This volume deals with two complementary topics. On one hand the book deals with the problem of determining the the probability distribution of a positive compound random variable, a problem which appears in the banking and insurance industries, in many areas of operational research and in reliability problems in the engineering sciences. On the other hand, the methodology proposed to solve such problems, which is based on an application of the maximum entropy method to invert the Laplace transform of the distributions, can be applied to many other problems. The book contains applications to a large variety of problems, including the problem of dependence of the sample data used to estimate empirically the Laplace transform of the random variable. Contents Introduction Frequency models Individual severity models Some detailed examples Some traditional approaches to the aggregation problem Laplace transforms and fractional moment problems The standard maximum entropy method Extensions of the method of maximum entropy Superresolution in maxentropic Laplace transform inversion Sample data dependence Disentangling frequencies and decompounding losses Computations using the maxentropic density Review of statistical procedures

There is an easier way to build Hadoop applications. With this hands-on book, you'll learn how to use Cascading, the open source abstraction framework for Hadoop that lets you easily create and manage powerful enterprise-grade data processing applications - without having to learn the intricacies of MapReduce. Working with sample apps based on Java and other JVM languages, you'll quickly learn Cascading's streamlined approach to data processing, data filtering, and workflow optimization. This book demonstrates how this framework can help your business extract meaningful information from large amounts of distributed data.Start working on Cascading example projects right away Model and analyze unstructured data in any format, from any source Build and test applications with familiar constructs and reusable components Work with the Scalding and Cascalog Domain-Specific Languages Easily deploy applications to Hadoop, regardless of cluster location or data size Build workflows that integrate several big data frameworks and processes Explore common use cases for Cascading, including features and tools that support them Examine a case study that uses a dataset from the Open Data Initiative

This book highlights advanced applications of geospatial data analytics to address real-world issues in urban society. With a connected world, we are generating spatial at unprecedented rates which can be harnessed for insightful analytics which define the way we analyze past events and define the future directions. This book is an anthology of applications of spatial data and analytics performed on them for gaining insights which can be used for problem solving in an urban setting. Each chapter is contributed by spatially aware data scientists in the making who present spatial perspectives drawn on spatial big data. The book shall benefit mature researchers and student alike to discourse a variety of urban applications which display the use of machine learning algorithms on spatial big data for real-world problem solving.

Die SchAnheit der Natur mit dem Rechner nachzubilden, fasziniert die Computergraphik seit jeher. Im vorliegenden Buch werden Verfahren zur Erzeugung kA1/4nstlicher Pflanzenmodelle beschrieben und deren Anwendung in Bereichen wie Simulation, Virtual Reality, Botanik, Landschaftsplanung und Architektur. Die Modelle werden zu GArten, Parks und ganzen Landschaften kombiniert. Die Palette der Darstellungsformen reicht von tAuschend echt wirkenden Bildern bis zu abstrakten ReprAsentationen. Mit Ahnlichen Algorithmen kAnnen organische KArper hergestellt, verAndert und animiert werden. Die beigefA1/4gten Programme (Windows) erlauben dies auch dem Leser.

The book presents new results of computer-chess research in the areas of selective forward pruning, the efficient application of game-theoretical knowledge, and the behavior of the search at increasing depths. It shows how to make sophisticated game-tree searchers more scalable at ever higher depths. Throughout the whole book, the high-speed and master-strength chess program "DarkThought" serves as a realistic test vehicle to conduct numerous experiments at unprecedented search depths. The extensive experimental evaluations provide convincing empirical evidence for the practical usefulness of the presented techniques.

The LNCS journal Transactions on Large-Scale Data- and Knowledge-Centered Systems focuses on data management, knowledge discovery, and knowledge processing, which are core and hot topics in computer science. Since the 1990s, the Internet has become the main driving force behind application development in all domains. An increase in the demand for resource sharing (e.g., computing resources, services, metadata, data sources) across different sites connected through networks has led to an evolution of data- and knowledge management systems from centralized systems to decentralized systems enabling large-scale distributed applications providing high scalability. This, the 48th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains 8 invited papers dedicated to the memory of Prof. Dr. Roland Wagner. The topics covered include distributed database systems, NewSQL, scalable transaction management, strong consistency, caches, data warehouse, ETL, reinforcement learning, stochastic approximation, multi-agent systems, ontology, model-driven development, organisational modelling, digital government, new institutional economics and data governance.

This book includes original unpublished contributions presented at the International Conference on Data Analytics and Management (ICDAM 2021), held at Jan Wyzykowski University, Poland, during June 2021. The book covers the topics in data analytics, data management, big data, computational intelligence, and communication networks. The book presents innovative work by leading academics, researchers, and experts from industry which is useful for young researchers and students.

This book gathers a collection of high-quality peer-reviewed research papers presented at the International Conference on Big Data, IoT and Machine Learning (BIM 2021), held in Cox's Bazar, Bangladesh, during 23-25 September 2021. The book covers research papers in the field of big data, IoT and machine learning. The book will be helpful for active researchers and practitioners in the field.

Data Science and Analytics explores the solutions to problems in society, environment and in industry. With the increase in the availability of data, analytics has now become a major element in both the top line and the bottom line of any organization. This book explores perspectives on how big data and business analytics are increasingly essential in better decision making. This edited work explores the application of big data and business analytics by academics, researchers, industrial experts, policy makers and practitioners, helping the reader to understand how big data can be efficiently utilized in better managerial applications. Data Science and Analytics brings together researchers, engineers and practitioners to encompass a wide and diverse range of topics in a wide range of fields. The book will provide unique insights to researchers, academics and data scientists from a variety of disciplines interested in analyzing and application of big data analytics, as well as data analysts, students and scholars pursuing advanced study in big data.

Machine learning has finally come of age. With H2O software, you can perform machine learning and data analysis using a simple open source framework that's easy to use, has a wide range of OS and language support, and scales for big data. This hands-on guide teaches you how to use H20 with only minimal math and theory behind the learning algorithms. If you're familiar with R or Python, know a bit of statistics, and have some experience manipulating data, author Darren Cook will take you through H2O basics and help you conduct machine-learning experiments on different sample data sets. You'll explore several modern machine-learning techniques such as deep learning, random forests, unsupervised learning, and ensemble learning. Learn how to import, manipulate, and export data with H2O Explore key machine-learning concepts, such as cross-validation and validation data sets Work with three diverse data sets, including a regression, a multinomial classification, and a binomial classification Use H2O to analyze each sample data set with four supervised machine-learning algorithms Understand how cluster analysis and other unsupervised machine-learning algorithms work

Big data is a field of research that is growing rapidly, and as the Covid-19 crisis has shown, health care is an area that could benefit greatly from its increased use and application. Big data, as derived partly from the internet of things and analysed according to specific algorithms, has a large and beneficial role to play in preventative medicine, in monitoring the health of specific groups, and in improving diagnostics. Big Data Analytics and Intelligence: A Perspective for Health Care focuses on various areas of health care, ranging from nutrition to cancer, and providing diverse perspectives on all of them. This book explores the entire life-cycle of big data, from information retrieval to analysis, and it shows how big data's applications can enhance, streamline and improve services for patients and health-care professionals. Each chapter focuses on a specific area of health care and how big data is applicable to it, with background and current examples provided.

Educational Data Analytics (EDA) have been attributed with significant benefits for enhancing on-demand personalized educational support of individual learners as well as reflective course (re)design for achieving more authentic teaching, learning and assessment experiences integrated into real work-oriented tasks. This open access textbook is a tutorial for developing, practicing and self-assessing core competences on educational data analytics for digital teaching and learning. It combines theoretical knowledge on core issues related to collecting, analyzing, interpreting and using educational data, including ethics and privacy concerns. The textbook provides questions and teaching materials/ learning activities as quiz tests of multiple types of questions, added after each section, related to the topic studied or the video(s) referenced. These activities reproduce real-life contexts by using a suitable use case scenario (storytelling), encouraging learners to link theory with practice; self-assessed assignments enabling learners to apply their attained knowledge and acquired competences on EDL. By studying this book, you will know where to locate useful educational data in different sources and understand their limitations; know the basics for managing educational data to make them useful; understand relevant methods; and be able to use relevant tools; know the basics for organising, analysing, interpreting and presenting learner-generated data within their learning context, understand relevant learning analytics methods and be able to use relevant learning analytics tools; know the basics for analysing and interpreting educational data to facilitate educational decision making, including course and curricula design, understand relevant teaching analytics methods and be able to use relevant teaching analytics tools; understand issues related with educational data ethics and privacy. This book is intended for school leaders and teachers engaged in blended (using the flipped classroom model) and online (during COVID-19 crisis and beyond) teaching and learning; e-learning professionals (such as, instructional designers and e-tutors) of online and blended courses; instructional technologists; researchers as well as undergraduate and postgraduate university students studying education, educational technology and relevant fields.

ERP-Systeme gibt es seit einigen Jahrzehnten. Sie haben sich aus ursprunglich einfachen betriebswirtschaftlichen Programmen zu immer ausgefeilteren hochkomplexen Softwarepaketen entwickelt. Einige ihrer Hersteller zahlen heute mit zu den erfolgreichsten boersennotierten Unternehmen. Das Buch beschreibt zunachst die wichtigsten logischen Grundlagen, sodann Funktionalitaten und Loesungen, wie sie in der Wirtschaft gefordert sind. In einem zweiten Teil werden schematisch die heute gangigsten Systeme auf dem Markt vorgestellt und nach festgelegten Kriterien bewertet. Diese Bewertung bietet eine Orientierungshilfe fur Interessierte, die solche Systeme einfuhren bzw. vorhandene in ihren Organisationen ersetzen wollen.

This book presents intelligent data analysis as a tool to fight against COVID-19 pandemic. The intelligent data analysis includes machine learning, natural language processing, and computer vision applications to teach computers to use big data-based models for pattern recognition, explanation, and prediction. These functions are discussed in detail in the book to recognize (diagnose), predict, and explain (treat) COVID-19 infections, and help manage socio-economic impacts. It also discusses primary warnings and alerts; tracking and prediction; data dashboards; diagnosis and prognosis; treatments and cures; and social control by the use of intelligent data analysis. It provides analysis reports, solutions using real-time data, and solution through web applications details.

Data Mining: Concepts and Techniques, Fourth Edition introduces concepts, principles, and methods for mining patterns, knowledge, and models from various kinds of data for diverse applications. Specifically, it delves into the processes for uncovering patterns and knowledge from massive collections of data, known as knowledge discovery from data, or KDD. It focuses on the feasibility, usefulness, effectiveness, and scalability of data mining techniques for large data sets. After an introduction to the concept of data mining, the authors explain the methods for preprocessing, characterizing, and warehousing data. They then partition the data mining methods into several major tasks, introducing concepts and methods for mining frequent patterns, associations, and correlations for large data sets; data classificcation and model construction; cluster analysis; and outlier detection. Concepts and methods for deep learning are systematically introduced as one chapter. Finally, the book covers the trends, applications, and research frontiers in data mining.

Der Weg von der Inbetriebnahme eines Prozessorsystems bis zur Implementierung einer Human Machine Interface (HMI) bildet den Schwerpunkt dieses Werks. Der Autor erlautert, wie Treiber und Betriebssystem (QNX, Linux) konfiguriert, gebaut und geladen werden. Alle notwendigen Kenntnisse werden systematisch und fundiert vermittelt, auch Fragen der Virtualisierung und der Einsatz von MultiCore-Systemen. Der Band enthalt praktische Beispiele sowie Anleitungen fur die Fehlersuche und die Performance-Optimierung, Code-Snippets werden zur Verfugung gestellt."

This book provides users with cutting edge methods and technologies in the area of big data and visual analytics, as well as an insight to the big data and data analytics research conducted by world-renowned researchers in this field. The authors present comprehensive educational resources on big data and visual analytics covering state-of-the art techniques on data analytics, data and information visualization, and visual analytics. Each chapter covers specific topics related to big data and data analytics as virtual data machine, security of big data, big data applications, high performance computing cluster, and big data implementation techniques. Every chapter includes a description of an unique contribution to the area of big data and visual analytics. This book is a valuable resource for researchers and professionals working in the area of big data, data analytics, and information visualization. Advanced-level students studying computer science will also find this book helpful as a secondary textbook or reference.

Leverage the power of Talent Intelligence (TI) to make evidence-informed decisions that drive business performance by using data about people, skills, jobs, business functions and geographies. Improved access to people and business data has created huge opportunities for the HR function. However, simply having access to this data is not enough. HR professionals need to know how to analyse the data, know what questions to ask of it and where and how the insights from the data can add the most value. Talent Intelligence is a practical guide that explains everything HR professionals need to know to achieve this. It outlines what Talent Intelligence (TI) is why it's important, how to use it to improve business results and includes guidance on how HR professionals can build the business case for it. This book also explains how and why talent intelligence is different from workforce planning, sourcing research and standard predictive HR analytics and shows how to assess where in the organization talent intelligence can have the biggest impact and how to demonstrate the results to all stakeholders. Most importantly, this book covers KPIs and metrics for success, short-term and long-term TI goals, an outline of what success looks like and the skills needed for effective Talent Intelligence. It also features case studies from organizations including Philips, Barclays and Kimberly-Clark.

Die Haupteigenschaften des in der Wirtschaft "ausschlaggebenden" Menschen werden sich andern. Wir verlassen die "Bauerngesellschaft" der ruhigen, pflichttreuen Menschen, die Tradition, Erfahrung und Orndung herrschen lassen (Old Economy). die neue Zeit "kampft" mit neuen Geschaftsmodellen und immer schnelleren Technologiezyklen um die Milliarden, die der Erste im Markt erringen kann. Keine Zeit mehr fur Erfahrung & Co. Wie wird das sein - in E-Man's World? Besser? Mit 40 Millionar oder Burnout? Wie lange tobt der Umbruch? E-Man muss vor allem kreativ, proaktiv, authentisch, erneuerungs- und risikofahig sein, voller Verrtrauen im starksten Wandel.

Ein Bericht aus der Turbulenzzone des Managements und des Innermenschlichen. Wie gewohnt spannend, provokativ, streitbar und leidenschaftlich subjektiv Die dritte Auflage wurde um ein Nachwort des Autors erganzt.

This book provides readers the "big picture" and a comprehensive survey of the domain of big data processing systems. For the past decade, the Hadoop framework has dominated the world of big data processing, yet recently academia and industry have started to recognize its limitations in several application domains and thus, it is now gradually being replaced by a collection of engines that are dedicated to specific verticals (e.g. structured data, graph data, and streaming data). The book explores this new wave of systems, which it refers to as Big Data 2.0 processing systems. After Chapter 1 presents the general background of the big data phenomena, Chapter 2 provides an overview of various general-purpose big data processing systems that allow their users to develop various big data processing jobs for different application domains. In turn, Chapter 3 examines various systems that have been introduced to support the SQL flavor on top of the Hadoop infrastructure and provide competing and scalable performance in the processing of large-scale structured data. Chapter 4 discusses several systems that have been designed to tackle the problem of large-scale graph processing, while the main focus of Chapter 5 is on several systems that have been designed to provide scalable solutions for processing big data streams, and on other sets of systems that have been introduced to support the development of data pipelines between various types of big data processing jobs and systems. Next, Chapter 6 focuses on covering the emerging frameworks and systems in the domain of scalable machine learning and deep learning processing. Lastly, Chapter 7 shares conclusions and an outlook on future research challenges. This new and considerably enlarged second edition not only contains the completely new chapter 6, but also offers a refreshed content for the state-of-the-art in all domains of big data processing over the last years. Overall, the book offers a valuable reference guide for professional, students, and researchers in the domain of big data processing systems. Further, its comprehensive content will hopefully encourage readers to pursue further research on the subject.

This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory. Students will find the book both stimulating and accessible, while practitioners will be guided smoothly through the material required for a good grasp of the theory and its applications. The concepts are introduced gradually in accessible and self-contained stages, while the presentation is rigorous and thorough. Pointers to relevant literature and web sites containing software make it an ideal starting point for further study.

This book constitutes revised selected papers of the 9th International Conference on Analysis of Images, Social Networks and Texts, AIST 2020, held in Moscow, Russia, in october 2020. Due to the COVID-19 pandemic the conference was held online. The 14 full papers, 9 short papers and 4 poster papers were carefully reviewed and selected from 108 qualified submissions. The papers are organized in topical sections on natural language processing; computer vision; social network analysis; data analysis and machine learning; theoretical machine learning and optimization; process mining; posters.

Hybride Leistungsbundel (HLB) dienen dazu, ein innovatives und nutzenorientiertes Produktverstandnis von Sach- und Dienstleistungen zu etablieren. Hochkomplexe Anlagen lassen sich durch diese integrierte Betrachtung von Sach- und Dienstleistungsanteilen deutlich besser vermarkten. Der Band liefert einen Uberblick zu diesem Konzept und stellt entsprechende Methoden und Werkzeuge zur Entwicklung von Sach- und Dienstleistungen vor. Dabei berucksichtigen die Autoren den gesamten Zyklus: von der Planung und Entwicklung bis zur Erbringung und Nutzung."

This volume presents the latest advances in statistics and data science, including theoretical, methodological and computational developments and practical applications related to classification and clustering, data gathering, exploratory and multivariate data analysis, statistical modeling, and knowledge discovery and seeking. It includes contributions on analyzing and interpreting large, complex and aggregated datasets, and highlights numerous applications in economics, finance, computer science, political science and education. It gathers a selection of peer-reviewed contributions presented at the 16th Conference of the International Federation of Classification Societies (IFCS 2019), which was organized by the Greek Society of Data Analysis and held in Thessaloniki, Greece, on August 26-29, 2019.

Imagine you are a business user, consultant, or developer about to enter an SAP S/4HANA implementation project. You are well-versed with SAP's product portfolio and you know that the preferred reporting option in S/4HANA is embedded analytics. But what exactly is embedded analytics? And how can it be implemented? And who can do it: a business user, a functional consultant specialized in financial or logistics processes? Or does a business intelligence expert or a programmer need to be involved? Good questions! This book will answer these questions, one by one. It will also take you on the same journey that the implementation team needs to follow for every reporting requirement that pops up: start with assessing a more standard option and only move on to a less standard option if the requirement cannot be fulfilled. In consecutive chapters, analytical apps delivered by SAP, apps created using Smart Business Services, and Analytical Queries developed either using tiles or in a development environment are explained in detail with practical examples. The book also explains which option is preferred in which situation. The book covers topics such as in-memory computing, cloud, UX, OData, agile development, and more. Author Freek Keijzer writes from the perspective of an implementation consultant, focusing on functionality that has proven itself useful in the field. Practical examples are abundant, ranging from "codeless" to "hardcore coding." What You Will Learn Know the difference between static reporting and interactive querying on real-time data Understand which options are available for analytics in SAP S/4HANA Understand which option to choose in which situation Know how to implement these options Who This Book is ForSAP power users, functional consultants, developers