wayDiscusses technical viewpoints, concepts, theories, and underlying assumptions that are used in smart manufacturingInformation delivered in a user-friendly manner for students, researchers, industrial experts, and business innovators, as well as for professionals and practitioners
R 5,656.58
Cloud-Based Big Data Analytics in Vehicular Ad-Hoc Networks
Vehicular traffic congestion and accidents remain universal issues in today's world. Due to the continued growth in the use of vehicles, optimizing traffic management operations is an immense challenge. To reduce the number of traffic accidents, improve the performance of transportation systems, enhance road safety, and protect the environment, vehicular ad-hoc networks have been introduced. Current developments in wireless communication, computing paradigms, big data, and cloud computing enable the enhancement of these networks, equipped with wireless communication capabilities and high-performance processing tools. Cloud-Based Big Data Analytics in Vehicular Ad-Hoc Networks is a pivotal reference source that provides vital research on cloud and data analytic applications in intelligent transportation systems. While highlighting topics such as location routing, accident detection, and data warehousing, this publication addresses future challenges in vehicular ad-hoc networks and presents viable solutions. This book is ideally designed for researchers, computer scientists, engineers, automobile industry professionals, IT practitioners, academicians, and students seeking current research on cloud computing models in vehicular networks.
R 7,929.01
Advances in Production Management Systems. The Path to Digital Transformation and Innovation of Production Management Systems
The two-volume set IFIP AICT 591 and 592 constitutes the refereed proceedings of the International IFIP WG 5.7 Conference on Advances in Production Management Systems, APMS 2020, held in Novi Sad, Serbia, in August/September 2020.The 164 papers presented were carefully reviewed and selected from 199 submissions. They discuss globally pressing issues in smart manufacturing, operations management, supply chain management, and Industry 4.0. The papers are organized in the following topical sections:Part I: advanced modelling, simulation and data analytics in production and supply networks; advanced, digital and smart manufacturing; digital and virtual quality management systems; cloud-manufacturing; cyber-physical production systems and digital twins; IIOT interoperability; supply chain planning and optimization; digital and smart supply chain management; intelligent logistics networks management; artificial intelligence and blockchain technologies in logistics and DSN; novel production planning and control approaches; machine learning and artificial intelligence; connected, smart factories of the future; manufacturing systems engineering: agile, flexible, reconfigurable; digital assistance systems: augmented reality and virtual reality; circular products design and engineering; circular, green, sustainable manufacturing; environmental and social lifecycle assessments; socio-cultural aspects in production systems; data-driven manufacturing and services operations management; product-service systems in DSN; and collaborative design and engineeringPart II: the Operator 4.0: new physical and cognitive evolutionary paths; digital transformation approaches in production management; digital transformation for more sustainable supply chains; data-driven applications in smart manufacturing and logistics systems; data-driven services: characteristics, trends and applications; the future of lean thinking and practice; digital lean manufacturing and its emerging practices; new reconfigurable, flexible or agile production systems in the era of industry 4.0; operations management in engineer-to-order manufacturing; production management in food supply chains; gastronomic service system design; product and asset life cycle management in the circular economy; and production ramp-up strategies for product
R 3,106.67
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 7,580.72
Data Analytics and Digital Transformation
Understanding the significance of data analytics is paramount for digital transformation but in many organisations they are separate units without fully aligned goals. As organisations are applying digital transformations to be adaptive and agile in a competitive environment, data analytics can play a critical role for their success. This book explores the crossroads between them and how to leverage their connection for improved business outcomes. The need to collaborate and share data is becoming an integral part of digital transformation. This creates new opportunities but also requires well considered and continuously assessed decision making as competitiveness is at stake. This book details approaches, concepts and frameworks, as well as actionable insights and good practices, including combined data management and agile concepts. Critical issues are discussed such as data quality and data governance, as well as compliance, privacy, and ethics. It also offers insights into how both private and public organisations can innovate and keep up with growing data volumes and increasing technological developments in the short, mid and long term. This book will be of direct appeal to global researchers and students across a range of business disciplines, including technology and innovation management, organisational studies and strategic management. It is also relevant for policy makers, regulators and executives of private and public organisations looking to implement successful transformation policies.
R 2,478.15
Data Analytics
If you are a manager who receives the results of any data analyst’s work to help with your decision-making, this book is for you. Anyone playing a role in the field of analytics can benefit from this book as well. In the two decades the editors of this book spent teaching and consulting in the field of analytics, they noticed a critical shortcoming in the communication abilities of many analytics professionals. Specifically, analysts have difficulty in articulating in business terms what their analyses showed and what actionable recommendations were made. When analysts made presentations, they tended to lapse into the technicalities of mathematical procedures, rather than focusing on the strategic and tactical impact and meaning of their work. As analytics has become more mainstream and widespread in organizations, this problem has grown more acute.Data Analytics: Effective Methods for Presenting Results tackles this issue. The editors have used their experience as presenters and audience members who have become lost during presentation. Over the years, they experimented with different ways of presenting analytics work to make a more compelling case to top managers. They have discovered tried and true methods for improving presentations, which they share. The book also presents insights from other analysts and managers who share their own experiences. It is truly a collection of experiences and insight from academics and professionals involved with analytics.The book is not a primer on how to draw the most beautiful charts and graphs or about how to perform any specific kind of analysis. Rather, it shares the experiences of professionals in various industries about how they present their analytics results effectively. They tell their stories on how to win over audiences. The book spans multiple functional areas within a business, and in some cases, it discusses how to adapt presentations to the needs of audiences at different levels of management.
R 2,820.20
Data Analytics in Project Management
This book aims to help the reader better understand the importance of data analysis in project management. Moreover, it provides guidance by showing tools, methods, techniques and lessons learned on how to better utilize the data gathered from the projects. First and foremost, insight into the bridge between data analytics and project management aids practitioners looking for ways to maximize the practical value of data procured. The book equips organizations with the know-how necessary to adapt to a changing workplace dynamic through key lessons learned from past ventures. The book’s integrated approach to investigating both fields enhances the value of research findings.
R 5,831.92
Predictive Analytics in Human Resource Management
This volume is a step-by-step guide to implementing predictive data analytics in human resource management (HRM). It demonstrates how to apply and predict various HR outcomes which have an organisational impact, to aid in strategising and better decision-making.The book:Presents key concepts and expands on the need and role of HR analytics in business management.Utilises popular analytical tools like artificial neural networks (ANNs) and K-nearest neighbour (KNN) to provide practical demonstrations through R scripts for predicting turnover and applicant screening.Discusses real-world corporate examples and employee data collected first-hand by the authors.Includes individual chapter exercises and case studies for students and teachers.Comprehensive and accessible, this guide will be useful for students, teachers, and researchers of data analytics, Big Data, human resource management, statistics, and economics. It will also be of interest to readers interested in learning more about statistics or programming.
R 9,941.73
R 4,301.00
Data Analytics, Computational Statistics, and Operations Research for Engineers
With the rapidly advancing fields of Data Analytics and Computational Statistics, it’s important to keep up with current trends, methodologies, and applications. This book investigates the role of data mining in computational statistics for machine learning. It offers applications that can be used in various domains and examines the role of transformation functions in optimizing problem statements. Data Analytics, Computational Statistics, and Operations Research for Engineers: Methodologies and Applications presents applications of computationally intensive methods, inference techniques, and survival analysis models. It discusses how data mining extracts information and how machine learning improves the computational model based on the new information. Those interested in this reference work will include students, professionals, and researchers working in the areas of data mining, computational statistics, operations research, and machine learning.
R 2,954.90
