Search results for "Operations Management and Data Analytics Modelling"
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Management of Digital EcoSystems
This book constitutes the proceedings of the 16th International Conference on Management of Digital EcoSystems, MEDES 2024, held in Naples, Italy, during November 2024. The 23 full papers and 10 Short papers included in this book were carefully reviewed and selected from 65 submissions. They were organized in topical sections as follows: Energy and Environment; Recommendation Systems; Social Networks and Emotion Recognition; Medical and Health Issues; Cybersecurity and Privacy; Data Mining Operations; Parallel and Distributed Systems; Network Aspects, and Applications.
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Information Technologies and Mathematical Modelling. Queueing Theory and Applications
This book constitutes the refereed proceedings of the 21st International Conference on Information Technologies and Mathematical Modelling. Queueing Theory and Applications, ITMM 2022, held in Karshi, Uzbekistan, during October 25–29, 2022. The 19 full papers included in this book were carefully reviewed and selected from 89 submissions. The papers are devoted to new results in queueing theory and its applications. Its target audience includes specialists in probabilistic theory, random processes, mathematical modeling as well as engineers engaged in logical and technical design and operational management of data processing systems, communication, and computer networks.
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Data Engineering for Data Science
This open access book aims to synthesize and integrate the research challenges in data science and data engineering. It offers a comprehensive survey of the entire data management stack, from scalable and explainable data analytics to traceable data workflows. By providing a consistent framework, it facilitates a thorough understanding of the data science lifecycle, from basic definitions to state-of-the-art concepts and techniques. The book is divided into four parts, each focusing on a different aspect of the data management and science lifecycle: governance, storage and processing, preparation, and analysis. Each part is organized to provide a coherent conceptual framework and is divided into multiple chapters, each focusing on a specific topic but together offering a comprehensive overview of the state of the art and the key challenges in the respective areas. While the parts and chapters follow a logical sequence, each chapter is designed to be self-contained and can be read independently. Chapters include references for further reading and deeper exploration, and often also provide concrete examples or use cases to make the material more accessible. In addition, many chapters introduce a taxonomy to break down complex research areas into manageable components, highlighting the core directions and developments within each domain.The book is designed to be a valuable resource for both researchers and practitioners seeking to leverage data engineering for data science applications. For both seasoned experts or budding professionals, it provides the tools and knowledge needed to stay at the forefront of data-driven advancements.
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Data Science for Marketing Analytics
Turbocharge your marketing plans by making the leap from simple descriptive statistics in Excel to sophisticated predictive analytics with the Python programming languageKey FeaturesUse data analytics and machine learning in a sales and marketing contextGain insights from data to make better business decisionsBuild your experience and confidence with realistic hands-on practiceBook DescriptionUnleash the power of data to reach your marketing goals with this practical guide to data science for business.This book will help you get started on your journey to becoming a master of marketing analytics with Python. You'll work with relevant datasets and build your practical skills by tackling engaging exercises and activities that simulate real-world market analysis projects.You'll learn to think like a data scientist, build your problem-solving skills, and discover how to look at data in new ways to deliver business insights and make intelligent data-driven decisions.As well as learning how to clean, explore, and visualize data, you'll implement machine learning algorithms and build models to make predictions. As you work through the book, you'll use Python tools to analyze sales, visualize advertising data, predict revenue, address customer churn, and implement customer segmentation to understand behavior.By the end of this book, you'll have the knowledge, skills, and confidence to implement data science and machine learning techniques to better understand your marketing data and improve your decision-making.What you will learnLoad, clean, and explore sales and marketing data using pandasForm and test hypotheses using real data sets and analytics toolsVisualize patterns in customer behavior using MatplotlibUse advanced machine learning models like random forest and SVMUse various unsupervised learning algorithms for customer segmentationUse supervised learning techniques for sales predictionEvaluate and compare different models to get the best outcomesOptimize models with hyperparameter tuning and SMOTEWho this book is forThis marketing book is for anyone who wants to learn how to use Python for cutting-edge marketing analytics. Whether you're a developer who wants to move into marketing, or a marketing analyst who wants to learn more sophisticated tools and techniques, this book will get you on the right path.Basic prior knowledge of Python and experience working with data will help you access this book more easily.
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Modelling and Performance Analysis of Cyclic Systems
Striking a balance between state-of-the-art research and practical applications, this book provides a forum for contributions that cover the main research challenges in the cyclic modeling, development, and validation of concurrently acting distributed production systems: systems that employ multi-assortment production in large quantities, are characterized by gradual changes in their product mix, and exclusively manufacture products in a cyclic manner. Similar issues also arise in computer systems, e.g., embedded systems. Cyclic optimization problems that occur in them are unique and under-researched, but are attracting new interest primarily due to their great practical importance and the difficulty involved in obtaining efficient algorithms for solving specific cases with real constraints arising from manufacturing practice. Addressing these and other topics, the book will be of great interest to researchers in computer science, operations management, and production control, as well as practicing managers and engineers.
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Database Safari
Pack your field kit—this is a guided safari through today’s database ecosystem. In the wild, one size rarely fits all: Modern systems often need specialized databases to deliver real‑world performance and scale. This book is a practical field guide to those less commonly used databases—vector, time‑series, graph, columnar/OLAP, geospatial, event‑stream, and more. It shows you how each engine works, where it excels, and how to choose the right engine for each workload. The book provides clear trade‑off tables, indexing strategies, query patterns, and use‑case recipes you can apply immediately.Starting with foundations in SQL and NoSQL, this book explores each “species” chapter by chapter, emphasizing hands‑on scenarios and pitfalls to avoid. It then brings everything together in polyglot/hybrid architectures, helping you combine engines confidently, whether you’re designing a similarity search with vector databases (ANN), building observability pipelines on time‑series data and event logs, modeling relationships in graph databases, or delivering analytics with columnar/OLAP and geospatial queries. The result is a concise, engineer‑first map of the database landscape that turns theory into pragmatic decision‑making.What You Will LearnIdentify the right engine for the job across vector, time‑series, graph, columnar/OLAP, geospatial, and event‑stream databasesEvaluate trade‑offs in performance, scalability, consistency, storage layout, indexing, and operational complexityDesign polyglot architectures that combine multiple engines (e.g., vector + OLAP + event‑stream) for end‑to‑end solutionsApply use‑case patterns for similarity search (ANN), observability metrics, fraud/network analysis, geospatial routing, and analytical reportingUnderstand core internals that matter (index types, retention policies, log‑structured storage, spatial indexes, columnar execution) without vendor lock‑inAvoid common pitfalls in schema design, query planning, and cross‑engine data movementBuild a decision checklist and trade‑off tables to justify engine selection to stakeholdersWho This Book Is ForSoftware engineers; back-end developers; data engineers; system architects; site reliability engineers (SREs); technical leads with basic SQL/NoSQL experience who need practical guidance to select, compare, and combine specialized databases for real‑world applications
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High-Performance Big-Data Analytics
This book presents a detailed review of high-performance computing infrastructures for next-generation big data and fast data analytics. Features: includes case studies and learning activities throughout the book and self-study exercises in every chapter; presents detailed case studies on social media analytics for intelligent businesses and on big data analytics (BDA) in the healthcare sector; describes the network infrastructure requirements for effective transfer of big data, and the storage infrastructure requirements of applications which generate big data; examines real-time analytics solutions; introduces in-database processing and in-memory analytics techniques for data mining; discusses the use of mainframes for handling real-time big data and the latest types of data management systems for BDA; provides information on the use of cluster, grid and cloud computing systems for BDA; reviews the peer-to-peer techniques and tools and the common information visualization techniques, used in BDA.
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Cloud Computing for Geospatial Big Data Analytics
This book introduces the latest research findings in cloud, edge, fog, and mist computing and their applications in various fields using geospatial data. It solves a number of problems of cloud computing and big data, such as scheduling, security issues using different techniques, which researchers from industry and academia have been attempting to solve in virtual environments. Some of these problems are of an intractable nature and so efficient technologies like fog, edge and mist computing play an important role in addressing these issues. By exploring emerging advances in cloud computing and big data analytics and their engineering applications, the book enables researchers to understand the mechanisms needed to implement cloud, edge, fog, and mist computing in their own endeavours, and motivates them to examine their own research findings and developments.
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Urban Analytics with Social Media Data
The use of data science and urban analytics has become a defining feature of smart cities. This timely book is a clear guide to the use of social media data for urban analytics.The book presents the foundations of urban analytics with social media data, along with real-world applications and insights on the platforms we use today. It looks at social media analytics platforms, cyberphysical data analytics platforms, crowd detection platforms, City-as-a-Platform, and city-as-a-sensor for platform urbanism. The book provides examples to illustrate how we apply and analyse social media data to determine disaster severity, assist authorities with pandemic policy, and capture public perception of smart cities.This will be a useful reference for those involved with and researching social, data, and urban analytics and informatics.
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Parts Management Models and Applications
Parts are commonly used in making, repairing or maintaining consumer or industry products. Parts could be purchased or manufactured by a business enterprise. Advance models were applied to parts operations for manufacturers of transport refrigeration equipment and high-pressure positive displacement plunger pumps. Both companies have an established network of dealers for sales and service of equipment and parts. A number of areas in the parts business operation were researched which show the potential for improved operational efficiency and customer service that increase market share when advanced process models were used to integrate the supply chain. This book covers the subject of parts management through: (1) an introduction to areas of parts business operation with potential for substantial improvements and overview of various models proposed in Chapter 1; (2) quantitative effects on customer service level of inventory miscount and lead time variability and methods to reduce these factors in Chapter 2; (3) optimal division of items based on economics within a two-level distribution system; which items should be serviced through dealers and which items directly by the company to end-customers in Chapters; (4) optimal ordering procedures for a multi-item common supplier system with either constant or random demand rates for various items in Chapter 4; Vll viii Parts Management Models and Applications (5) attribute based classification scheme to promote standardization of design and manufacturing techniques for expediting product development and control design proliferation in Chapters 5 and 6; (6) knowledge base management to enhance manufacturing operations effectiveness in Chapter 7;
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Entwicklung von Datenmodellen für ein objektorientiertes Engineering Data Management System zur Unterstützung von teamorientierten Organisationsformen
Die vorliegende Arbeit entstand während meiner Tätigkeit als Mitarbeiter der Fa. Prantner, Verfahrenstechnik GmbH, Reutlingen, und des Fraunhofer-lnstituts für Arbeitswirtschaft und Organisation (IAO), Stuttgart, im Rahmen der BMFT- Fördermaßnahme "Forschungskooperation zwischen Industrie und Wissenschaft", sowie als freier Mitarbeiter am IAO, Stuttgart. Meinen besonderen Dank gilt Herrn Prof. Dr.-lng. habil. Prof. E.h. Dr. h.c. Hans-Jörg Bullinger für seine großzügige Förderung, die entscheidend zur erfolgreichen Durchführung der Arbeit beigetragen hat. Herrn Prof. Dr.-lng. G. Lechner danke ich für die Übernahme des Koreferates und für seine zahlreichen wertvollen Hinweise, die sich daraus ergaben. Mein Dank gilt auch Herrn Dr.-lng. Joachim Warschat, der durch viele kritische Anregungen und stete Diskussionsbereitschaft einen wesentlichen Beitrag zu dieser Arbeit geleistet hat. Meinen besonderen Dank möchte ich Herrn Dr.-lng. Ralph Richter aussprechen, der mir als Ansprechpartner aus der Industrie relevante Hinweise auf die Praktikabilität meiner Arbeit gab. Darüber hinaus möchte ich allen Kollegen des IAO und den zahlreichen Studenten für ihre stete Hilfsbereitschaft, Unterstützung sowie ausgezeichnete Zusammenarbeit danken. Insbesondere möchte ich hier Herrn Dipl.-lng. Jürgen Matthes sowie Frau cand.-inform. Eva Neuwirth, Herrn cand.-inform. Jürgen Braun und Frau cand.-inform.
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Using Strategy Analytics to Measure Corporate Performance and Business Value Creation
Strategic analytics is a relatively new field in conjunction with strategic management and business intelligence. Generally, the strategic management field deals with the enhancement of the decision-making capabilities of managers. Typically, such decision-making processes are heavily dependent upon various internal and external reports. Managers need to develop their strategies using clear strategy processes supported by the increasing availability of data. This situation calls for a different approach to strategy, including integration with analytics, as the science of extracting value from data and structuring complex problems. Using Strategy Analytics to Measure Corporate Performance and Business Value Creation discusses how to tackle complex business dynamics using optimization techniques and modern business analytics tools. It covers not only introductory concepts of strategic analytics but also provides strategic analytics applications in each area of management such as market dynamics, customer analysis, operations, and people management. It unveils the best industry practices and how managers can become expert strategists and analysts to better measure and enhance corporate performance and their businesses. This book is ideal for analysts, executives, managers, entrepreneurs, researchers, students, industry professionals, stakeholders, practitioners, academicians, and others interested in the strategic analytics domain and how it can be applied to complex business dynamics.
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Strategic Modelling and Business Dynamics, + Website
Insightful modelling of dynamic systems for better business strategy The business environment is constantly changing and organisations need the ability to rehearse alternative futures. By mimicking the interlocking operations of firms and industries, modelling serves as a ‘dry run’ for testing ideas, anticipating consequences, avoiding strategic pitfalls and improving future performance. Strategic Modelling and Business Dynamics is an essential guide to credible models; helping you to understand modelling as a creative process for distilling and communicating those factors that drive business success and sustainability. Written by an internationally regarded authority, the book covers all stages of model building, from conceptual to analytical. The book demonstrates a range of in-depth practical examples that vividly illustrate important or puzzling dynamics in firm operations, strategy, public policy, and everyday life. This updated new edition also offers a rich Learners' website with models, articles and videos, as well as a separate Instructors' website resource, with lecture slides and other course materials (see Related Websites/Extra section below). Together the book and websites deliver a powerful package of blended learning materials that: Introduce the system dynamics approach of modelling strategic problems in business and societyInclude industry examples and public sector applications with interactive simulators and contemporary visual modelling softwareProvide the latest state-of-the-art thinking, concepts and techniques for systems modelling The comprehensive Learners' website features models, microworlds, journal articles and videos. Easy-to-use simulators enable readers to experience dynamic complexity in business and society. Like would-be CEOs, readers can re-design operations and then re-simulate in the quest for well-coordinated strategy and better performance. The simulators include a baffling hotel shower, a start-up low-cost airline, an international radio broadcaster, a diversifying tyre maker, commercial fisheries and the global oil industry. "Much more than an introduction, John Morecroft’s Strategic Modelling and Business Dynamics uses interactive ‘mini-simulators and microworlds’ to create an engaging and effective learning environment in which readers, whatever their background, can develop their intuition about complex dynamic systems." John Sterman, Jay W. Forrester Professor of Management, MIT Sloan School of Management "Illustrated by examples from everyday life, business and policy, John Morecroft expertly demonstrates how systems thinking aided by system dynamics can improve our understanding of the world around us." Stewart Robinson, Associate Dean Research, President of the Operational Research Society, Professor of Management Science, School of Business and Economics, Loughborough University
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Big Data Analytics and Knowledge Discovery
This book constitutes the refereed proceedings of the 19th International Conference on Big Data Analytics and Knowledge Discovery, DaWaK 2017, held in Lyon, France, in August 2017.The 24 revised full papers and 11 short papers presented were carefully reviewed and selected from 97 submissions. The papers are organized in the following topical sections: new generation data warehouses design; cloud and NoSQL databases; advanced programming paradigms; non-functional requirements satisfaction; machine learning; social media and twitter analysis; sentiment analysis and user influence; knowledge discovery; and data flow management and optimization.
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Evolving Predictive Analytics in Healthcare
A major use of practical predictive analytics in medicine has been in the diagnosis of current diseases, particularly through medical imaging. Now there is sufficient improvement in AI, IoT and data analytics to deal with real time problems with an increased focus on early prediction using machine learning and deep learning algorithms. With the power of artificial intelligence alongside the internet of 'medical' things, these algorithms can input the characteristics/data of their patients and get predictions of future diagnoses, classifications, treatment and costs. Evolving Predictive Analytics in Healthcare: New AI techniques for real-time interventions discusses deep learning algorithms in medical diagnosis, including applications such as Covid-19 detection, dementia detection, and predicting chemotherapy outcomes on breast cancer tumours. Smart healthcare monitoring frameworks using IoT with big data analytics are explored and the latest trends in predictive technology for solving real-time health care problems are examined. By using real-time data inputs to build predictive models, this new technology can literally 'see' your future health and allow clinicians to intervene as needed. This book is suitable reading for researchers interested in healthcare technology, big data analytics, and artificial intelligence.
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Quality and Operations Management
This book considers the associated concepts of quality and operations and places customers at the heart of a quality business operation. It explains how organisations that are focused on delivering quality products and services continuously improve the design, planning and control of their operational processes so that they meet the developing needs of their customer.
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Essentials of Data Science and Analytics
This text provides a comprehensive overview of Data Science. With the continued advancement in storage and computing technologies, data science has emerged as one of the most desired fields in driving business decisions. Data science employs techniques and methods from many other fields, such as statistics, mathematics, computer science, and information science. Besides the methods and theories drawn from several fields, data science uses visualization techniques using specially designed big data software and statistical programming language, such as R programming, and Python. Data Science has wide applications in the areas of Machine Learning (ML) and Artificial Intelligence (AI). The book is divided into four different areas divided into different chapters. These chapters explain the core of Data Science. Part I of the book introduces the field of Data Science, different disciplines it comprises of, and the scope with future outlook and career prospects. This section also explains analytics, business analytics, and business intelligence and their similarities and differences with Data Science. Since the data is at the core of Data science, Part II is devoted to explaining the data, big data, and other features of data. One full chapter is devoted to Data Analysis, creating visuals, pivot table, and other applications using Excel with office 365. Part III explains the statistics behind Data Science. It uses several chapters to explain the statistics and its importance, numerical and data visualization tools and methods, probability, and probability distribution applications in Data Science. Other chapters in the Part III are Sampling, Estimation, and Hypothesis Testing. All these are integral part of Data Science applications. Part IV of the book provides the basics of Machine Learning (ML) and R-statistical software. Data Science has wide applications in the areas of Machine Learning (ML) and Artificial Intelligence (AI) and R-statistical software is widely used by data science professionals. The book also outlines a brief history, the body of knowledge, skills and education requirements for Data Scientist and data science professionals. Some statistics on job growth and prospects are also summarized. A career in data science is ranked at the third best job in America for 2020 by Glassdoor, and was ranked the number one best job from 2016-2019.[29]
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Multivariate Statistical Modeling in Engineering and Management
The book focuses on problem solving for practitioners and model building for academicians under multivariate situations. This book helps readers in understanding the issues, such as knowing variability, extracting patterns, building relationships, and making objective decisions. A large number of multivariate statistical models are covered in the book. The readers will learn how a practical problem can be converted to a statistical problem and how the statistical solution can be interpreted as a practical solution.Key features:Links data generation process with statistical distributions in multivariate domainProvides step by step procedure for estimating parameters of developed modelsProvides blueprint for data driven decision makingIncludes practical examples and case studies relevant for intended audiencesThe book will help everyone involved in data driven problem solving, modeling and decision making.
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Advanced Analytics with R and Tableau
Leverage the power of advanced analytics and predictive modeling in Tableau using the statistical powers of RAbout This Book• A comprehensive guide that will bring out the creativity in you to visualize the results of complex calculations using Tableau and R• Combine Tableau analytics and visualization with the power of R using this step-by-step guide• Wondering how R can be used with Tableau? This book is your one-stop solution.Who This Book Is ForThis book will appeal to Tableau users who want to go beyond the Tableau interface and deploy the full potential of Tableau, by using R to perform advanced analytics with Tableau.A basic familiarity with R is useful but not compulsory, as the book will start off with concrete examples of R and will move quickly into more advanced spheres of analytics using online data sources to support hands-on learning. Those R developers who want to integrate R in Tableau will also benefit from this book.What You Will Learn• Integrate Tableau's analytics with the industry-standard, statistical prowess of R.• Make R function calls in Tableau, and visualize R functions with Tableau using RServe.• Use the CRISP-DM methodology to create a roadmap for analytics investigations.• Implement various supervised and unsupervised learning algorithms in R to return values to Tableau.• Make quick, cogent, and data-driven decisions for your business using advanced analytical techniques such as forecasting, predictions, association rules, clustering, classification, and other advanced Tableau/R calculated field functions.In DetailTableau and R offer accessible analytics by allowing a combination of easy-to-use data visualization along with industry-standard, robust statistical computation.Moving from data visualization into deeper, more advanced analytics? This book will intensify data skills for data viz-savvy users who want to move into analytics and data science in order to enhance their businesses by harnessing the analytical power of R and the stunning visualization capabilities of Tableau. Readers will come across a wide range of machine learning algorithms and learn how descriptive, prescriptive, predictive, and visually appealing analytical solutions can be designed with R and Tableau. In order to maximize learning, hands-on examples will ease the transition from being a data-savvy user to a data analyst using sound statistical tools to perform advanced analytics.By the end of this book, you will get to grips with advanced calculations in R and Tableau for analytics and prediction with the help of use cases and hands-on examples.Style and approachTableau (uniquely) offers excellent visualization combined with advanced analytics; R is at the pinnacle of statistical computational languages. When you want to move from one view of data to another, backed up by complex computations, the combination of R and Tableau makes the perfect solution. This example-rich guide will teach you how to combine these two to perform advanced ana
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Handbook of Research on Advances in Data Analytics and Complex Communication Networks
The use of the internet has increased with the introduction of wireless systems and the internet of things (IoT). The growth of smart devices helps to create a new generation and knowledge-sharing platforms. Namely, data analytics has a major role to play in this growth and the success of wireless and IoT applications. The growth of data has become exponential, and it is difficult to analyze with respect to complex communication networks, hence the need for research in the field to understand both big data and these communication networks. Additionally, there are many unforeseen challenges that can emerge and disrupt the normal flow of things, such as the global COVID-19 pandemic, which produce more stress on current systems and technologies. The study of IoT, data analytics, and complex communication networks is essential in current times. The Handbook of Research on Advances in Data Analytics and Complex Communication Networks discusses and addresses the issues and challenges of the development of various systems using data analytics and complex communication networks. The chapters identify different issues, suggest feasible solutions to those identified issues, and present comprehensive information on emerging technologies related to data analytics and complex communication networks such as cloud computing, big data, and IoT. This book will also address different aspects of data analytics and propose novel strategies based on wireless systems and IoT including optimization, control, statistics, social computing, and more. This book is valuable for data scientists, data analysts, network analysts, researchers, academicians, and students interested in the latest developments and advancements in data analytics and complex communication networks.
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Responsible Analytics and Data Mining in Education
Winner of two Outstanding Book Awards from the Association of Educational Communications and Technology (Culture, Learning, & Technology and Systems Thinking & Change divisions)! Rapid advancements in our ability to collect, process, and analyze massive amounts of data along with the widespread use of online and blended learning platforms have enabled educators at all levels to gain new insights into how people learn. Responsible Analytics and Data Mining in Education addresses the thoughtful and purposeful navigation, evaluation, and implementation of these emerging forms of educational data analysis. Chapter authors from around the world explore how data analytics can be used to improve course and program quality; how the data and its interpretations may inadvertently impact students, faculty, and institutions; the quality and reliability of data, as well as the accuracy of data-based decisions; ethical implications surrounding the collection, distribution, and use of student-generated data; and more. This volume unpacks and explores this complex issue through a systematic framework whose dimensions address the issues that must be considered before implementation of a new initiative or program.
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Local Polynomial Modelling and Its Applications
Data-analytic approaches to regression problems, arising from many scientific disciplines are described in this book. The aim of these nonparametric methods is to relax assumptions on the form of a regression function and to let data search for a suitable function that describes the data well. The use of these nonparametric functions with parametric techniques can yield very powerful data analysis tools. Local polynomial modeling and its applications provides an up-to-date picture on state-of-the-art nonparametric regression techniques. The emphasis of the book is on methodologies rather than on theory, with a particular focus on applications of nonparametric techniques to various statistical problems. High-dimensional data-analytic tools are presented, and the book includes a variety of examples. This will be a valuable reference for research and applied statisticians, and will serve as a textbook for graduate students and others interested in nonparametric regression.
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Digitalization and Analytics for Smart Plant Performance
This book addresses the topic of integrated digitization of plants on an objective basis and in a holistic manner by sharing data, applying analytics tools and integrating workflows via pertinent examples from industry. It begins with an evaluation of current performance management practices and an overview of the need for a "Connected Plant" via digitalization followed by sections on "Connected Assets: Improve Reliability and Utilization," "Connected Processes: Optimize Performance and Economic Margin " and "Connected People: Digitalizing the Workforce and Workflows and Developing Ownership and Digital Culture," then culminating in a final section entitled "Putting All Together Into an Intelligent Digital Twin Platform for Smart Operations and Demonstrated by Application cases."
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Applied Machine Learning and Data Analytics
This book constitutes the refereed proceedings of the 5th International Conference on Applied Machine Learning and Data Analytics, AMLDA 2022, held in Reynosa, Tamaulipas, Mexico, during December 22–23, 2022. The 16 full papers and 4 short papers included in this book were carefully reviewed and selected from 89 submissions. They were organized in topical sections as follows: Machine learning, Healthcare and medical imaging informatics; biometrics; forensics; precision agriculture; risk management; robotics and satellite imaging.
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SQL for Data Analytics
Take your first steps to becoming a fully qualified data analyst by learning how to explore complex datasetsKey FeaturesMaster each concept through practical exercises and activitiesDiscover various statistical techniques to analyze your dataImplement everything you’ve learned on a real-world case study to uncover valuable insightsBook DescriptionEvery day, businesses operate around the clock, and a huge amount of data is generated at a rapid pace. This book helps you analyze this data and identify key patterns and behaviors that can help you and your business understand your customers at a deep, fundamental level. SQL for Data Analytics, Third Edition is a great way to get started with data analysis, showing how to effectively sort and process information from raw data, even without any prior experience. You will begin by learning how to form hypotheses and generate descriptive statistics that can provide key insights into your existing data. As you progress, you will learn how to write SQL queries to aggregate, calculate, and combine SQL data from sources outside of your current dataset. You will also discover how to work with advanced data types, like JSON. By exploring advanced techniques, such as geospatial analysis and text analysis, you will be able to understand your business at a deeper level. Finally, the book lets you in on the secret to getting information faster and more effectively by using advanced techniques like profiling and automation. By the end of this book, you will be proficient in the efficient application of SQL techniques in everyday business scenarios and looking at data with the critical eye of analytics professional.What you will learnUse SQL to clean, prepare, and combine different datasetsAggregate basic statistics using GROUP BY clausesPerform advanced statistical calculations using a WINDOW functionImport data into a database to combine with other tablesExport SQL query results into various sourcesAnalyze special data types in SQL, including geospatial, date/time, and JSON dataOptimize queries and automate tasksThink about data problems and find answers using SQLWho this book is forIf you're a database engineer looking to transition into analytics or a backend engineer who wants to develop a deeper understanding of production data and gain practical SQL knowledge, you will find this book useful. This book is also ideal for data scientists or business analysts who want to improve their data analytics skills using SQL.Basic familiarity with SQL (such as basic SELECT, WHERE, and GROUP BY clauses) as well as a good understanding of linear algebra, statistics, and PostgreSQL 14 are necessary to make the most of this SQL data analytics book.
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Data Mining and Predictive Analytics for Business Decisions
With many recent advances in data science, we have many more tools and techniques available for data analysts to extract information from data sets. This book will assist data analysts to move up from simple tools such as Excel for descriptive analytics to answer more sophisticated questions using machine learning. Most of the exercises use R and Python, but rather than focus on coding algorithms, the book employs interactive interfaces to these tools to perform the analysis. Using the CRISP-DM data mining standard, the early chapters cover conducting the preparatory steps in data mining: translating business information needs into framed analytical questions and data preparation. The Jamovi and the JASP interfaces are used with R and the Orange3 data mining interface with Python. Where appropriate, Voyant and other open-source programs are used for text analytics. The techniques covered in this book range from basic descriptive statistics, such as summarization and tabulation, to more sophisticated predictivetechniques, such as linear and logistic regression, clustering, classification, and text analytics. Includes companion files with case study files, solution spreadsheets, data sets and charts, etc. from the book. FEATURES:Covers basic descriptive statistics, such as summarization and tabulation, to more sophisticated predictive techniques, such as linear and logistic regression, clustering, classification, and text analyticsUses R, Python, Jamovi and JASP interfaces, and the Orange3 data mining interfaceIncludes companion files with the case study files from the book, solution spreadsheets, data sets, etc.
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Emerging Trends in Decision Sciences and Business Operations
This volume analyzes the rising inclusion of new and emerging technologies in business. It measures the effectiveness and challenges of these tools in various aspects of strategy and decision making within small and big businesses.Businesses in the competitive market must be agile and innovative to drive growth, and the inclusion of technology and reliance on data science for decision making is fraught with its own set of challenges. Through various case studies and analysis of trends, the book explores diverse dimensions of decision science from a micro as well as macro perspective. It examines the function, management, and implementation of new technologies, like big data and AI in business operations and infrastructure, highlighting their practical applications as well as the barriers to their adoption.This book will be useful to scholars and researchers of management studies, business management, financial management, business economics, international business, finance and marketing, development studies, and economics. It will also interest policymakers and practitioners in the field.
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Operations Management in Agriculture
Operations Management in Agriculture bridges the knowledge gap on operations management for agricultural machinery. It complements traditional topics (cost of using and choosing machinery) with advanced engineering approaches recently applied in agricultural machinery management (area coverage planning and sequential scheduling). The book covers new technologies in bio-production systems (robotics, IoT) and environmental compliance by employing a systems engineering perspective with focuses on sub-systems, including advanced optimization, supply chain systems, sustainability, autonomous vehicles and IT-driven decision-making. It will be a valuable resource for students studying decision-making and those working to improve the efficiency, effectiveness and sustainability of production through machinery choice.
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Machine Intelligence and Data Analytics for Sustainable Future Smart Cities
This book presents the latest advances in computational intelligence and data analytics for sustainable future smart cities. It focuses on computational intelligence and data analytics to bring together the smart city and sustainable city endeavors. It also discusses new models, practical solutions and technological advances related to the development and the transformation of cities through machine intelligence and big data models and techniques. This book is helpful for students and researchers as well as practitioners.
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Data Analytics and Big Data
The main purpose of this book is to investigate, explore and describe approaches and methods to facilitate data understanding through analytics solutions based on its principles, concepts and applications. But analyzing data is also about involving the use of software. For this, and in order to cover some aspect of data analytics, this book uses software (Excel, SPSS, Python, etc) which can help readers to better understand the analytics process in simple terms and supporting useful methods in its application.
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R 2,372.18
Operations Research and Health Care Policy
Operations research tools are ideally suited to providing solutions and insights for the many problems health policy-maker's face. Indeed, a growing body of literature on health policy analysis, based on operations research methods, has emerged to address the problems mentioned above and several others. The research in this field is often multi-disciplinary, being conducted by teams that include not only operations researchers but also clinicians, economists and policy analysts. The research is also often very applied, focusing on a specific question driven by a decision-maker and many times yielding a tool to assist in future decisions.The goal of this volume was to bring together a group of papers by leading experts that could showcase the current state of the field of operations research applied to health-care policy. There are 18 chapters that illustrate the breadth of this field. The chapters use a variety of techniques, including classical operations research tools, such as optimization, queuing theory, and discrete event simulation, as well as statistics, epidemic models and decision-analytic models. The book spans the field and includes work that ranges from highly conceptual to highly applied. An example of the former is the chapter by Kimmel and Schackman on building policy models, and an example of the latter is the chapter by Coyle and colleagues on developing a Markov model for use by an organization in Ontario that makes recommendations about the funding of new drugs. The book also includes a mix of review chapters, such as the chapter by Hutton on public health response to influenza outbreaks, and original research, such as the paper by Blake and colleagues analyzing a decision by Canadian Blood Services to consolidate services. This volume could provide an excellent introduction to the field of operations research applied to health-care policy, and it could also serve as an introduction to new areas for researchers already familiar with the topic.The book is divided into six sections. The first section contains two chapters that describe several different applications of operations research in health policy and provide an excellent overview of the field. Sections 2 to 4 present policy models in three focused areas. Section 5 contains two chapters on conceptualizing and building policy models. The book concludes in Section 6 with two chapters describing work that was done with policy-makers and presenting insights gained from working directly with policy-makers.
R 6,109.22
Integrating Marketing Analytics Approaches into B2B Lead Management
Despite the growing importance of acquiring new customers due to increased global competition, lead management processes in business-to-business (B2B) companies often remain ineffective. Marketing analytics offers opportunities to address common issues in lead generation, lead scoring, and lead nurturing through data insights. Simultaneously, it enables companies to measure the effectiveness of their lead management efforts. Nevertheless, the extent of data-driven decision-making varies in practice, and the academic literature on marketing analytics in B2B lead management remains fragmented. This thesis bridges the gap by demonstrating how marketing analytics approaches can be integrated into B2B lead management to improve process effectiveness. The findings are summarized in a practical guide that contains concrete steps and recommendations for B2B marketing professionals, considering different maturity levels.
R 3,862.79
R 3,870.29
Handbook of Research on Lifestyle Sustainability and Management Solutions Using AI, Big Data Analytics, and Visualization
The sudden outbreak of the COVID-19 pandemic has curbed human lifestyle by imposing restrictions on regular daily movements that had been taken for granted. Due to the pandemic, the welfare segment has received more attention, and every possible effort is being made to prioritize the services at the top. This can be made possible while using the latest tools, technologies, and resources that impact the human culture and welfare of well-being. Novel methods and devices that make the welfare services more efficient, adaptive, transparent, and cost-effective need to be explored. The Handbook of Research on Lifestyle Sustainability and Management Solutions Using AI, Big Data Analytics, and Visualization offers extensive research on lifestyle management and services that contribute towards indication, detection, conduction, protection, and technological enhancement including machine learning, deep learning, artificial intelligence, big data analytics, and visualization. It also provides mechanisms that can improve lifestyle monitoring and help in increasing the immunity of the human body. Covering topics such as big data, robot therapy, and wearable technology, it is ideal for students, researchers, technologists, IT specialists, computer engineers, systems engineers, data scientists, doctors, hospital administrators, engineers, academicians, and technology providers.
R 16,748.61
Data Analytics in e-Learning: Approaches and Applications
This book focuses on research and development aspects of building data analytics workflows that address various challenges of e-learning applications.This book represents a guideline for building a data analysis workflow from scratch. Each chapter presents a step of the entire workflow, starting from an available dataset and continuing with building interpretable models, enhancing models, and tackling aspects of evaluating engagement and usability. The related work shows that many papers have focused on machine learning usage and advancement within e-learning systems. However, limited discussions have been found on presenting a detailed complete roadmap from the raw dataset up to the engagement and usability issues. Practical examples and guidelines are provided for designing and implementing new algorithms that address specific problems or functionalities. This roadmap represents a potential resource for various advances of researchers and practitioners in educational data mining and learning analytics.
R 7,296.24
Algorithmic Aspects in Information and Management
This volume constitutes the proceedings of the 11th International Conference on Algorithmic Aspects in Information and Management, AAIM 2016, held in Bergamo, Italy, in July 2016.The 18 revised full papers presented were carefully reviewed and selected from 41 submissions. The papers deal with current trends of research on algorithms, data structures, operation research, combinatorial optimization and their applications.
R 2,163.89
