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High-Performance Modelling and Simulation for Big Data Applications
This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project.Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources.On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains.It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications.
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Geospatial Data Analytics on AWS
Build an end-to-end geospatial data lake in AWS using popular AWS services such as RDS, Redshift, DynamoDB, and Athena to manage geodata Purchase of the print or Kindle book includes a free PDF eBook.Key FeaturesExplore the architecture and different use cases to build and manage geospatial data lakes in AWSDiscover how to leverage AWS purpose-built databases to store and analyze geospatial dataLearn how to recognize which anti-patterns to avoid when managing geospatial data in the cloudBook DescriptionManaging geospatial data and building location-based applications in the cloud can be a daunting task. This comprehensive guide helps you overcome this challenge by presenting the concept of working with geospatial data in the cloud in an easy-to-understand way, along with teaching you how to design and build data lake architecture in AWS for geospatial data.You’ll begin by exploring the use of AWS databases like Redshift and Aurora PostgreSQL for storing and analyzing geospatial data. Next, you’ll leverage services such as DynamoDB and Athena, which offer powerful built-in geospatial functions for indexing and querying geospatial data. The book is filled with practical examples to illustrate the benefits of managing geospatial data in the cloud. As you advance, you’ll discover how to analyze and visualize data using Python and R, and utilize QuickSight to share derived insights. The concluding chapters explore the integration of commonly used platforms like Open Data on AWS, OpenStreetMap, and ArcGIS with AWS to enable you to optimize efficiency and provide a supportive community for continuous learning.By the end of this book, you’ll have the necessary tools and expertise to build and manage your own geospatial data lake on AWS, along with the knowledge needed to tackle geospatial data management challenges and make the most of AWS services.What you will learnDiscover how to optimize the cloud to store your geospatial dataExplore management strategies for your data repository using AWS Single Sign-On and IAMCreate effective SQL queries against your geospatial data using AthenaValidate postal addresses using Amazon Location servicesProcess structured and unstructured geospatial data efficiently using RUse Amazon SageMaker to enable machine learning features in your applicationExplore the free and subscription satellite imagery data available for use in your GISWho this book is forIf you understand the importance of accurate coordinates, but not necessarily the cloud, then this book is for you. This book is best suited for GIS developers, GIS analysts, data analysts, and data scientists looking to enhance their solutions with geospatial data for cloud-centric applications. A basic understanding of geographic concepts is suggested, but no experience with the cloud is necessary for understanding the concepts in this book.
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Big-Data Analytics for Cloud, IoT and Cognitive Computing
The definitive guide to successfully integrating social, mobile, Big-Data analytics, cloud and IoT principles and technologies The main goal of this book is to spur the development of effective big-data computing operations on smart clouds that are fully supported by IoT sensing, machine learning and analytics systems. To that end, the authors draw upon their original research and proven track record in the field to describe a practical approach integrating big-data theories, cloud design principles, Internet of Things (IoT) sensing, machine learning, data analytics and Hadoop and Spark programming. Part 1 focuses on data science, the roles of clouds and IoT devices and frameworks for big-data computing. Big data analytics and cognitive machine learning, as well as cloud architecture, IoT and cognitive systems are explored, and mobile cloud-IoT-interaction frameworks are illustrated with concrete system design examples. Part 2 is devoted to the principles of and algorithms for machine learning, data analytics and deep learning in big data applications. Part 3 concentrates on cloud programming software libraries from MapReduce to Hadoop, Spark and TensorFlow and describes business, educational, healthcare and social media applications for those tools. The first book describing a practical approach to integrating social, mobile, analytics, cloud and IoT (SMACT) principles and technologiesCovers theory and computing techniques and technologies, making it suitable for use in both computer science and electrical engineering programsOffers an extremely well-informed vision of future intelligent and cognitive computing environments integrating SMACT technologiesFully illustrated throughout with examples, figures and approximately 150 problems to support and reinforce learningFeatures a companion website with an instructor manual and PowerPoint slides www.wiley.com/go/hwangIOT Big-Data Analytics for Cloud, IoT and Cognitive Computing satisfies the demand among university faculty and students for cutting-edge information on emerging intelligent and cognitive computing systems and technologies. Professionals working in data science, cloud computing and IoT applications will also find this book to be an extremely useful working resource.
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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.
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Security, Data Analytics, and Energy-Aware Solutions in the IoT
The recent rapid proliferation in hardware, software, and communication technologies has facilitated the spread of interconnected sensors, actuators, and heterogeneous devices to collect data for offering a new class of advanced services characterized by being available anywhere, at any time and for anyone. This ecosystem is referred to as the Internet of Things (IoT). IoT networks collect, store, and exchange a large amount of heterogeneous data. It has already shown promising outcomes in the provisioning of potentially critical services (e.g., safety applications, healthcare, manufacturing), but it raises many challenges related to the security, data analysis, and limited resources of the performed operations and provided services. Accordingly, research on the data analysis and security of IoT is attractive to both industry and academia. Thus, the central theme of this book is to report novel methodologies, theories, technologies, techniques, and solutions for security and data analytics techniques and energy-aware solutions for IoT.This book aims at addressing these topics across multiple abstraction levels, ranging from architectural models, the provisioning of services, protocols, and interfaces to specific implementation approaches. Furthermore, additional focus will be given to areas related to the role of artificial intelligence in modeling and deploying secure and trustworthy sensors, actuators, and wireless networks for IoT systems. It aims to present the most start-of-the-art technologies to overcome the challenges related to security, data analytics, and energy-aware solutions in the Internet of Things.
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Operations Management for Healthcare Organizations
Varying factors such as the aging of the population, the increasing burden of chronic conditions and the recent global pandemic have highlighted the need for a redesign of healthcare production processes. This book provides a useful framework to capture the necessary organizational conditions to successfully implement operations strategies within healthcare organizations. The Operations Management (OM) function has become crucial; in fact, it is essential for managing the flow of both patients and supplies, in an efficient, responsive and flexible manner. The book outlines the models and operational solutions to these two key areas that characterize OM in healthcare: patient flow logistics, with the goal of optimizing the entire production cycle from first access by the patient to discharge and follow-up; and supply chain management, with the goal of ensuring that resources are successfully managed throughout the production lifecycle. The examples and case studies included in the book are based on the experience of the author who has had the opportunity to do research and training in the area of operations management, within different types of healthcare delivery organizations at both the national and international level, and often at publicly owned institutions. The book is a useful guide for students, managers and policy makers interested in the development and implementation of the OM function in healthcare delivery organizations.
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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..
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BIM and Big Data for Construction Cost Management
This book is designed to help practitioners and students in a wide range of construction project management professions to understand what building information modelling (BIM) and big data could mean for them and how they should prepare to work successfully on BIM-compliant projects and maintain their competencies in this essential and expanding area. In this book, the state-of-the-art information technologies that support high-profile BIM implementation are introduced, and case studies show how BIM has integrated core quantity surveying and cost management responsibilities and how big data can enable informed decision-making for cost control and cost planning. The authors' combined professional and academic experience demonstrates, with practical examples, the importance of using BIM and particularly the fusion of BIM and big data, to sharpen competitiveness in global and domestic markets. This book is a highly valuable guide for people in a wide range of construction project management and quantity surveying roles. In addition, implications for project management, facilities management, contract administration, and dispute resolution are also explored through the case studies, making this book essential reading for built environment and engineering professionals.
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Patient-Centric Analytics in Health Care
In Patient-Centric Analytics in Health Care: Driving Value in Clinical Settings and Psychological Practice, James J. Gillespie and Gregory J. Privitera introduce a framework that explores the utility of analytics for managing care that is based on six key inputs of the health care system: patients, policy makers, providers, pharmacies, pharmaceuticals, and payers. Understanding the roles of these 6 P’s and the utility of analytics to promote data-driven decision models can lead to new innovations. These improvements can enhance quality, increase access, and reduce costs, and thereby drive value for the most important stakeholders in health care: the patients. As the accessibility and volume of data continues to increase, there is a growing desire to utilize data to guide and optimize decision-making in health care environments. There is a wealth of data in health care organizations and much of it is not fully utilized. In today’s climate, these organizations are under increased regulatory and financial pressures to deliver measurable value, particularly as it relates to the quality of patient care in clinical and diagnostic settings. This book includes short contributions from practitioners, including Laurie Branch, Puneet Chahal, Patrick C. Cunningham, Star* Cunningham, Matthew Dreckmeier, Joseph P. Gaspero, Sherri Matis-Mitchell, Gail Mayeaux, Edwin K. Morris, Plamen Petrov, Steven Press, Andrew J. Privitera, Derek Walton, and Daniel Yunker.
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Causal Models and Intelligent Data Management
The need to electronically store, manipulate and analyze large-scale, high-dimensional data sets requires new computational methods. This book presents new intelligent data management methods and tools, including new results from the field of inference. Leading experts also map out future directions of intelligent data analysis. This book will be a valuable reference for researchers exploring the interdisciplinary area between statistics and computer science as well as for professionals applying advanced data analysis methods in industry.
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Healthcare Data Analytics
At the intersection of computer science and healthcare, data analytics has emerged as a promising tool for solving problems across many healthcare-related disciplines. Supplying a comprehensive overview of recent healthcare analytics research, Healthcare Data Analytics provides a clear understanding of the analytical techniques currently available to solve healthcare problems.The book details novel techniques for acquiring, handling, retrieving, and making best use of healthcare data. It analyzes recent developments in healthcare computing and discusses emerging technologies that can help improve the health and well-being of patients.Written by prominent researchers and experts working in the healthcare domain, the book sheds light on many of the computational challenges in the field of medical informatics. Each chapter in the book is structured as a "survey-style" article discussing the prominent research issues and the advances made on that research topic. The book is divided into three major categories: Healthcare Data Sources and Basic Analytics - details the various healthcare data sources and analytical techniques used in the processing and analysis of such data Advanced Data Analytics for Healthcare - covers advanced analytical methods, including clinical prediction models, temporal pattern mining methods, and visual analytics Applications and Practical Systems for Healthcare - covers the applications of data analytics to pervasive healthcare, fraud detection, and drug discovery along with systems for medical imaging and decision supportComputer scientists are usually not trained in domain-specific medical concepts, whereas medical practitioners and researchers have limited exposure to the data analytics area. The contents of this book will help to bring together these diverse communities by carefully and comprehensively discussing the most relevant contributions from each domain.
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Earth Observation Data Analytics Using Machine and Deep Learning
Earth Observation Data Analytics Using Machine and Deep Learning: Modern tools, applications and challenges covers the basic properties, features and models for Earth observation (EO) recorded by very high-resolution (VHR) multispectral, hyperspectral, synthetic aperture radar (SAR), and multi-temporal observations. Approaches for applying pre-processing methods and deep learning techniques to satellite images for various applications - such as identifying land cover features, object detection, crop classification, target recognition, and the monitoring of earth resources - are described. Cost-efficient resource allocation solutions are provided, which are robust against common uncertainties that occur in annotating and extracting features on satellite images. This book is a valuable resource for engineers and researchers in academia and industry working on AI, machine and deep learning, data science, remote sensing, GIS, SAR, satellite communications, space science, image processing and computer vision. It will also be of interest to staff at research agencies, lecturers and advanced students in related fields. Readers will need a basic understanding of computing, remote sensing, GIS and image interpretation.
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Excel Data Analysis
This book offers a comprehensive and readable introduction to modern business and data analytics. It is based on the use of Excel, a tool that virtually all students and professionals have access to. The explanations are focused on understanding the techniques and their proper application, and are supplemented by a wealth of in-chapter and end-of-chapter exercises. In addition to the general statistical methods, the book also includes Monte Carlo simulation and optimization. The second edition has been thoroughly revised: new topics, exercises and examples have been added, and the readability has been further improved. The book is primarily intended for students in business, economics and government, as well as professionals, who need a more rigorous introduction to business and data analytics – yet also need to learn the topic quickly and without overly academic explanations.
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Sustainable Development Through Data Analytics and Innovation
Sustainable development is based on the idea that societies should advance without compromising their future development requirements. This book explores how the application of data analytics and digital technologies can ensure that development changes are executed on the basis of factual data and information. It addresses how innovations that rely on digital technologies can support sustainable development across all sectors and all social, economic, and environmental aspects and help us achieve the Sustainable Development Goals (SDGs). The book also highlights techniques, processes, models, tools, and practices used to achieve sustainable development through data analysis.The various topics covered in this book are critically evaluated, not only theoretically, but also from an application perspective. It will be of interest to researchers and students, especially those in the fields of applied data analytics, business intelligence and knowledge management.
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WCOM (World Class Operations Management)
This book deals with World Class Operations Management (WCOM), detailing its principles, methods and organisation, and the results that this approach can bring about. Utilising real-world case studies illustrated by companies that have adopted this model (interviews with Saint-Gobain, L’Oréal, Tetra Pak, Bemis, and Bel Executives), it describes common patterns drawn from decades of hands-on experience, so as to present a theoretical approach together with the concrete application of its principles. WCOM, adopted by several multinational companies, is one of the more innovative management practises, as it integrates the best Continuous Improvement approaches (Lean, Total Productive Management, World Class Manufacturing) as well as the most innovative approaches in human dynamics like Change Leadership, Performance Behavior, Shingo Model, to name a few. Every book’s chapter has been authored by an expert in these different fields, thus revealing the synergy among the different practices, which is one of the distinguishing and successful aspects of WCOM Maximising reader insights into the successful implementation of such an approach, and explaining not only its potentialities, but also its implementation dynamics, the critical points and the ways it can be integrated into different situations, this book is also about how to create a culture of excellence that is sustainable over a long period of time and delivers consistent (or ever-improving) results.
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Schaum's Outline of Operations Management
Confusing Textbooks? Missed Lectures? Not Enough Time?Fortunately for you, there's Schaum's Outlines. More than 40 million students have trusted Schaum's to help them succeed in the classroom and on exams. Schaum's is the key to faster learning and higher grades in every subject. Each Outline presents all the essential course information in an easy-to-follow, topic-by-topic format. You also get hundreds of examples, solved problems, and practice exercises to test your skills. This Schaum's Outline gives youPractice problems with full explanations that reinforce knowledgeCoverage of the most up-to-date developments in your course fieldIn-depth review of practices and applicationsFully compatible with your classroom text, Schaum's highlights all the important facts you need to know. Use Schaum's to shorten your study time-and get your best test scores!Schaum's Outlines-Problem Solved.
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Analytics Engineering with SQL and Dbt
With the shift from data warehouses to data lakes, data now lands in repositories before it's been transformed, enabling engineers to model raw data into clean, well-defined datasets. DBT (data build tool) helps you take data further. This practical book shows data analysts, data engineers, BI developers, and data scientists how to create a true self-service transformation platform through the use of dynamic SQL.Authors Rui Machado from Monstarlab and Helder Russa from Jumia show you how to quickly deliver new data products by focusing more on value delivery and less on architectural and engineering aspects. If you know your business well and have the technical skills to model raw data into clean, well-defined datasets, you'll learn how to design and deliver data models without any technical influence.With this book, you'll learn:What DBT is and how a DBT project is structuredHow DBT fits into the data engineering and analytics worldsHow to collaborate on building data modelsThe main tools and architectures for building useful, functional data modelsHow to fit DBT into data warehousing and laking architectureHow to build tests for data transformations
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ISE Matching Supply with Demand: An Introduction to Operations Management
Cachon Matching Supply with Demand, 4e is a clear, concise and more rigorous approach to an introductory Operations management course. Written by Wharton authors who use their guiding principles “real operations, real solutions” to bring the text and concepts to life, writing the majority of chapters from the perspective of specific companies. The “real solutions” refers to providing students with tools and strategies they can implement in practice and apply the author's models in a realistic operational setting. The authors strive for “real simple” by using as little mathematical notation as possible, focusing on many real-world examples and consistent terminology and phrasing throughout.
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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.
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Specification and Analytical Evaluation of Heterogeneous Dynamic Quorum-Based Data Replication Schemes
Data replication by employing quorum systems is an important concept to improve operation availability on data objects in distributed systems that have strong data consistency demands. These data replication schemes must be modeled and carefully evaluated with respect to different quality measures. Christian Storm addresses the former by a uniform data replication scheme specification method and realizes the latter by a comprehensive approach to the analytical evaluation of quorum-based data replication schemes. The system model allows to evaluate operation availability and other quality measures for the write as well as for the read operation.
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Active Operations Management
If you’re at the sharp end of management, juggling conflicting demands to keep your organization’s promises to customers, with ever-reducing resources whilst implementing the latest digital change programme and keeping your team happy – this is the book for you. Active Operations Management gives you the framework and tools to help you take control and make the most of the evolving world of service operations. The challenges of robotics, remote working and lean operations demand a new approach to give people and organizations the confidence they need to thrive and deliver in the agile age.This ground-breaking playbook specifically addresses the practical needs of operations managers. Discover:The four critical activities for effective control, and the daily and weekly rhythms which make them effective and sustainable.Practical measures of work and performance which enable like-for-like comparisons and resource balancing across diverse teams.Real-world examples showing you how to raise productivity, improve staff engagement and wellbeing.Online resources which support your control of the truly agile operation.Neil Bentley and Richard Jeffery have spent over 30 years working with operations managers to simplify and bring structure to the challenges of managing in complex organizations. Today their Active Operations Management methodology is used by thousands of managers, raising performance and ensuring the wellbeing of those involved.
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Foundations of Programming, Statistics, and Machine Learning for Business Analytics
Business Analysts and Data Scientists are in huge demand, as global companies seek to digitally transform themselves and leverage their data resources to realize competitive advantage.This book covers all the fundamentals, from statistics to programming to business applications, to equip you with the solid foundational knowledge needed to progress in business analytics. Assuming no prior knowledge of programming or statistics, this book takes a simple step-by-step approach which makes potentially intimidating topics easy to understand, by keeping Maths to a minimum and including examples of business analytics in practice.Key features:· Introduces programming fundamentals using R and Python· Covers data structures, data management and manipulation and data visualization· Includes interactive coding notebooks so that you can build up your programming skills progressivelySuitable as an essential text for undergraduate and postgraduate students studying Business Analytics or as pre-reading for students studying Data Science.Ram Gopal is Pro-Dean and Professor of Information Systems at the University of Warwick.Daniel Philps is an Artificial Intelligence Researcher and Head of Rothko Investment Strategies.Tillman Weyde is Senior Lecturer at City, University of London.
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Data Analytics and AI
Analytics and artificial intelligence (AI), what are they good for? The bandwagon keeps answering, absolutely everything! Analytics and artificial intelligence have captured the attention of everyone from top executives to the person in the street. While these disciplines have a relatively long history, within the last ten or so years they have exploded into corporate business and public consciousness. Organizations have rushed to embrace data-driven decision making. Companies everywhere are turning out products boasting that "artificial intelligence is included." We are indeed living in exciting times. The question we need to ask is, do we really know how to get business value from these exciting tools?Unfortunately, both the analytics and AI communities have not done a great job in collaborating and communicating with each other to build the necessary synergies. This book bridges the gap between these two critical fields. The book begins by explaining the commonalities and differences in the fields of data science, artificial intelligence, and autonomy by giving a historical perspective for each of these fields, followed by exploration of common technologies and current trends in each field. The book also readers introduces to applications of deep learning in industry with an overview of deep learning and its key architectures, as well as a survey and discussion of the main applications of deep learning. The book also presents case studies to illustrate applications of AI and analytics. These include a case study from the healthcare industry and an investigation of a digital transformation enabled by AI and analytics transforming a product-oriented company into one delivering solutions and services. The book concludes with a proposed AI-informed data analytics life cycle to be applied to unstructured data.
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Big Data Governance and Perspectives in Knowledge Management
The world is witnessing the growth of a global movement facilitated by technology and social media. Fueled by information, this movement contains enormous potential to create more accountable, efficient, responsive, and effective governments and businesses, as well as spurring economic growth.Big Data Governance and Perspectives in Knowledge Management is a collection of innovative research on the methods and applications of applying robust processes around data, and aligning organizations and skillsets around those processes. Highlighting a range of topics including data analytics, prediction analysis, and software development, this book is ideally designed for academicians, researchers, information science professionals, software developers, computer engineers, graduate-level computer science students, policymakers, and managers seeking current research on the convergence of big data and information governance as two major trends in information management.
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Data Analytics: Paving the Way to Sustainable Urban Mobility
This book aims at showing how big data sources and data analytics can play an important role in sustainable mobility. It is especially intended to provide academicians, researchers, practitioners and decision makers with a snapshot of methods that can be effectively used to improve urban mobility. The different chapters, which report on contributions presented at the 4th Conference on Sustainable Urban Mobility, held on May 24-25, 2018, in Skiathos Island, Greece, cover different thematic areas, such as social networks and traveler behavior, applications of big data technologies in transportation and analytics, transport infrastructure and traffic management, transportation modeling, vehicle emissions and environmental impacts, public transport and demand responsive systems, intermodal interchanges, smart city logistics systems, data security and associated legal aspects. They show in particular how to apply big data in improving urban mobility, discuss important challenges in developing and implementing analytics methods and provide the reader with an up-to-date review of the most representative research on data management techniques for enabling sustainable urban mobility
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Healthcare Data Analytics
At the intersection of computer science and healthcare, data analytics has emerged as a promising tool for solving problems across many healthcare-related disciplines. Supplying a comprehensive overview of recent healthcare analytics research, Healthcare Data Analytics provides a clear understanding of the analytical techniques currently available to solve healthcare problems.The book details novel techniques for acquiring, handling, retrieving, and making best use of healthcare data. It analyzes recent developments in healthcare computing and discusses emerging technologies that can help improve the health and well-being of patients.Written by prominent researchers and experts working in the healthcare domain, the book sheds light on many of the computational challenges in the field of medical informatics. Each chapter in the book is structured as a "survey-style" article discussing the prominent research issues and the advances made on that research topic. The book is divided into three major categories: Healthcare Data Sources and Basic Analytics - details the various healthcare data sources and analytical techniques used in the processing and analysis of such data Advanced Data Analytics for Healthcare - covers advanced analytical methods, including clinical prediction models, temporal pattern mining methods, and visual analytics Applications and Practical Systems for Healthcare - covers the applications of data analytics to pervasive healthcare, fraud detection, and drug discovery along with systems for medical imaging and decision supportComputer scientists are usually not trained in domain-specific medical concepts, whereas medical practitioners and researchers have limited exposure to the data analytics area. The contents of this book will help to bring together these diverse communities by carefully and comprehensively discussing the most relevant contributions from each domain.
R 2,274.80
Knowledge Discovery And Data Design Innovation - Proceedings Of The International Conference On Knowledge Management (Ickm 2017)
To navigate the complex ecosystem of societal challenges, the International Conference on Knowledge Management Conference (ICKM 2017) focused on big data and data analytics as part of the relationship to the wider concept of knowledge management processes and practices.This book includes top papers presenting the major, and diverse, topics discussed at the conference. The papers covered various aspects of big data ranging from enhancing access to the big data to facilitating its wide applications in healthcare, social media, library and information centers, governments, and corporations.
R 6,725.18
Risk Modeling, Assessment, and Management
Presents systems-based theory, methodology, and applications in risk modeling, assessment, and management This book examines risk analysis, focusing on quantifying risk and constructing probabilities for real-world decision-making, including engineering, design, technology, institutions, organizations, and policy. The author presents fundamental concepts (hierarchical holographic modeling; state space; decision analysis; multi-objective trade-off analysis) as well as advanced material (extreme events and the partitioned multi-objective risk method; multi-objective decision trees; multi-objective risk impact analysis method; guiding principles in risk analysis); avoids higher mathematics whenever possible; and reinforces the material with examples and case studies. The book will be used in systems engineering, enterprise risk management, engineering management, industrial engineering, civil engineering, and operations research. The fourth edition of Risk Modeling, Assessment, and Management features: Expanded chapters on systems-based guiding principles for risk modeling, planning, assessment, management, and communication; modeling interdependent and interconnected complex systems of systems with phantom system models; and hierarchical holographic modelingAn expanded appendix including a Bayesian analysis for the prediction of chemical carcinogenicity, and the Farmer’s Dilemma formulated and solved using a deterministic linear modelUpdated case studies including a new case study on sequential Pareto-optimal decisions for emergent complex systems of systemsA new companion website with over 200 solved exercises that feature risk analysis theories, methodologies, and application Risk Modeling, Assessment, and Management, Fourth Edition, is written for both undergraduate and graduate students in systems engineering and systems management courses. The text also serves as a resource for academic, industry, and government professionals in the fields of homeland and cyber security, healthcare, physical infrastructure systems, engineering, business, and more.
R 5,325.00
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
R 824.00
Manage Your Own Learning Analytics
This book sheds light on the practice of learning analytics, illuminating how others approach their data analysis. At the beginning of the book, a ‘prescriptive learning analytics planning model’ gives straightforward instructions for people to follow. This book is organized into ten chapters, falling into four topical sections: Managing Learning Analytics (overview, instructional systems design (ISD), instructional design, and planning data analysis); Cognitive Performance Measurement Practices (classical test theory (CTT), Rasch measurement theory (RMT), Item response theory(IRT), Rasch Modeling Tools (research design, setting methodology); and Case Studies (corporate training settings, healthcare industry, and educational courseware design). This book is an important reference for: educational research community and instructional systems designers; corporate training developers; postgraduate course developers; and doctoral students.
R 5,152.31
Analytics Engineering with Microsoft Fabric and Power BI
While Microsoft Power BI has dominated the business intelligence market for years and is a go-to tool for creating visually appealing, interactive reports and dashboards, it's now an integral part of Microsoft Fabric, the end-to-end analytics platform that offers unprecedented flexibility and scalability for building enterprise-grade data analytics solutions.This book covers everything analytics engineers need to know to design and implement robust and efficient analytics solutions using Microsoft Fabric and Power BI. You'll learn the core components of Fabric, such as lakehouses, warehouses, and eventhouses, and how to work with semantic models, ensuring that data is structured and ready for analysis. You'll also discover essential techniques in both Microsoft Fabric and Power BI that you can apply in your day-to-day work.Explore the core components of Microsoft FabricImplement, manage, and optimize Power BI semantic modelsDiscover numerous architectural solutions with Microsoft Fabric and Power BIBuild Fabric items such as lakehouses, warehouses, semantic models, and more, and share them within your organizationIdentify when to use a particular Fabric item or implement a particular design patternImplement the analytics development lifecycleOptimize and fine-tune existing analytics solutions
R 1,731.00
Operations Research Proceedings 2024
This book contains a selection of peer-reviewed papers presented at the International Conference on Operations Research (OR 2024), held at the Technical University of Munich, Germany, from September 3 to 6, 2024. Over 650 scientists from all over the world attended the OR 2024 in Munich. Three plenaries and nine semi-plenaries covered theoretical aspects of Operations Research, applications, and real-world practices. In addition, more than 500 presentations were held over three days in up to 23 parallel sessions. Covering a wide range of topics, the book highlights cutting-edge research and practical applications across various domains of modern operations research. It places a special emphasis on the theme "Data, Learning, and Optimization", exploring how data-driven approaches, machine learning techniques, and optimization strategies are shaping decision-making processes and operational efficiency. The contributions in this book reflect the diversity and innovation in the field, making it a valuable resource for researchers, practitioners, and academics alike.
R 9,158.00
Essential Data Analytics, Data Science, and AI
In today’s world, understanding data analytics, data science, and artificial intelligence is not just an advantage but a necessity. This book is your thorough guide to learning these innovative fields, designed to make the learning practical and engaging.The book starts by introducing data analytics, data science, and artificial intelligence. It illustrates real-world applications, and, it addresses the ethical considerations tied to AI. It also explores ways to gain data for practice and real-world scenarios, including the concept of synthetic data. Next, it uncovers Extract, Transform, Load (ETL) processes and explains how to implement them using Python. Further, it covers artificial intelligence and the pivotal role played by machine learning models. It explains feature engineering, the distinction between algorithms and models, and how to harness their power to make predictions. Moving forward, it discusses how to assess machine learning models after their creation, with insights into various evaluation techniques. It emphasizes the crucial aspects of model deployment, including the pros and cons of on-device versus cloud-based solutions. It concludes with real-world examples and encourages embracing AI while dispelling fears, and fostering an appreciation for the transformative potential of these technologies.Whether you’re a beginner or an experienced professional, this book offers valuable insights that will expand your horizons in the world of data and AI.What you will learn:What are Synthetic data and Telemetry dataHow to analyze data using programming languages like Python and Tableau.What is feature engineeringWhat are the practical Implications of Artificial IntelligenceWho this book is for:Data analysts, scientists, and engineers seeking to enhance their skills, explore advanced concepts, and stay up-to-date with ethics. Business leaders and decision-makers across industries are interested in understanding the transformative potential and ethical implications of data analytics and AI in their organizations.
R 1,254.00
Big Data Analytics
While the term Big Data is open to varying interpretation, it is quite clear that the Volume, Velocity, and Variety (3Vs) of data have impacted every aspect of computational science and its applications. The volume of data is increasing at a phenomenal rate and a majority of it is unstructured. With big data, the volume is so large that processing it using traditional database and software techniques is difficult, if not impossible. The drivers are the ubiquitous sensors, devices, social networks and the all-pervasive web. Scientists are increasingly looking to derive insights from the massive quantity of data to create new knowledge. In common usage, Big Data has come to refer simply to the use of predictive analytics or other certain advanced methods to extract value from data, without any required magnitude thereon. Challenges include analysis, capture, curation, search, sharing, storage, transfer, visualization, and information privacy. While there are challenges, there are huge opportunities emerging in the fields of Machine Learning, Data Mining, Statistics, Human-Computer Interfaces and Distributed Systems to address ways to analyze and reason with this data. The edited volume focuses on the challenges and opportunities posed by "Big Data" in a variety of domains and how statistical techniques and innovative algorithms can help glean insights and accelerate discovery. Big data has the potential to help companies improve operations and make faster, more intelligent decisions.
R 7,356.00
Industrial Engineering and Operations Management
This proceedings gathers selected, peer-reviewed papers presented at the XXX International Joint Conference on Industrial Engineering and Operations Management (IJCIEOM), held from June 26 to 28, 2024, in Salvador, Brazil. The works in this volume explores critical areas such as Supply Chain risk models, last-mile delivery optimization, stochastic inventory models, and human development focusing on digital training for operations management in emergencies.Tailored to benefit academics, the volume comprises studies predominantly rooted in real-world case studies, systematic, and meta-reviews, offering valuable insights also for professionals within the industrial sector by presenting solutions to intricate industrial challenges.
R 11,897.35
R 1,824.00
Simulation of Recreational Use for Park and Wilderness Management
First Published in 2011. This book describes the application of an advanced analytical technique, simulation modeling, (WUSM) to a significant problem in resources management. It includes ideas which have grown out of practical resource management problems that have progressed through conceptual models to operational tools and finally to application in actual public land management settings. It is similarly rewarding to see the work being adapted for use by the National Park Service and other agencies at home and abroad.
R 5,635.23
