Real-Time Enterprise (RTE) ist eine Unternehmensstruktur, die durch kontinuierliche Beschleunigung der Managementprozesse die eigene Aktions- und Reaktionsgeschwindigkeit nachhaltig verbessert und verzogerungsfrei auf externe sowie interne Anderungen reagieren und damit Zeit und Kosten einsparen kann. Voraussetzungen und Rahmenbedingungen hierfur sind durchgangig integrierte Geschaftsprozesse und Informationsflusse entlang der kompletten Wertschopfungskette. Im Idealfall sind dabei alle Arbeitsprozesse in einem Unternehmen optimal aufeinander abgestimmt, so dass Data Warehouse, Supply Chain Management, Customer Relationship Management und Enterprise Resource Planning wirkungsvoll ineinander greifen. Dabei endet die Betrachtung von Prozessen und Informationen nicht an den Unternehmensgrenzen. Statt dessen sind Kunden und beteiligte Partnerfirmen beziehungsweise deren Prozesse transparent eingebunden und tauschen Informationen uber alle relevanten Ereignisse zeitnah aus. "

In spatial data science, things in closer proximity to one another likely have more in common than things that are farther apart. With this practical book, geospatial professionals, data scientists, business analysts, geographers, geologists, and others familiar with data analysis and visualization will learn the fundamentals of spatial data analysis to gain a deeper understanding of their data questions. Author Bonny P. McClain demonstrates why detecting and quantifying patterns in geospatial data is vital. Both proprietary and open source platforms allow you to process and visualize spatial information. This book is for people familiar with data analysis or visualization who are eager to explore geospatial integration with Python. This book helps you: Understand the importance of applying spatial relationships in data science Select and apply data layering of both raster and vector graphics Apply location data to leverage spatial analytics Design informative and accurate maps Automate geographic data with Python scripts Explore Python packages for additional functionality Work with atypical data types such as polygons, shape files, and projections Understand the graphical syntax of spatial data science to stimulate curiosity

Microsoft Power BI is a data analytics and visualization tool powerful enough for the most demanding data scientists, but accessible enough for everyday use for anyone who needs to get more from data. The market has many books designed to train and equip professional data analysts to use Power BI, but few of them make this tool accessible to anyone who wants to get up to speed on their own. This streamlined intro to Power BI covers all the foundational aspects and features you need to go from "zero to hero" with data and visualizations. Whether you work with large, complex datasets or work in Microsoft Excel, author Jeremey Arnold shows you how to teach yourself Power BI and use it confidently as a regular data analysis and reporting tool. You'll learn how to: Import, manipulate, visualize, and investigate data in Power BI Approach solutions for both self-service and enterprise BI Use Power BI in your organization's business intelligence strategy Produce effective reports and dashboards Create environments for sharing reports and managing data access with your team Determine the right solution for using Power BI offerings based on size, security, and computational needs

Das Buch soll Mainframe-Anwendern helfen, die Philosophie von Datenbanken zu verstehen und selbst Datenbankrecherchen zu formulieren. Hierfur stellt IBMs Query Management Facility, kurz QMF, eine sehr geschickt komponierte Sammlung von Werkzeugen zur Verfugung. Das Buch richtet sich an Endanwender und zeigt, wie man mit Hilfe von QMF fachliche Probleme loesen kann, auch ohne die Entwicklungsabteilung zu bemuhen. Die beigefugte CD-ROM enthalt die Tabellen, die allen Beispielen zugrunde liegen, als ASCII-Dateien und ermoeglicht die praktische Durchfuhrung der Fallbeispiele mit dem Computer.

With this book, managers and decision makers are given the tools to make more informed decisions about big data purchasing initiatives. Big Data Analytics: A Practical Guide for Managers not only supplies descriptions of common tools, but also surveys the various products and vendors that supply the big data market. Comparing and contrasting the different types of analysis commonly conducted with big data, this accessible reference presents clear-cut explanations of the general workings of big data tools. Instead of spending time on HOW to install specific packages, it focuses on the reasons WHY readers would install a given package. The book provides authoritative guidance on a range of tools, including open source and proprietary systems. It details the strengths and weaknesses of incorporating big data analysis into decision-making and explains how to leverage the strengths while mitigating the weaknesses. Describes the benefits of distributed computing in simple terms Includes substantial vendor/tool material, especially for open source decisions Covers prominent software packages, including Hadoop and Oracle Endeca Examines GIS and machine learning applications Considers privacy and surveillance issues The book further explores basic statistical concepts that, when misapplied, can be the source of errors. Time and again, big data is treated as an oracle that discovers results nobody would have imagined. While big data can serve this valuable function, all too often these results are incorrect, yet are still reported unquestioningly. The probability of having erroneous results increases as a larger number of variables are compared unless preventative measures are taken. The approach taken by the authors is to explain these concepts so managers can ask better questions of their analysts and vendors as to the appropriateness of the methods used to arrive at a conclusion. Because the world of science and medicine has been grappling with similar issues in the publication of studies, the authors draw on their efforts and apply them to big data.

To date, statistics has tended to be neatly divided into two theoretical approaches or frameworks: frequentist (or classical) and Bayesian. Scientists typically choose the statistical framework to analyse their data depending on the nature and complexity of the problem, and based on their personal views and prior training on probability and uncertainty. Although textbooks and courses should reflect and anticipate this dual reality, they rarely do so. This accessible textbook explains, discusses, and applies both the frequentist and Bayesian theoretical frameworks to fit the different types of statistical models that allow an analysis of the types of data most commonly gathered by life scientists. It presents the material in an informal, approachable, and progressive manner suitable for readers with only a basic knowledge of calculus and statistics. Statistical Modeling with R is aimed at senior undergraduate and graduate students, professional researchers, and practitioners throughout the life sciences, seeking to strengthen their understanding of quantitative methods and to apply them successfully to real world scenarios, whether in the fields of ecology, evolution, environmental studies, or computational biology.

Die automatische Auswertung von Signalen spielt in der modernen Informationstechnik eine grosse Rolle. Dieses Lehrbuch bietet, ausgehend von der Reprasentation des Signals im Merkmalraum, die Beschreibung wichtiger Klassifikationsverfahren. Dazu zahlen Linear- und Bayes Klassifikatoren, Supportvektormaschinen, Klassifikatoren auf der Basis von Gaussian-Mixture-Modellen und Hidden-Markov-Modellen sowie Klassenfolgenklassifikatoren.Weiterhin werden wichtige Grundlagen der Automatentheorie (Finite State Machines) sowie ausgewahlte maschinelle Lernverfahren dargestellt.Die Darstellung setzt die Verfahren zur Merkmalgewinnung voraus, die im ersten Band vermittelt wurden, so dass das Gesamtwerk eine umfassende Beschreibung der Kette darstellt, die in modernen Systemen der Informationsverarbeitung von der Signalerfassung bis hin zum Klassifikationsergebnis fuhrt.

This book provides a first-hand account of business analytics and its implementation, and an account of the brief theoretical framework underpinning each component of business analytics. The themes of the book include (1) learning the contours and boundaries of business analytics which are in scope; (2) understanding the organization design aspects of an analytical organization; (3) providing knowledge on the domain focus of developing business activities for financial impact in functional analysis; and (4) deriving a whole gamut of business use cases in a variety of situations to apply the techniques. The book gives a complete, insightful understanding of developing and implementing analytical solution.

Building a simple but powerful recommendation system is much easier than you think. Approachable for all levels of expertise, this report explains innovations that make machine learning practical for business production settings--and demonstrates how even a small-scale development team can design an effective large-scale recommendation system. Apache Mahout committers Ted Dunning and Ellen Friedman walk you through a design that relies on careful simplification. You'll learn how to collect the right data, analyze it with an algorithm from the Mahout library, and then easily deploy the recommender using search technology, such as Apache Solr or Elasticsearch. Powerful and effective, this efficient combination does learning offline and delivers rapid response recommendations in real time. Understand the tradeoffs between simple and complex recommenders Collect user data that tracks user actions--rather than their ratings Predict what a user wants based on behavior by others, using Mahoutfor co-occurrence analysis Use search technology to offer recommendations in real time, complete with item metadata Watch the recommender in action with a music service example Improve your recommender with dithering, multimodal recommendation, and other techniques

The last decade has witnessed the rise of big data in game development as the increasing proliferation of Internet-enabled gaming devices has made it easier than ever before to collect large amounts of player-related data. At the same time, the emergence of new business models and the diversification of the player base have exposed a broader potential audience, which attaches great importance to being able to tailor game experiences to a wide range of preferences and skill levels. This, in turn, has led to a growing interest in data mining techniques, as they offer new opportunities for deriving actionable insights to inform game design, to ensure customer satisfaction, to maximize revenues, and to drive technical innovation. By now, data mining and analytics have become vital components of game development. The amount of work being done in this area nowadays makes this an ideal time to put together a book on this subject. Data Analytics Applications in Gaming and Entertainment seeks to provide a cross section of current data analytics applications in game production. It is intended as a companion for practitioners, academic researchers, and students seeking knowledge on the latest practices in game data mining. The chapters have been chosen in such a way as to cover a wide range of topics and to provide readers with a glimpse at the variety of applications of data mining in gaming. A total of 25 authors from industry and academia have contributed 12 chapters covering topics such as player profiling, approaches for analyzing player communities and their social structures, matchmaking, churn prediction and customer lifetime value estimation, communication of analytical results, and visual approaches to game analytics. This book's perspectives and concepts will spark heightened interest in game analytics and foment innovative ideas that will advance the exciting field of online gaming and entertainment.

Making use of data is not anymore a niche project but central to almost every project. With access to massive compute resources and vast amounts of data, it seems at least in principle possible to solve any problem. However, successful data science projects result from the intelligent application of: human intuition in combination with computational power; sound background knowledge with computer-aided modelling; and critical reflection of the obtained insights and results. Substantially updating the previous edition, then entitled Guide to Intelligent Data Analysis, this core textbook continues to provide a hands-on instructional approach to many data science techniques, and explains how these are used to solve real world problems. The work balances the practical aspects of applying and using data science techniques with the theoretical and algorithmic underpinnings from mathematics and statistics. Major updates on techniques and subject coverage (including deep learning) are included. Topics and features: guides the reader through the process of data science, following the interdependent steps of project understanding, data understanding, data blending and transformation, modeling, as well as deployment and monitoring; includes numerous examples using the open source KNIME Analytics Platform, together with an introductory appendix; provides a review of the basics of classical statistics that support and justify many data analysis methods, and a glossary of statistical terms; integrates illustrations and case-study-style examples to support pedagogical exposition; supplies further tools and information at an associated website. This practical and systematic textbook/reference is a "need-to-have" tool for graduate and advanced undergraduate students and essential reading for all professionals who face data science problems. Moreover, it is a "need to use, need to keep" resource following one's exploration of the subject.

Sprachtechnologie in der Anwendung" wurde von Praktikern fur Praktiker geschrieben. Das Buch fuhrt ein in die Technologien zur Sprachverarbeitung und informiert uber den Stand der Technik von Sprachdialogsystemen. Es unterstutzt die Entscheidungsfindung und Vorbereitung bei der Einfuhrung eines Sprachportals, hilft bei der Auswahl der richtigen Systeme und der Vermeidung von Stolpersteinen.

Durch seine klare Sprache, die zahlreichen Praxisbeispiele und das ausfuhrliche Glossar schlagt das Buch eine Brucke zwischen Entscheidern und Technologen im Unternehmen. Es wendet sich sowohl an Manager, die uber die Einfuhrung von Sprachportalen entscheiden, als auch IT-Fachleute, die diese umsetzen. Betreiber von Sprachportalen unterstutzt es bei der Bewertung bestehender Anwendungen und gibt Hinweise zu deren Optimierung."

Das Buch beschreibt die Potenziale zwischenbetrieblicher Kooperationen in Logistiknetzwerken sowie den unternehmensubergreifenden Einsatz von mySAP.com-Losungen. Im Mittelpunkt stehen Anwendungen aus den Bereichen XML/EDI, Data Warehouse, Supply-Chain-Management und Elektronische Marktplatze. Anhand von funf Fallstudien aus der Automobilindustrie wird der Praxiseinsatz von mySAP.com-Losungen demonstriert. Bei den Anwendern handelt es sich um die Unternehmen Bosch GmbH, Goodyear Tire & Rubber Company, Schenker AG, Porsche AG sowie den Elektronischen Marktplatz SupplyOn.
Die vorliegende zweite Auflage wurde vollig uberarbeitet, um den aktuellen Herausforderungen an den Aufbau von Kooperationen sowohl aus Management- als auch aus Softwareperspektive Rechnung zu tragen. Dazu wurde zum einen das Autorenteam erweitert und zum anderen der Fallstudienteil der ersten Auflage ausgebaut."

If you're like most R users, you have deep knowledge and love for statistics. But as your organization continues to collect huge amounts of data, adding tools such as Apache Spark makes a lot of sense. With this practical book, data scientists and professionals working with large-scale data applications will learn how to use Spark from R to tackle big data and big compute problems. Authors Javier Luraschi, Kevin Kuo, and Edgar Ruiz show you how to use R with Spark to solve different data analysis problems. This book covers relevant data science topics, cluster computing, and issues that should interest even the most advanced users. Analyze, explore, transform, and visualize data in Apache Spark with R Create statistical models to extract information and predict outcomes; automate the process in production-ready workflows Perform analysis and modeling across many machines using distributed computing techniques Use large-scale data from multiple sources and different formats with ease from within Spark Learn about alternative modeling frameworks for graph processing, geospatial analysis, and genomics at scale Dive into advanced topics including custom transformations, real-time data processing, and creating custom Spark extensions

SQL is full of difficulties and traps for the unwary. You can avoid them if you understand relational theory, but only if you know how to put that theory into practice. In this book, Chris Date explains relational theory in depth, and demonstrates through numerous examples and exercises how you can apply it to your use of SQL. This third edition has been revised, extended, and improved throughout. Topics whose treatment has been expanded include data types and domains, table comparisons, image relations, aggregate operators and summarization, view updating, and subqueries. A special feature of this edition is a new appendix on NoSQL and relational theory. Could you write an SQL query to find employees who have worked at least once in every programming department in the company? And be sure it's correct? Why is proper column naming so important? Nulls in the database cause wrong answers. Why? What you can do about it? How can image relations help you formulate complex SQL queries? SQL supports "quantified comparisons," but they're better avoided. Why? And how? Database theory and practice have evolved considerably since Codd first defined the relational model, back in 1969. This book draws on decades of experience to present the most up to date treatment of the material available anywhere. Anyone with a modest to advanced background in SQL can benefit from the insights it contains. The book is product independent.

Pipelines can be challenging to manage, especially when your data has to flow through a collection of application components, servers, and cloud services. Airflow lets you schedule, restart, and backfill pipelines, and its easy-to-use UI and workflows with Python scripting has users praising its incredible flexibility. Data Pipelines with Apache Airflow takes you through best practices for creating pipelines for multiple tasks, including data lakes, cloud deployments, and data science. Data Pipelines with Apache Airflow teaches you the ins-and-outs of the Directed Acyclic Graphs (DAGs) that power Airflow, and how to write your own DAGs to meet the needs of your projects. With complete coverage of both foundational and lesser-known features, when you're done you'll be set to start using Airflow for seamless data pipeline development and management. Key Features Framework foundation and best practices Airflow's execution and dependency system Testing Airflow DAGs Running Airflow in production For data-savvy developers, DevOps and data engineers, and system administrators with intermediate Python skills. About the technology Data pipelines are used to extract, transform and load data to and from multiple sources, routing it wherever it's needed -- whether that's visualisation tools, business intelligence dashboards, or machine learning models. Airflow streamlines the whole process, giving you one tool for programmatically developing and monitoring batch data pipelines, and integrating all the pieces you use in your data stack. Bas Harenslak and Julian de Ruiter are data engineers with extensive experience using Airflow to develop pipelines for major companies including Heineken, Unilever, and Booking.com. Bas is a committer, and both Bas and Julian are active contributors to Apache Airflow.

By implementing a comprehensive data analytics program, utility companies can meet the continually evolving challenges of modern grids that are operationally efficient, while reconciling the demands of greenhouse gas legislation and establishing a meaningful return on investment from smart grid deployments.
Readable and accessible, Big Data Analytics Strategies for the Smart Grid addresses the needs of applying big data technologies and approaches, including Big Data cybersecurity, to the critical infrastructure that makes up the electrical utility grid. It supplies industry stakeholders with an in-depth understanding of the engineering, business, and customer domains within the power delivery market.
The book explores the unique needs of electrical utility grids, including operational technology, IT, storage, processing, and how to transform grid assets for the benefit of both the utility business and energy consumers. It not only provides specific examples that illustrate how analytics work and how they are best applied, but also describes how to avoid potential problems and pitfalls.
Discussing security and data privacy, it explores the role of the utility in protecting their customers right to privacy while still engaging in forward-looking business practices. The book includes discussions of:

• SAS for asset management tools
• The AutoGrid approach to commercial analytics
• Space-Time Insight s work at the California ISO (CAISO)

This book is an ideal resource for mid- to upper-level utility executives who need to understand the business value of smart grid data analytics. It explains critical concepts in a manner that will better position executives to make the right decisions about building their analytics programs.
At the same time, the book provides sufficient technical depth that it is useful for data analytics professionals who need to better understand the nuances of the engineering and business challenges unique to the utilities industry.
"

Kann man mit E-Government die Verwaltung optimieren? Eine empirische Studie zur Prozessorientierung von E-Government-Initiativen auf Bundes- und Landesebene steht im Mittelpunkt des Buches. Sie zeigt, dass der Nutzen von E-Government-LAsungen entscheidend davon abhAngt, inwiefern die bestehenden VerwaltungsablAufe verbessert werden kAnnen. Obwohl diese Erkenntnis von der Mehrheit der befragten EntscheidungstrAger unterstA1/4tzt wird, ist das Thema Prozessoptimierung in vielen laufenden E-Government-Projekten noch nicht strukturiert umgesetzt. Vielfach dominiert noch die Technikeuphorie.

Neben einer Bestandsaufnahme der laufenden E-Government-Initiativen in Deutschland bietet das Buch ausfA1/4hrliche Empfehlungen, wie ein systematisches Prozessmanagement in die E-Government-Programme der Affentlichen Verwaltung integriert werden kann. Fazit: Kein E-Government-Erfolg ohne ProzessverAnderung

Environmental science (ecology, conservation, and resource management) is an increasingly quantitative field. A well-trained ecologist now needs to evaluate evidence generated from complex quantitative methods, and to apply these methods in their own research. Yet the existing books and academic coursework are not adequately serving most of the potential audience - instead they cater to the specialists who wish to focus on either mathematical or statistical aspects, and overwhelmingly appeal to those who already have confidence in their quantitative skills. At the same time, many texts lack an explicit emphasis on the epistemology of quantitative techniques. That is, how do we gain understanding about the real world from models that are so vastly simplified? This accessible textbook introduces quantitative ecology in a manner that aims to confront these limitations and thereby appeal to a far wider audience. It presents material in an informal, approachable, and encouraging manner that welcomes readers with any degree of confidence and prior training. It covers foundational topics in both mathematical and statistical ecology before describing how to implement these concepts to choose, use, and analyse models, providing guidance and worked examples in both spreadsheet format and R. The emphasis throughout is on the skilful interpretation of models to answer questions about the natural world. Introduction to Quantitative Ecology is suitable for advanced undergraduate students and incoming graduate students, seeking to strengthen their understanding of quantitative methods and to apply them successfully to real world ecology, conservation, and resource management scenarios.

Immer mehr Kreative nutzen die Moglichkeit, eigene dreidimensionale Objekte in Kunststoff, Metall oder Keramik schnell und preisgunstig herstellen zu lassen. Der 3D-Druck ist eine revolutionare Technologie, die dieVerwirklichung von Ideen ermoglicht. 3D-Drucker werden immer kleiner und leistungsstarker und damit burotauglicher. Eine umfassende Beschreibung dieser Zukunftstechnologie bietet dieses praxisnahe und anwenderorientierte Buch. Dabei hilft es mit Tipps und Hinweisen bei der Auswahl des optimalen CAD-Programms und 3D-Druckers."

What is latent class analysis? If you asked that question thirty or forty years ago you would have gotten a different answer than you would today. Closer to its time of inception, latent class analysis was viewed primarily as a categorical data analysis technique, often framed as a factor analysis model where both the measured variable indicators and underlying latent variables are categorical. Today, however, it rests within much broader mixture and diagnostic modeling framework, integrating measured and latent variables that may be categorical and/or continuous, and where latent classes serve to define the subpopulations for whom many aspects of the focal measured and latent variable model may differ. For latent class analysis to take these developmental leaps required contributions that were methodological, certainly, as well as didactic. Among the leaders on both fronts was C. Mitchell "Chan" Dayton, at the University of Maryland, whose work in latent class analysis spanning several decades helped the method to expand and reach its current potential. The current volume in the Center for Integrated Latent Variable Research (CILVR) series reflects the diversity that is latent class analysis today, celebrating work related to, made possible by, and inspired by Chan's noted contributions, and signaling the even more exciting future yet to come.

What is the cost of employees today and what will this be in the future? This book explains how to take a data-driven approach to workforce planning and allow the business to reach its strategic goals. Organizational Planning and Analysis (OP&A) is a data-driven approach to workforce planning. It allows HR professionals, OD practitioners and business leaders to monitor an organization's activities and analyse business data to regularly adjust plans to ensure that the business succeeds. This book covers everything from how to build an OP&A function, the difference between strategic and operational workforce planning and how to manage demand and supply through to how to match people to new or changing roles and develop robust succession planning. Organizational Planning and Analysis also covers how OP&A works with HR operations including recruitment, L&D, reward and performance management and includes a chapter on new human capital analytics which allow a business to improve the return on investment for each of its employees. Full of practical advice and step by step guidance, this book is also supported by case studies from organizations including KPMG, Sainsbury's, WPP, Accenture, TSB, Johnson & Johnson, Aer Lingus and FedEx.

Konzentrationstendenzen, Globalisierung und gut informierte Kunden sind Belege f r den harten Wettbewerb, in dem sich Handelsunternehmen befinden. Um in diesem Wettbewerb zu bestehen, ben tigen H ndler flexibel an die jeweilige Unternehmensstruktur anpassbare Informations- und Kommunikationssysteme, die die operativen Abl ufe, Beschaffung, Lagerung und Distribution und die betriebswirtschaftlich-administrativen Aufgaben der Buchhaltung, Kostenrechnung und Personalwirtschaft unterst tzen und aussagekr ftige Auswertungssysteme umfassen. Dar ber hinaus sind Informations- und Planungssysteme zur Unterst tzung von Marketing und Management heute kritischer Erfolgsfaktor. Das Buch stellt die Architektur von Handelsinformationssystemen am Beispiel des SAP Retail-Systems dar. Es zeigt auf, wie modernes Handelsmanagement durch Einsatz integrierter Standardsoftware realisiert werden kann.

The statistical analyses that students of the life-sciences are being expected to perform are becoming increasingly advanced. Whether at the undergraduate, graduate, or post-graduate level, this book provides the tools needed to properly analyze your data in an efficient, accessible, plainspoken, frank, and occasionally humorous manner, ensuring that readers come away with the knowledge of which analyses they should use and when they should use them. The book uses the statistical language R, which is the choice of ecologists worldwide and is rapidly becoming the 'go-to' stats program throughout the life-sciences. Furthermore, by using a single, real-world dataset throughout the book, readers are encouraged to become deeply familiar with an imperfect but realistic set of data. Indeed, early chapters are specifically designed to teach basic data manipulation skills and build good habits in preparation for learning more advanced analyses. This approach also demonstrates the importance of viewing data through different lenses, facilitating an easy and natural progression from linear and generalized linear models through to mixed effects versions of those same analyses. Readers will also learn advanced plotting and data-wrangling techniques, and gain an introduction to writing their own functions. Applied Statistics with R is suitable for senior undergraduate and graduate students, professional researchers, and practitioners throughout the life-sciences, whether in the fields of ecology, evolution, environmental studies, or computational biology.

Anwendbarkeit des Mediendienstestaatsvertrages oder handelt es sich um Rund funk mit der Folge der Anwendung der Rundfunkgesetzes der Lander? Der zweite Abschnitt behandelt den "Rechtsverkehr im Internet'. Zunachst wird in Kapitel 3 der "Vertragsschluss im Internet" nach deutschem Recht erfasst. In Kapitel 15 ("Electronic Commerce im Internet") und 16 ("Rechtsfragen des In ternet-Vertriebs von Versicherungsdienstleistungen" werden die europaischen Re gelungen - insbesondere aus der Sicht des Verbraucherschutzes - hierzu bereits an tizipiert. Ferner gilt es zu berucksichtigen, dass der Geschaftsverkehr uber das In ternet eine zusatzliche Flankierung durch die Moglichkeit der Abwicklung von "Zahlungsverkehr im Internet' erhalt. Die zahlreichen rechtlichen Probleme, die mit der Verwendung von Cybermoney etc. auftauchen, werden in Kapitel 4 aufge griffen. Das Kapitel 5 behandelt sodann mit dem Thema, Rechtssicherheit im digitalen Rechtsverkehr'' einen zentralen Gesichtspunkt des Rechtsverkehrs. Dabei wird neben dem deutschen Signaturgesetz samt Signaturverordnung auch die eu ropaische Rechtsentwicklung berucksichtigt. Der dritte Abschnitt umfasst "die Rechtsstellung der Beteiligten." Zentral hier fiir ist die Frage nach der Verantwortlichkeit die sowohl den Diensteanbieter Kapi tel 6) als auch den Netzbelreiber (Kapitel 7) betrifft. Die strafrechtliche Perspek tive wird gesondert in Kapitel 18 aufgenommen. Eine in der Praxis immer haufi ger auftretende Frage gilt der Einordnung der "Vertragsgestaltung zwischen den Beteiligten" woruber Kapitel 8 Auskunft gibt."