Tourism is one of the leading industries worldwide. The magnitude of growth in tourism will bring both opportunities and problems to source and destination markets in years to come, especially in the internal and external exchange of information in the industry. ""Information and Communication Technologies in Support of the Tourism Industry"" examines the process of transformation as it relates to the tourism industry, and the changes to that industry from modern electronic communications. ""Information and Communication Technologies in Support of the Tourism Industry"" covers not only geographically supportive technologies in communication, but also in terms of culture, economics, marketing, social, and regional issues. In-depth analyses range from the use of the Internet to supply information to the emerging patterns of tourist decision making and investments.

This volume conveys some of the surprises, puzzles and success stories in high-dimensional and complex data analysis and related fields. Its peer-reviewed contributions showcase recent advances in variable selection, estimation and prediction strategies for a host of useful models, as well as essential new developments in the field. The continued and rapid advancement of modern technology now allows scientists to collect data of increasingly unprecedented size and complexity. Examples include epigenomic data, genomic data, proteomic data, high-resolution image data, high-frequency financial data, functional and longitudinal data, and network data. Simultaneous variable selection and estimation is one of the key statistical problems involved in analyzing such big and complex data. The purpose of this book is to stimulate research and foster interaction between researchers in the area of high-dimensional data analysis. More concretely, its goals are to: 1) highlight and expand the breadth of existing methods in big data and high-dimensional data analysis and their potential for the advancement of both the mathematical and statistical sciences; 2) identify important directions for future research in the theory of regularization methods, in algorithmic development, and in methodologies for different application areas; and 3) facilitate collaboration between theoretical and subject-specific researchers.

Linear mixed-effects models (LMMs) are an important class of statistical models that can be used to analyze correlated data. Such data are encountered in a variety of fields including biostatistics, public health, psychometrics, educational measurement, and sociology. This book aims to support a wide range of uses for the models by applied researchers in those and other fields by providing state-of-the-art descriptions of the implementation of LMMs in R. To help readers to get familiar with the features of the models and the details of carrying them out in R, the book includes a review of the most important theoretical concepts of the models. The presentation connects theory, software and applications. It is built up incrementally, starting with a summary of the concepts underlying simpler classes of linear models like the classical regression model, and carrying them forward to LMMs. A similar step-by-step approach is used to describe the R tools for LMMs. All the classes of linear models presented in the book are illustrated using real-life data. The book also introduces several novel R tools for LMMs, including new class of variance-covariance structure for random-effects, methods for influence diagnostics and for power calculations. They are included into an R package that should assist the readers in applying these and other methods presented in this text.

This book is designed as a gentle introduction to the fascinating field of choice modeling and its practical implementation using the R language. Discrete choice analysis is a family of methods useful to study individual decision-making. With strong theoretical foundations in consumer behavior, discrete choice models are used in the analysis of health policy, transportation systems, marketing, economics, public policy, political science, urban planning, and criminology, to mention just a few fields of application. The book does not assume prior knowledge of discrete choice analysis or R, but instead strives to introduce both in an intuitive way, starting from simple concepts and progressing to more sophisticated ideas. Loaded with a wealth of examples and code, the book covers the fundamentals of data and analysis in a progressive way. Readers begin with simple data operations and the underlying theory of choice analysis and conclude by working with sophisticated models including latent class logit models, mixed logit models, and ordinal logit models with taste heterogeneity. Data visualization is emphasized to explore both the input data as well as the results of models. This book should be of interest to graduate students, faculty, and researchers conducting empirical work using individual level choice data who are approaching the field of discrete choice analysis for the first time. In addition, it should interest more advanced modelers wishing to learn about the potential of R for discrete choice analysis. By embedding the treatment of choice modeling within the R ecosystem, readers benefit from learning about the larger R family of packages for data exploration, analysis, and visualization.

The papers in this volume represent the most timely and advanced contributions to the 2014 Joint Applied Statistics Symposium of the International Chinese Statistical Association (ICSA) and the Korean International Statistical Society (KISS), held in Portland, Oregon. The contributions cover new developments in statistical modeling and clinical research: including model development, model checking, and innovative clinical trial design and analysis. Each paper was peer-reviewed by at least two referees and also by an editor. The conference was attended by over 400 participants from academia, industry, and government agencies around the world, including from North America, Asia, and Europe. It offered 3 keynote speeches, 7 short courses, 76 parallel scientific sessions, student paper sessions, and social events.

With the increasing advances in hardware technology for data collection, and advances in software technology (databases) for data organization, computer scientists have increasingly participated in the latest advancements of the outlier analysis field. Computer scientists, specifically, approach this field based on their practical experiences in managing large amounts of data, and with far fewer assumptions- the data can be of any type, structured or unstructured, and may be extremely large. Outlier Analysis is a comprehensive exposition, as understood by data mining experts, statisticians and computer scientists. The book has been organized carefully, and emphasis was placed on simplifying the content, so that students and practitioners can also benefit. Chapters will typically cover one of three areas: methods and techniques commonly used in outlier analysis, such as linear methods, proximity-based methods, subspace methods, and supervised methods; data domains, such as, text, categorical, mixed-attribute, time-series, streaming, discrete sequence, spatial and network data; and key applications of these methods as applied to diverse domains such as credit card fraud detection, intrusion detection, medical diagnosis, earth science, web log analytics, and social network analysis are covered.

"This volume provides essential guidance for transforming mathematics learning in schools through the use of innovative technology, pedagogy, and curriculum. It presents clear, rigorous evidence of the impact technology can have in improving students learning of important yet complex mathematical concepts -- and goes beyond a focus on technology alone to clearly explain how teacher professional development, pedagogy, curriculum, and student participation and identity each play an essential role in transforming mathematics classrooms with technology. Further, evidence of effectiveness is complemented by insightful case studies of how key factors lead to enhancing learning, including the contributions of design research, classroom discourse, and meaningful assessment.
The volume organizes over 15 years of sustained research by multiple investigators in different states and countries who together developed an approach called "SimCalc" that radically transforms how Algebra and Calculus are taught.
The SimCalc program engages students around simulated motions, such as races on a soccer field, and builds understanding using visual representations such as graphs, and familiar representations such as stories to help students to develop meaning for more abstract mathematical symbols. Further, the SimCalc program leverages classroom wireless networks to increase participation by all students in doing, talking about, and reflecting on mathematics. Unlike many technology programs, SimCalc research shows the benefits of balanced attention to curriculum, pedagogy, teacher professional development, assessment and technology -- and has proven effectiveness results at the scale of hundreds of schools and classrooms.
Combining the findings of multiple investigators in one accessible volume reveals the depth and breadth of the research program, andengages readers interested in: "

"* Engaging students in deeply learning the important concepts in mathematics
* Designing innovative curriculum, software, and professional development
. Effective uses of technology to improve mathematics education
* Creating integrated systems of teaching that transform mathematics classrooms
* Scaling up new pedagogies to hundreds of schools and classrooms
* Conducting research that really matters for the future of mathematics learning"

"* Engaging students in deeply learning the important concepts in mathematics
* Designing innovative curriculum, software, and professional development
. Effective uses of technology to improve mathematics education
* Creating integrated systems of teaching that transform mathematics classrooms
* Scaling up new pedagogies to hundreds of schools and classrooms
* Conducting research that really matters for the future of mathematics learning"

"

"Managing Data in Motion" describes techniques that have been developed for significantly reducing the complexity of managing system interfaces and enabling scalable architectures. Author April Reeve brings over two decades of experience to present a vendor-neutral approach to moving data between computing environments and systems. Readers will learn the techniques, technologies, and best practices for managing the passage of data between computer systems and integrating disparate data together in an enterprise environment.

The average enterprise's computing environment is comprised of hundreds to thousands computer systems that have been built, purchased, and acquired over time. The data from these various systems needs to be integrated for reporting and analysis, shared for business transaction processing, and converted from one format to another when old systems are replaced and new systems are acquired.

The management of the "data in motion" in organizations is rapidly becoming one of the biggest concerns for business and IT management. Data warehousing and conversion, real-time data integration, and cloud and "big data" applications are just a few of the challenges facing organizations and businesses today. "Managing Data in Motion" tackles these and other topics in a style easily understood by business and IT managers as well as programmers and architects.
Presents a vendor-neutral overview of the different technologies and techniques for moving data between computer systems including the emerging solutions for unstructured as well as structured data typesExplains, in non-technical terms, the architecture and components required to perform data integration Describes how to reduce the complexity of managing system interfaces and enable a scalable data architecture that can handle the dimensions of "Big Data"

This Festschrift in honour of Ursula Gather's 60th birthday deals with modern topics in the field of robust statistical methods, especially for time series and regression analysis, and with statistical methods for complex data structures. The individual contributions of leading experts provide a textbook-style overview of the topic, supplemented by current research results and questions. The statistical theory and methods in this volume aim at the analysis of data which deviate from classical stringent model assumptions, which contain outlying values and/or have a complex structure. Written for researchers as well as master and PhD students with a good knowledge of statistics.

This book provides new insights on the study of global environmental changes using the ecoinformatics tools and the adaptive-evolutionary technology of geoinformation monitoring. The main advantage of this book is that it gathers and presents extensive interdisciplinary expertise in the parameterization of global biogeochemical cycles and other environmental processes in the context of globalization and sustainable development. In this regard, the crucial global problems concerning the dynamics of the nature-society system are considered and the key problems of ensuring the system's sustainable development are studied. A new approach to the numerical modeling of the nature-society system is proposed and results are provided on modeling the dynamics of the system's characteristics with regard to scenarios of anthropogenic impacts on biogeochemical cycles, land ecosystems and oceans. The main purpose of this book is to develop a universal guide to information-modeling technologies for assessing the function of environmental subsystems under various climatic and anthropogenic conditions.

The contributions gathered here provide an overview of current research projects and selected software products of the Fraunhofer Institute for Algorithms and Scientific Computing SCAI. They show the wide range of challenges that scientific computing currently faces, the solutions it offers, and its important role in developing applications for industry. Given the exciting field of applied collaborative research and development it discusses, the book will appeal to scientists, practitioners, and students alike. The Fraunhofer Institute for Algorithms and Scientific Computing SCAI combines excellent research and application-oriented development to provide added value for our partners. SCAI develops numerical techniques, parallel algorithms and specialized software tools to support and optimize industrial simulations. Moreover, it implements custom software solutions for production and logistics, and offers calculations on high-performance computers. Its services and products are based on state-of-the-art methods from applied mathematics and information technology.

In today s world, most global companies face enormous challenges in dealing with an inflexible budget climate when complex changes are required. "Secrets to a Successful Commercial Software Implementation" will help guide business leaders to gain understanding of how commercial, off-the-shelf (COTS) software like SAP, Seibel, and PeopleSoft should be applied in order to ultimately achieve significant cost savings.

Project management professional Nick Berg utilizes his strong background in domestic and international Systems Applications and Products to teach others the potential benefits of implementing COTS products such as faster deployment time, enhanced quality and reliability, reduced development risk, provided periodic upgrades and improvements, and an already established support system. He introduces a unique process for COTS development, presents best-practice processes for COTS projects, and defines the architecture procedures within the COTS environment. Finally, he walks through each project phase of a COTS-based project by introducing the objectives, road map, roles, activities, artifacts, and milestone of the phase.

The cultural impact on an organization facing this decision is profound, but if implemented with forethought, planning, and dedicated guidance and execution, the benefits to an organization will be long reaching and significant.

This book introduces the ade4 package for R which provides multivariate methods for the analysis of ecological data. It is implemented around the mathematical concept of the duality diagram, and provides a unified framework for multivariate analysis. The authors offer a detailed presentation of the theoretical framework of the duality diagram and also of its application to real-world ecological problems. These two goals may seem contradictory, as they concern two separate groups of scientists, namely statisticians and ecologists. However, statistical ecology has become a scientific discipline of its own, and the good use of multivariate data analysis methods by ecologists implies a fair knowledge of the mathematical properties of these methods. The organization of the book is based on ecological questions, but these questions correspond to particular classes of data analysis methods. The first chapters present both usual and multiway data analysis methods. Further chapters are dedicated for example to the analysis of spatial data, of phylogenetic structures, and of biodiversity patterns. One chapter deals with multivariate data analysis graphs. In each chapter, the basic mathematical definitions of the methods and the outputs of the R functions available in ade4 are detailed in two different boxes. The text of the book itself can be read independently from these boxes. Thus the book offers the opportunity to find information about the ecological situation from which a question raises alongside the mathematical properties of methods that can be applied to answer this question, as well as the details of software outputs. Each example and all the graphs in this book come with executable R code.

Organisationen im Wandel der Markte: A.-W. Scheer, R. Borowsky, U. Markus: Neue Markte, neue Medien, neue Methoden - Roadmap zur agilen Organisation; B. Anderer, K. Knue: Sichere Transaktionen in Electronic Banking und Electronic Commerce; D. Budaus: Public Private Partnership als innovative Organisationsform; U. Dalkmann, F. Karbenn: Energieabrechnug im Wandel - Der Weg zum Kunden uber leistungsfahigen 'Customer Service'; E. Frese: Von der Planwirtschaft zur Marktwirtschaft - auch in der Unternehmung?; P. Neef, M. Moeller: Erfolg im Netz; E. Rauch: Bankenfusionen.- Methoden der Organisationsentwicklung: P. Hintermann, W. Hoffmann, C.-P. Koch: Prozessorientiertes Informationssystem als Voraussetzung fur eine erfolgreiche Unternehmensintegration; M. Lapp: Intranet - Internes Internet; R. Minz: IT als Managementaufgabe begreifen; A. Muller: Vom Geschaftsprozessdesign zum prozessorientierten Managementsystem; S. Neumann, G. Fenk, B. Fluegge, J.T. Finerty: Knowledge Management Systems - optimaler Einsatz des 'Produktionsfaktors Wissen'; M. Pastowsky, F. Hausen-Mabilon: Gestaltung von Kommunikationsprozessen im Entwicklungsbereich: Rahmenbedingungen, Vorgehen und Beispiele; A. Poscay: Mit neuen Medien zu einem effizienten Beratungsnetzwerk.- M. Reiss Wandel im Management des Wandels; J. Hagemeyer, R. Rolles, Y. Schmidt, J. Bachmann, A. Haas: Dynamische Prozesse durch workflow-zentrierte Geschaftsprozessoptimierung: Herausforderungen in der Praxis; J. Schweitzer, H. Baltes, K. Merschjahn, G. Schneider: Professionelle Telekooperation fur das Teammanagement in virtuellen Unternehmen; H.-G. Servatius: Vom Reengineering zum Wissensmanagement.- Anforderungen an das Controlling: J. Fiedler, G. Barzel, K. Vernau: Kosten- und Leistungsrechnung als Steuerungsinstrument - flachendeckende und zugige Einfuhrung in einer deutschen Grossstadt; H. Frei: Mit Qualitatscontrolling auf dem Weg zum European Quality Award (EQA); O. Froehling: Controlling goes Multimedia; P. Hirschmann: Prozesskostenrechnerische Bewertung von Dienstleistungen zur Verbesserung der innerbetrieblichen Leistungsverrechnung; P. Horvath: Mit Balanced Scorecard Strategien erfolgreich umsetzen; R. Mahnkopf: Neues Kommunales Rechnungswesen - eine neue Methode fur ein anspruchsvolles Verwaltungsinformationssystem; R. Moser: Neue Perspektiven durch die Istkostenrechnung; H. Neukam: Neues Controlling durch integrierte Standardsoftware; K. Vikas: Trends und neue Entwicklungen im Controlling; A. Hoffmann, K. Wolf: Wertorientiertes Management auch fur Informatik Investitionen?; J. Zinkernagel: Prozesscontrolling im Entwicklungsprozess eines Automobilzulieferers; W. Kraemer, F. Milius, V. Zimmermann: Elektronische Bildungsmarkte fur ein integriertes Wissens- und Qualitatsmanagement.

Biological and biomedical studies have entered a new era over the past two decades thanks to the wide use of mathematical models and computational approaches. A booming of computational biology, which sheerly was a theoretician's fantasy twenty years ago, has become a reality. Obsession with computational biology and theoretical approaches is evidenced in articles hailing the arrival of what are va- ously called quantitative biology, bioinformatics, theoretical biology, and systems biology. New technologies and data resources in genetics, such as the International HapMap project, enable large-scale studies, such as genome-wide association st- ies, which could potentially identify most common genetic variants as well as rare variants of the human DNA that may alter individual's susceptibility to disease and the response to medical treatment. Meanwhile the multi-electrode recording from behaving animals makes it feasible to control the animal mental activity, which could potentially lead to the development of useful brain-machine interfaces. - bracing the sheer volume of genetic, genomic, and other type of data, an essential approach is, ?rst of all, to avoid drowning the true signal in the data. It has been witnessed that theoretical approach to biology has emerged as a powerful and st- ulating research paradigm in biological studies, which in turn leads to a new - search paradigm in mathematics, physics, and computer science and moves forward with the interplays among experimental studies and outcomes, simulation studies, and theoretical investigations.

This book presents various recently developed and traditional statistical techniques, which are increasingly being applied in social science research. The social sciences cover diverse phenomena arising in society, the economy and the environment, some of which are too complex to allow concrete statements; some cannot be defined by direct observations or measurements; some are culture- (or region-) specific, while others are generic and common. Statistics, being a scientific method - as distinct from a 'science' related to any one type of phenomena - is used to make inductive inferences regarding various phenomena. The book addresses both qualitative and quantitative research (a combination of which is essential in social science research) and offers valuable supplementary reading at an advanced level for researchers.

This book provides a general framework for learning sparse graphical models with conditional independence tests. It includes complete treatments for Gaussian, Poisson, multinomial, and mixed data; unified treatments for covariate adjustments, data integration, and network comparison; unified treatments for missing data and heterogeneous data; efficient methods for joint estimation of multiple graphical models; effective methods of high-dimensional variable selection; and effective methods of high-dimensional inference. The methods possess an embarrassingly parallel structure in performing conditional independence tests, and the computation can be significantly accelerated by running in parallel on a multi-core computer or a parallel architecture. This book is intended to serve researchers and scientists interested in high-dimensional statistics, and graduate students in broad data science disciplines. Key Features: A general framework for learning sparse graphical models with conditional independence tests Complete treatments for different types of data, Gaussian, Poisson, multinomial, and mixed data Unified treatments for data integration, network comparison, and covariate adjustment Unified treatments for missing data and heterogeneous data Efficient methods for joint estimation of multiple graphical models Effective methods of high-dimensional variable selection Effective methods of high-dimensional inference

Dynamic Business Process Formation fuses practical needs with theoretical input to present important research innovations in supporting Instant Virtual Enterprises (IVEs). This new organization type brings a combination of business dynamism and explicit business process structure to domains where on-the-fly formation of well-organized business networks is required to deal with the complexity of new products or services under high time pressure. This book contains the main results of the IST CrossWork project, and, importantly, looks beyond the boundaries of this project and sources input from related projects and general trends in collaborative enterprises and the automotive industry. Both the business and technical aspects of Virtual Enterprise coordination are covered within the modular structure of the book, which enables readers from different backgrounds to benefit from the book according to their interests.

Multiple criteria decision aid (MCDA) methods are illustrated in this book through theoretical and computational techniques utilizing Python. Existing methods are presented in detail with a step by step learning approach. Theoretical background is given for TOPSIS, VIKOR, PROMETHEE, SIR, AHP, goal programming, and their variations. Comprehensive numerical examples are also discussed for each method in conjunction with easy to follow Python code. Extensions to multiple criteria decision making algorithms such as fuzzy number theory and group decision making are introduced and implemented through Python as well. Readers will learn how to implement and use each method based on the problem, the available data, the stakeholders involved, and the various requirements needed. Focusing on the practical aspects of the multiple criteria decision making methodologies, this book is designed for researchers, practitioners and advanced graduate students in the applied mathematics, information systems, operations research and business administration disciplines, as well as other engineers and scientists oriented in interdisciplinary research. Readers will greatly benefit from this book by learning and applying various MCDM/A methods. (Adiel Teixeira de Almeida, CDSID-Center for Decision System and Information Development, Universidade Federal de Pernambuco, Recife, Brazil) Promoting the development and application of multicriteria decision aid is essential to ensure more ethical and sustainable decisions. This book is a great contribution to this objective. It is a perfect blend of theory and practice, providing potential users and researchers with the theoretical bases of some of the best-known methods as well as with the computing tools needed to practice, to compare and to put these methods to use. (Jean-Pierre Brans, Vrije Universiteit Brussel, Brussels, Belgium) This book is intended for researchers, practitioners and students alike in decision support who wish to familiarize themselves quickly and efficiently with multicriteria decision aiding algorithms. The proposed approach is original, as it presents a selection of methods from the theory to the practical implementation in Python, including a detailed example. This will certainly facilitate the learning of these techniques, and contribute to their effective dissemination in applications. (Patrick Meyer, IMT Atlantique, Lab-STICC, Univ. Bretagne Loire, Brest, France)