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Analytical and Stochastic Modelling Techniques and Applications: 23rd International Conference, ASMTA 2016, Cardiff, UK, August 24-26, 2016, Proceedings (Lecture Notes in Computer Science #9845)
by Sabine Wittevrongel Tuan Phung-DucThis book constitutes the refereed proceedings of the 23rd International Conference on Analytical and Stochastic Modelling Techniques and Applications, ASMTA 2016, held in Cardiff, UK, in August 2016. The 21 full papers presented in this book were carefully reviewed and selected from 30 submissions. The papers discuss the latest developments in analytical, numerical and simulation algorithms for stochastic systems, including Markov processes, queueing networks, stochastic Petri nets, process algebras, game theory, etc.
Analytical and Stochastic Modelling Techniques and Applications: 24th International Conference, ASMTA 2017, Newcastle-upon-Tyne, UK, July 10-11, 2017, Proceedings (Lecture Notes in Computer Science #10378)
by Nigel Thomas Matthew ForshawThis book constitutes the refereed proceedings of the 24th International Conference on Analytical and Stochastic Modelling Techniques and Applications, ASMTA 2017, held in Newcastle-upon-Tyne UK, in July 2017.The 14 full papers presented in this book were carefully reviewed and selected from 27 submissions. The scope of the conference is on following topics: analytical, numerical and simulation algorithms for stochastic systems, including Markov processes, queueing networks, stochastic Petri nets, process algebras, game theoretical models.
Analytical and Stochastic Modelling Techniques and Applications: 25th International Conference, ASMTA 2019, Moscow, Russia, October 21–25, 2019, Proceedings (Lecture Notes in Computer Science #12023)
by Marco Gribaudo Eduard Sopin Irina KochetkovaThis book constitutes the refereed proceedings of the 25th International Conference on Analytical and Stochastic Modelling Techniques and Applications, ASMTA 2019, held in Moscow, Russia, in October 2019. Methods of analytical and stochastic modelling are widely used in engineering to assess and design various complex systems, like computer and communication networks, and manufacturing systems. The 13 full papers presented in this book were carefully reviewed and selected from 22 submissions. The papers detail a diverse range of analysis techniques, including Markov processes, queueing theoretical results, reliability of stochastic systems, stochastic network calculus, and wide variety of applications.
Analytical and Stochastic Modelling Techniques and Applications: 28th International Conference, ASMTA 2024, Venice, Italy, June 14, 2024, Proceedings (Lecture Notes in Computer Science #14826)
by András Horváth Arnaud Devos Sabina RossiThis book constitutes the refereed proceedings of the 28th International Conference on Analytical and Stochastic Modelling Techniques and Applications, ASMTA 2024, held in Venice, Italy, on June 14, 2024. The 10 full papers presented were carefully reviewed and selected from 14 submissions. These papers covered a wide range of topics in analytical and stochastic modeling techniques and their applications.
Analytical or Semi-analytical Solutions of Functionally Graded Material Structures
by Zheng Zhong Guojun NieThis book provides a comprehensive introduction to the analysis of functionally graded materials and structures. Functionally graded materials (FGMs), in which the volume fractions of two or more constituent materials are designed to vary continuously as a function of position along certain direction(s), have been developed and studied over the past three decades. The major advantage of FGMs is that no distinct internal boundaries exist, and failures from interfacial stress concentrations developed in conventional components can be avoided. The gradual change of material properties can be tailored to different applications and working environments. As these materials’ range of application expands, new methodologies have to be developed to characterize them, and to design and analyze structural components made of them.Despite a number of existing papers on the analysis of functionally graded materials and structures, there is no single book that is devoted entirely to the analysis of functionally graded beams, plates and shells using different methods, e.g.,analytical or semi-analytical methods.Filling this gap in the literature, the book offers a valuable reference resource for senior undergraduates, graduate students, researchers, and engineers in this field. The results presented here can be used as a benchmark for checking the validity and accuracy of other numerical solutions. They can also be used directly in the design of functionally graded materials and structures.
Analyticity (New Problems of Philosophy)
by Cory Juhl Eric LoomisAnalyticity, or the 'analytic/synthetic' distinction is one of the most important and controversial problems in contemporary philosophy. It is also essential to understanding many developments in logic, philosophy of language, epistemology and metaphysics. In this outstanding introduction to analyticity Cory Juhl and Eric Loomis cover the following key topics: The origins of analyticity in the philosophy of Hume and Kant Carnap's arguments concerning analyticity in the early twentieth century Quine's famous objections to analyticity in his classic 'Two Dogmas of Empiricism' essay The relationship between analyticity and central issues in metaphysics, such as ontology The relationship between analyticity and epistemology Analyticity in the context of the current debates in philosophy, including mathematics and ontology Throughout the book the authors show how many philosophical controversies hinge on the problem of analyticity. Additional features include chapter summaries, annotated further reading and a glossary of technical terms making the book ideal to those coming to the problem for the first time.
Analyticity and Sparsity in Uncertainty Quantification for PDEs with Gaussian Random Field Inputs (Lecture Notes in Mathematics #2334)
by Christoph Schwab Dinh Dũng Van Kien Nguyen Jakob ZechThe present book develops the mathematical and numerical analysis of linear, elliptic and parabolic partial differential equations (PDEs) with coefficients whose logarithms are modelled as Gaussian random fields (GRFs), in polygonal and polyhedral physical domains. Both, forward and Bayesian inverse PDE problems subject to GRF priors are considered.Adopting a pathwise, affine-parametric representation of the GRFs, turns the random PDEs into equivalent, countably-parametric, deterministic PDEs, with nonuniform ellipticity constants. A detailed sparsity analysis of Wiener-Hermite polynomial chaos expansions of the corresponding parametric PDE solution families by analytic continuation into the complex domain is developed, in corner- and edge-weighted function spaces on the physical domain.The presented Algorithms and results are relevant for the mathematical analysis of many approximation methods for PDEs with GRF inputs, such as model order reduction, neural network and tensor-formatted surrogates of parametric solution families. They are expected to impact computational uncertainty quantification subject to GRF models of uncertainty in PDEs, and are of interest for researchers and graduate students in both, applied and computational mathematics, as well as in computational science and engineering.
Analytics And Modern Warfare: Dominance by the Numbers
by Michael TaillardThis book details very simply and for even the most novice of potential analysts not only how to perform analytics which describe what is happening, predict what is going to happen, and optimize responses, but also places these analytics in the context of proactive strategy development.
Analytics Enabled Decision Making
by Vinod Sharma Chandan Maheshkar Jeanne PouloseAnalytics is changing the landscape of businesses across sectors globally. This has led to the stimulation of interest of scholars and practitioners worldwide in this domain. The emergence of ‘big data’, has fanned the usages of machine learning techniques and the acceptance of ‘Analytics Enabled Decision Making’. This book provides a holistic theoretical perspective combined with the application of such theories by drawing on the experiences of industry professionals and academicians from around the world. The book discusses several paradigms including pattern mining, clustering, classification, and data analysis to name a few. The main objective of this book is to offer insight into the process of decision-making that is accelerated and made more precise with the help of analytics.
Analytics Engineering with SQL and dbt: Building Meaningful Data Models at Scale
by Rui Pedro Machado Helder RussaWith 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 Hélder 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
Analytics Modeling in Reliability and Machine Learning and Its Applications (Springer Series in Reliability Engineering)
by Hoang PhamThis book presents novel research and application chapters on topics in reliability, statistics, and machine learning. It has an emphasis on analytical models and techniques and practical applications in reliability engineering, data science, manufacturing, health care, and industry using machine learning, AI, optimization, and other computational methods. Today, billions of people are connected to each other through their mobile devices. Data is being collected and analysed more than ever before. The era of big data through machine learning algorithms, statistical inference, and reliability computing in almost all applications has resulted in a dramatic shift in the past two decades. Data analytics in business, finance, and industry is vital. It helps organizations and business to achieve better results and fact-based decision-making in all aspects of life. The book offers a broad picture of current research on the analytics modeling and techniques and its applications in industry. Topics include: l Reliability modeling and methods. l Software reliability engineering. l Maintenance modeling and policies. l Statistical feature selection. l Big data modeling. l Machine learning: models and algorithms. l Data-driven models and decision-making methods. l Applications and case studies in business, health care, and industrial systems. Postgraduates, researchers, professors, scientists, engineers, and practitioners in reliability engineering and management, machine learning engineering, data science, operations research, industrial and systems engineering, statistics, computer science and engineering, mechanical engineering, and business analytics will find in this book state-of-the-art analytics, modeling and methods in reliability and machine learning.
Analytics Optimization with Columnstore Indexes in Microsoft SQL Server: Optimizing OLAP Workloads
by Edward PollackMeet the challenge of storing and accessing analytic data in SQL Server in a fast and performant manner. This book illustrates how columnstore indexes can provide an ideal solution for storing analytic data that leads to faster performing analytic queries and the ability to ask and answer business intelligence questions with alacrity. The book provides a complete walk through of columnstore indexing that encompasses an introduction, best practices, hands-on demonstrations, explanations of common mistakes, and presents a detailed architecture that is suitable for professionals of all skill levels. With little or no knowledge of columnstore indexing you can become proficient with columnstore indexes as used in SQL Server, and apply that knowledge in development, test, and production environments. This book serves as a comprehensive guide to the use of columnstore indexes and provides definitive guidelines. You will learn when columnstore indexes should be used, and the performance gains that you can expect. You will also become familiar with best practices around architecture, implementation, and maintenance. Finally, you will know the limitations and common pitfalls to be aware of and avoid.As analytic data can become quite large, the expense to manage it or migrate it can be high. This book shows that columnstore indexing represents an effective storage solution that saves time, money, and improves performance for any applications that use it. You will see that columnstore indexes are an effective performance solution that is included in all versions of SQL Server, with no additional costs or licensing required. What You Will LearnImplement columnstore indexes in SQL ServerKnow best practices for the use and maintenance of analytic data in SQL ServerUse metadata to fully understand the size and shape of data stored in columnstore indexesEmploy optimal ways to load, maintain, and delete data from large analytic tablesKnow how columnstore compression saves storage, memory, and timeUnderstand when a columnstore index should be used instead of a rowstore indexBe familiar with advanced features and analyticsWho This Book Is ForDatabase developers, administrators, and architects who are responsible for analytic data, especially for those working with very large data sets who are looking for new ways to achieve high performance in their queries, and those with immediate or future challenges to analytic data and query performance who want a methodical and effective solution
Analytics Stories: Using Data to Make Good Things Happen
by Wayne L. WinstonInform your own analyses by seeing how one of the best data analysts in the world approaches analytics problems Analytics Stories: How to Make Good Things Happen is a thoughtful, incisive, and entertaining exploration of the application of analytics to real-world problems and situations. Covering fields as diverse as sports, finance, politics, healthcare, and business, Analytics Stories bridges the gap between the oft inscrutable world of data analytics and the concrete problems it solves. Distinguished professor and author Wayne L. Winston answers questions like: Was Liverpool over Barcelona the greatest upset in sports history? Was Derek Jeter a great infielder What's wrong with the NFL QB rating? How did Madoff keep his fund going? Does a mutual fund’s past performance predict future performance? What caused the Crash of 2008? Can we predict where crimes are likely to occur? Is the lot of the American worker improving? How can analytics save the US Republic? The birth of evidence-based medicine: How did James Lind know citrus fruits cured scurvy? How can I objectively compare hospitals? How can we predict heart attacks in real time? How does a retail store know if you're pregnant? How can I use A/B testing to improve sales from my website? How can analytics help me write a hit song? Perfect for anyone with the word “analyst” in their job title, Analytics Stories illuminates the process of applying analytic principles to practical problems and highlights the potential pitfalls that await careless analysts.
Analytics and Big Data for Accountants (AICPA)
by Jim LindellAnalytics is a new force driving business. Tools have been created to measure program effects and return on investment, visualize data and business processes, and uncover the relationship between key performance indicators—many using the unprecedented amount of data now moving into organizations. In this course, you will discuss leading-edge topics in analytics and finance in a session that is packed with useful tips and practical guidance that you can apply immediately.
Analytics and Big Data for Accountants (AICPA)
by Jim LindellWhy is big data analytics one of the hottest business topics today? This book will help accountants and financial managers better understand big data and analytics, including its history and current trends. It dives into the platforms and operating tools that will help you measure program impacts and ROI, visualize data and business processes, and uncover the relationship between key performance indicators. Key topics covered include: Evidence-based techniques for finding or generating data, selecting key performance indicators, isolating program effects Relating data to return on investment, financial values, and executive decision making Data sources including surveys, interviews, customer satisfaction, engagement, and operational data Visualizing and presenting complex results
Analytics and Big Data: The Davenport Collection
by D. J. Patil Thomas H. Davenport Jinho Kim Jeanne G. Harris Robert MorisonThe Analytics and Big Data collection offers a “greatest hits” digital compilation of ideas from world-renowned thought leader Thomas Davenport, who helped popularize the terms analytics and big data in the workplace. An agile and prolific thinker, Davenport has written or coauthored more than a dozen bestselling books. Several of these titles are offered together for the first time in this curated digital bundle, including: Big Data at Work, Competing on Analytics, Analytics at Work, and Keeping Up with the Quants. The collection also includes Davenport’s popular Harvard Business Review articles, “Data Scientist: The Sexiest Job of the 21st Century” (2012) and “Analytics 3.0” (2013). Combined, these works cover all the bases on analytics and big data: what each term means; the ramifications of each from a technical, consumer, and management perspective; and where each can have the biggest impact on your business. Whether you’re an executive, a manager, or a student wanting to learn more, Analytics and Big Data is the most comprehensive collection you’ll find on the ever-growing phenomenon of digital data and analysis—and how you can make this rising business trend work for you. Named one of the ten “Masters of the New Economy” by CIO magazine, Thomas Davenport has helped hundreds of companies revitalize their management practices. He combines his interests in research, teaching, and business management as the President’s Distinguished Professor of Information Technology & Management at Babson College. Davenport has also taught at Harvard Business School, the University of Chicago, Dartmouth’s Tuck School of Business, and the University of Texas at Austin and has directed research centers at Accenture, McKinsey & Company, Ernst & Young, and CSC.
Analytics and Business Performance: Transforming the Ability to Compete on Analytics into a Lasting Competitive Advantage
by Thomas H. Davenport Jeanne G. HarrisThis chapter explores the links between the extensive use of analytics and business performance, describing how several highly successful companies have transformed their ability to compete analytically into a key point of differentiation and long-term competitive advantage. This chapter was originally published as Chapter 3 of "Competing on Analytics."
Analytics and Data Science: Advances in Research and Pedagogy (Annals of Information Systems #21)
by Ashish Gupta Lakshmi S. Iyer Amit V. Deokar Mary C. JonesThis book explores emerging research and pedagogy in analytics and data science that have become core to many businesses as they work to derive value from data. The chapters examine the role of analytics and data science to create, spread, develop and utilize analytics applications for practice. Selected chapters provide a good balance between discussing research advances and pedagogical tools in key topic areas in analytics and data science in a systematic manner. This book also focuses on several business applications of these emerging technologies in decision making, i. e. , business analytics. The chapters in Analytics and Data Science: Advances in Research and Pedagogy are written by leading academics and practitioners that participated at the Business Analytics Congress 2015. Applications of analytics and data science technologies in various domains are still evolving. For instance, the explosive growth in big data and social media analytics requires examination of the impact of these technologies and applications on business and society. As organizations in various sectors formulate their IT strategies and investments, it is imperative to understand how various analytics and data science approaches contribute to the improvements in organizational information processing and decision making. Recent advances in computational capacities coupled by improvements in areas such as data warehousing, big data, analytics, semantics, predictive and descriptive analytics, visualization, and real-time analytics have particularly strong implications on the growth of analytics and data science.
Analytics and Decision Support in Health Care Operations Management
by Yasar A. OzcanA compendium of health care quantitative techniques based in Excel Analytics and Decision Support in Health Care Operations is a comprehensive introductory guide to quantitative techniques, with practical Excel-based solutions for strategic health care management. This new third edition has been extensively updated to reflect the continuously evolving field, with new coverage of predictive analytics, geographical information systems, flow process improvement, lean management, six sigma, health provider productivity and benchmarking, project management, simulation, and more. Each chapter includes additional new exercises to illustrate everyday applications, and provides clear direction on data acquisition under a variety of hospital information systems. Instructor support includes updated Excel templates, PowerPoint slides, web based chapter end supplements, and data banks to facilitate classroom instruction, and working administrators will appreciate the depth and breadth of information with clear applicability to everyday situations. The ability to use analytics effectively is a critical skill for anyone involved in the study or practice of health services administration. This book provides a comprehensive set of methods spanning tactical, operational, and strategic decision making and analysis for both current and future health care administrators. Learn critical analytics and decision support techniques specific to health care administration Increase efficiency and effectiveness in problem-solving and decision support Locate appropriate data in different commonly-used hospital information systems Conduct analyses, simulations, productivity measurements, scheduling, and more From statistical techniques like multiple regression, decision-tree analysis, queuing and simulation, to field-specific applications including surgical suite scheduling, roster management, quality monitoring, and more, analytics play a central role in health care administration. Analytics and Decision Support in Health Care Operations provides essential guidance on these critical skills that every professional needs.
Analytics and Dynamic Customer Strategy
by John F. Tanner Jr.Key decisions determine the success of big data strategyDynamic Customer Strategy: Big Profits from Big Data is a comprehensive guide to exploiting big data for both business-to-consumer and business-to-business marketing. This complete guide provides a process for rigorous decision making in navigating the data-driven industry shift, informing marketing practice, and aiding businesses in early adoption. Using data from a five-year study to illustrate important concepts and scenarios along the way, the author speaks directly to marketing and operations professionals who may not necessarily be big data savvy. With expert insight and clear analysis, the book helps eliminate paralysis-by-analysis and optimize decision making for marketing performance.Nearly seventy-five percent of marketers plan to adopt a big data analytics solution within two years, but many are likely to fail. Despite intensive planning, generous spending, and the best intentions, these initiatives will not succeed without a manager at the helm who is capable of handling the nuances of big data projects. This requires a new way of marketing, and a new approach to data. It means applying new models and metrics to brand new consumer behaviors. Dynamic Customer Strategy clarifies the situation, and highlights the key decisions that have the greatest impact on a company's big data plan. Topics include:Applying the elements of Dynamic Customer StrategyAcquiring, mining, and analyzing dataMetrics and models for big data utilizationShifting perspective from model to customerBig data is a tremendous opportunity for marketers and may just be the only factor that will allow marketers to keep pace with the changing consumer and thus keep brands relevant at a time of unprecedented choice. But like any tool, it must be wielded with skill and precision. Dynamic Customer Strategy: Big Profits from Big Data helps marketers shape a strategy that works.
Analytics and Knowledge Management (Data Analytics Applications)
by Suliman Hawamdeh and Hsia-Ching ChangThe process of transforming data into actionable knowledge is a complex process that requires the use of powerful machines and advanced analytics technique. Analytics and Knowledge Management examines the role of analytics in knowledge management and the integration of big data theories, methods, and techniques into an organizational knowledge management framework. Its chapters written by researchers and professionals provide insight into theories, models, techniques, and applications with case studies examining the use of analytics in organizations. The process of transforming data into actionable knowledge is a complex process that requires the use of powerful machines and advanced analytics techniques. Analytics, on the other hand, is the examination, interpretation, and discovery of meaningful patterns, trends, and knowledge from data and textual information. It provides the basis for knowledge discovery and completes the cycle in which knowledge management and knowledge utilization happen. Organizations should develop knowledge focuses on data quality, application domain, selecting analytics techniques, and on how to take actions based on patterns and insights derived from analytics. Case studies in the book explore how to perform analytics on social networking and user-based data to develop knowledge. One case explores analyze data from Twitter feeds. Another examines the analysis of data obtained through user feedback. One chapter introduces the definitions and processes of social media analytics from different perspectives as well as focuses on techniques and tools used for social media analytics. Data visualization has a critical role in the advancement of modern data analytics, particularly in the field of business intelligence and analytics. It can guide managers in understanding market trends and customer purchasing patterns over time. The book illustrates various data visualization tools that can support answering different types of business questions to improve profits and customer relationships. This insightful reference concludes with a chapter on the critical issue of cybersecurity. It examines the process of collecting and organizing data as well as reviewing various tools for text analysis and data analytics and discusses dealing with collections of large datasets and a great deal of diverse data types from legacy system to social networks platforms.
Analytics and Optimization for Renewable Energy Integration (Energy Analytics)
by Ning Zhang Yi Wang Chongqing Kang Ershun DuThe scope of this book covers the modeling and forecast of renewable energy and operation and planning of power system with renewable energy integration.The first part presents mathematical theories of stochastic mathematics; the second presents modeling and analytic techniques for renewable energy generation; the third provides solutions on how to handle the uncertainty of renewable energy in power system operation. It includes advanced stochastic unit commitment models to acquire the optimal generation schedule under uncertainty, efficient algorithms to calculate the probabilistic power, and an efficient operation strategy for renewable power plants participating in electricity markets.
Analytics at Work
by Thomas H. Davenport Jeanne G. Harris Robert MorisonMost companies have massive amounts of data at their disposal, yet fail to utilize it in any meaningful way. But a powerful new business tool - analytics - is enabling many firms to aggressively leverage their data in key business decisions and processes, with impressive results.In their previous book, Competing on Analytics, Thomas Davenport and Jeanne Harris showed how pioneering firms were building their entire strategies around their analytical capabilities. Rather than "going with the gut" when pricing products, maintaining inventory, or hiring talent, managers in these firms use data, analysis, and systematic reasoning to make decisions that improve efficiency, risk-management, and profits.Now, in Analytics at Work, Davenport, Harris, and coauthor Robert Morison reveal how any manager can effectively deploy analytics in day-to-day operations-one business decision at a time. They show how many types of analytical tools, from statistical analysis to qualitative measures like systematic behavior coding, can improve decisions about everything from what new product offering might interest customers to whether marketing dollars are being most effectively deployed.Based on all-new research and illustrated with examples from companies including Humana, Best Buy, Progressive Insurance, and Hotels.com, this implementation-focused guide outlines the five-step DELTA model for deploying and succeeding with analytical initiatives. You'll learn how to:· Use data more effectively and glean valuable analytical insights· Manage and coordinate data, people, and technology at an enterprise level· Understand and support what analytical leaders do· Evaluate and choose realistic targets for analytical activity· Recruit, hire, and manage analystsCombining the science of quantitative analysis with the art of sound reasoning, Analytics at Work provides a road map and tools for unleashing the potential buried in your company's data.
Analytics for Customer Insights: A Non-Technical Introduction
by Chuck ChakrapaniThis book is a quick an easy introduction to the techniques used in marketing research. Using no mathematics or formulas, this book explains all major analytic techniques used in marketing research. The techniques explained in this book include: - Factor Analysis - Discriminant Analysis - Boosting - Logistic Regression - Turf Analysis - Regression Analysis - Price Sensitivity Meter - Random Forest - MaxDiff - Decision Trees - Perceptual Mapping - Structural Equation (SEM) - Cluster Analysis - Conjoint Analysis - Support Vector Machines - Naive Bayes - Path Analysis - Artificial Neural Network