- Table View
- List View
Das sowjetische Fieber: Fußballfans im poststalinistischen Vielvölkerreich (Soviet and Post-Soviet Politics and Society #136)
by Manfred ZellerWhat did the citizens of the Soviet Union identify with? Where did the societal faultlines lie? Did mass demonstrations destabilize Soviet order? How did informal groups come into being within a society based on uniformity? What impact did media and new forms of connection have on the development of a multinational Soviet society? What remained after the end of the Soviet Union? Using Soviet soccer teams from Moscow (Spartak, Dynamo, ZSKA) and Kiev (Dynamo) as examples, Manfred Zeller tells a story of community and enmity in the post-Stalinist empire. He analyzes the complex loyalties that governed group identities and explains phenomena like the love-hate relationship between Kiev and Moscow. 'Moscow against Kiev' in Soviet times was not a question of war and peace, but in soccer there was already a feeling of 'us against them.' Zeller's book is an important contribution to research on Soviet culture after Stalin as well as to contemporary debates on antagonism in the post-Soviet world.
Das transformative Potenzial von Konsum zwischen Nachhaltigkeit und Digitalisierung: Chancen und Risiken (Kritische Verbraucherforschung)
by Renate Hübner Barbara SchmonWelche Bedeutung hat Konsum in der gesellschaftlichen Umbruchphase zwischen Klimawandel und Digitalisierungsprozessen? Konsum im Sinne einer nachhaltigen Entwicklung zu verändern und dabei Digitalisierung für eine gesellschaftliche Transformation aktiv zu nützen, ist eng verknüpft sowohl mit einem veränderten Konsumverständnis, als auch mit teilweise neuen Rollen der Akteure. Der vorliegende Band bietet Zugänge, wie durch nachhaltigkeitsorientierte Forschung und Bildung diese neuen Rollen und das damit verbundene transformative Potenzial von Konsum sichtbar und nutzbar gemacht werden können. Diese Zielsetzung ist verbunden mit dem Anliegen, VerbraucherInnen zu unterstützen, Konsumformen im Sinne einer Circular Economy zu praktizieren und Konsumhandlungen auch abseits marktvermittelter Lösungen stärker zu berücksichtigen.Der Inhalt• Nachhaltiger Konsum als Utopie? Wie kann Konsum transformative Kraft entwickeln? Soziale Praktiken ändern, nicht die Individuen! Über die transformativen Potenziale des „Scheiterns“• „Smarte“ Konsumwende und „Smarte“ Technologien? Chancen und Grenzen der Digitalisierung für klimafreundlichen Konsum• on/offline: Digitalisierung zwischen diskursivem politischen Konsum und dem pathogenen Potenzial von Konsum• Verbraucherbildung im und für Wandel: Normative, konzeptionelle und curriculare Transformationen im Spiegel konsumgesellschaftlicher Entwicklungen und das Problem der Grenzziehungen in heterogenen Klassen• Wie können Konferenzen durch ihre Gestaltung transformativ wirken?Die Herausgeberinnen Dr. Renate Hübner ist Nachhaltigkeitsforscherin und leitet den Studienbereich Nachhaltige Entwicklung an der Alpen-Adria-Universität Klagenfurt.Dr. Barbara Schmon ist Expertin für „Nachhaltigen Konsum“ im österreichischen Bundesministerium für Nachhaltigkeit und Tourismus (BMNT) in Wien.
Das Verbot der vertikalen Preisbindung: Eine betriebswirtschaftliche Analyse am Beispiel der Lebensmittelbranche (Unternehmenskooperation und Netzwerkmanagement)
by Benjamin C. ScheferInfolge aktueller Entwicklungen in der Kartellrechtspraxis wird das Preisbindungsverbot derzeit wieder kontrovers unter Vertretern der Rechtswissenschaft, der Wettbewerbstheorie und der Betriebswirtschaftslehre diskutiert. Benjamin C. Schefer analysiert in diesem Zusammenhang die marketingorientierte Argumentationslinie der Münsteraner Distributions- und Handelsforschung mit Hilfe einer qualitativ angelegten, empirischen Untersuchung. Auf Basis der vorrangig in der Lebensmittelbranche gewonnenen Erkenntnisse wird am Ende eine Empfehlung abgeleitet, wie die Vertikalmaßnahme aus ökonomischer Perspektive kartellrechtlich behandelt werden sollte.
Das vergessene Subjekt: Subjektkonstitutionen in mediatisierten Alltagswelten (Medien • Kultur • Kommunikation)
by Peter Gentzel Friedrich Krotz Jeffrey Wimmer Rainer WinterDer Band liefert eine kritische Bestandsaufnahme bestehender Subjektkonzeptionen der kommunikationswissenschaftlichen Forschung. Zudem werden Konzepte entwickelt um Subjektivität im Kontext aktueller theoretischer Debatten (u.a. Mediensoziologie, Cultural Studies, Psychoanalyse, Praxistheorie, Science and Technology Studies) sowie sozialer, kultureller und technischer Entwicklungen (u.a. Digitalisierung, Mediatisierung, Mobilität und Vernetzung) analysieren zu können. Da Subjektkonzeptionen für jegliche Kommunikations- und Medienanalysen von zentraler Bedeutung sind, schließt der Band eine zentrale Leerstelle der Kommunikations- und Medienwissenschaft.
Das Verhältnis von Kriminalität und Ökonomie: Eine empirische Studie am Beispiel der Privatisierung ehemaliger DDR-Betriebe
by Ingo TechmeierDas Verhältnis von Kriminalität und Ökonomie ist weitgehend unbekannt. Ingo Techmeier analysiert im Rahmen eines qualitativen Forschungsprojekts zur Wirtschaftskriminalität anlässlich der Privatisierung ehemaliger DDR-Betriebe dieses Verhältnis. Im Rückgriff auf ökonomische und kriminologische Theoreme mit geringem empirischen Gehalt untersucht er folgende Fragen: Welche ökonomische Funktion hat kriminelles Handeln und warum ist es Teil der ökonomischen Dynamik? Welcher Zusammenhang besteht zwischen wirtschaftskriminellen Handlungen und dem unternehmerischen Zeithorizont? Wieso halten unternehmerische Akteure das Strafrecht für unverzichtbar, stellen jedoch in der Regel selber keine Strafanzeige? Der Autor beschreibt ein widersprüchliches Verhältnis, in dem sich strafrechtliche Konflikte nicht vollständig vermeiden lassen, sondern auch produziert werden.
Das Videodrama: Ein religionspädagogisches Filmprojekt im interdisziplinären Dialog (pop.religion: lebensstil – kultur – theologie)
by Julian SengelmannDas religionspädagogische und praktisch-theologische Projekt VIDEODRAMA ist ein hybrider, interdisziplinärer Ansatz, bei dem mit einer Gruppe in einem kreativen Prozess ein Film produziert wird, der im „Raum eines biblischen Textes“ entsteht. Die Teilnehmenden entwickeln diese Filmerzählung in allen Facetten einer klassischen Filmproduktion bis hin zur Premiere in einem Kino. Das VIDEODRAMA ist ein interdisziplinäres Projekt, das sich dezidiert von wissenschaftlichen Konkurrenzen und Konzeptionen loslöst. Dieses Buch begleitet exemplarisch einen solchen Prozess, entwickelt eine Theorie des VIDEODRAMAS und bringt diese mit ausgewählten aktuellen religionspädagogischen Diskussionen ins Gespräch.
Das Virus im Netz medialer Diskurse: Zur Rolle der Medien in der Corona-Krise (ars digitalis)
by Angela Krewani Peter ZimmermannDie Corona-Krise gewinnt durch die digital-mediale Verbreitung eine beunruhigende Präsenz, der sich niemand entziehen kann. Dabei handelt es sich nicht nur um die aktuelle journalistische Berichterstattung, sondern auch um das Netz der digitalen Kommunikationen, die das Virus in verschiedenen ästhetischen Formen repräsentieren und reflektieren. Eine wichtige Rolle spielen dabei die Visualisierungen der Pandemie vom allzeit präsenten Ikon des Virus bis zu den Datengrafiken der Durchseuchung. Mit diesen Mitteln entwickelt sich ein visuelles Regime, das wiederum in die mediale Berichterstattung und die Pandemie-Kommunikation zurückwirkt.Damit reagiert das Buch auf die aktuelle Pandemie und bietet eine breitgefächerte Aufarbeitung der unterschiedlichen medialen Repräsentationen des Virus.Das Buch vertritt einen dezidiert digital-medienwissenschaftlichen Ansatz, der die Medialität des Virus und der kontroversen Diskurse der Pandemie mit ihren komplexen und heterogenen Aspekten dokumentiert und analysiert. Damit unterscheidet es sich signifikant von stärker publizistischen oder soziologisch ausgerichteten Studien.
Daseinsvorsorge und Gemeinwesen im ländlichen Raum
by Frieder Dünkel Michael Herbst Benjamin StahlIn entlegenen, ländlichen Räumen ergeben sich komplexe Problemlagen, die eine interdisziplinäre Erforschung notwendig machen. Der vorliegende Band beleuchtet mit Hilfe unterschiedlichster Fachdisziplinen neue Ansätze für die Daseinsvorsorge und das Gemeinwesen. Beteiligt sind Sozial-/Wirtschaftsgeografie und Ökologie, Soziologie, Politikwissenschaft, Agrarwissenschaften, Gesundheitswissenschaften, Psychiatrie, Theologie, Kriminologie und Präventionswissenschaft.
Data Analysis: A Model Comparison Approach To Regression, ANOVA, and Beyond, Third Edition
by Charles M. Judd Gary H. McClelland Carey S. RyanData Analysis: A Model Comparison Approach to Regression, ANOVA, and Beyond is an integrated treatment of data analysis for the social and behavioral sciences. It covers all of the statistical models normally used in such analyses, such as multiple regression and analysis of variance, but it does so in an integrated manner that relies on the comparison of models of data estimated under the rubric of the general linear model. Data Analysis also describes how the model comparison approach and uniform framework can be applied to models that include product predictors (i.e., interactions and nonlinear effects) and to observations that are nonindependent. Indeed, the analysis of nonindependent observations is treated in some detail, including models of nonindependent data with continuously varying predictors as well as standard repeated measures analysis of variance. This approach also provides an integrated introduction to multilevel or hierarchical linear models and logistic regression. Finally, Data Analysis provides guidance for the treatment of outliers and other problematic aspects of data analysis. It is intended for advanced undergraduate and graduate level courses in data analysis and offers an integrated approach that is very accessible and easy to teach. Highlights of the third edition include: a new chapter on logistic regression; expanded treatment of mixed models for data with multiple random factors; updated examples; an enhanced website with PowerPoint presentations and other tools that demonstrate the concepts in the book; exercises for each chapter that highlight research findings from the literature; data sets, R code, and SAS output for all analyses; additional examples and problem sets; and test questions.
Data Analysis: A Model Comparison Approach, Second Edition
by Gary H. Mcclelland Carey S. Ryan Charles M. JuddThis completely rewritten classic text features many new examples, insights and topics including mediational, categorical, and multilevel models. Substantially reorganized, this edition provides a briefer, more streamlined examination of data analysis. Noted for its model-comparison approach and unified framework based on the general linear model, the book provides readers with a greater understanding of a variety of statistical procedures. This consistent framework, including consistent vocabulary and notation, is used throughout to develop fewer but more powerful model building techniques. The authors show how all analysis of variance and multiple regression can be accomplished within this framework. The model-comparison approach provides several benefits: It strengthens the intuitive understanding of the material thereby increasing the ability to successfully analyze data in the future It provides more control in the analysis of data so that readers can apply the techniques to a broader spectrum of questions It reduces the number of statistical techniques that must be memorized It teaches readers how to become data analysts instead of statisticians. The book opens with an overview of data analysis. All the necessary concepts for statistical inference used throughout the book are introduced in Chapters 2 through 4. The remainder of the book builds on these models. Chapters 5 - 7 focus on regression analysis, followed by analysis of variance (ANOVA), mediational analyses, non-independent or correlated errors, including multilevel modeling, and outliers and error violations. The book is appreciated by all for its detailed treatment of ANOVA, multiple regression, nonindependent observations, interactive and nonlinear models of data, and its guidance for treating outliers and other problematic aspects of data analysis. Intended for advanced undergraduate or graduate courses on data analysis, statistics, and/or quantitative methods taught in psychology, education, or other behavioral and social science departments, this book also appeals to researchers who analyze data. A protected website featuring additional examples and problems with data sets, lecture notes, PowerPoint presentations, and class-tested exam questions is available to adopters. This material uses SAS but can easily be adapted to other programs. A working knowledge of basic algebra and any multiple regression program is assumed.
Data Analysis and Classification: Methods and Applications (Studies in Classification, Data Analysis, and Knowledge Organization)
by Krzysztof Jajuga Krzysztof Najman Marek WalesiakThis volume gathers peer-reviewed contributions that address a wide range of recent developments in the methodology and applications of data analysis and classification tools in micro and macroeconomic problems. The papers were originally presented at the 29th Conference of the Section on Classification and Data Analysis of the Polish Statistical Association, SKAD 2020, held in Sopot, Poland, September 7–9, 2020. Providing a balance between methodological contributions and empirical papers, the book is divided into five parts focusing on methodology, finance, economics, social issues and applications dealing with COVID-19 data. It is aimed at a wide audience, including researchers at universities and research institutions, graduate and doctoral students, practitioners, data scientists and employees in public statistical institutions.
Data Analysis for Social Science: A Friendly and Practical Introduction
by Kosuke Imai Elena LlaudetAn ideal textbook for complete beginners—teaches from scratch R, statistics, and the fundamentals of quantitative social scienceData Analysis for Social Science provides a friendly introduction to the statistical concepts and programming skills needed to conduct and evaluate social scientific studies. Assuming no prior knowledge of statistics and coding and only minimal knowledge of math, the book teaches the fundamentals of survey research, predictive models, and causal inference while analyzing data from published studies with the statistical program R. It teaches not only how to perform the data analyses but also how to interpret the results and identify the analyses&’ strengths and limitations.Progresses by teaching how to solve one kind of problem after another, bringing in methods as needed. It teaches, in this order, how to (1) estimate causal effects with randomized experiments, (2) visualize and summarize data, (3) infer population characteristics, (4) predict outcomes, (5) estimate causal effects with observational data, and (6) generalize from sample to population.Flips the script of traditional statistics textbooks. It starts by estimating causal effects with randomized experiments and postpones any discussion of probability and statistical inference until the final chapters. This unconventional order engages students by demonstrating from the very beginning how data analysis can be used to answer interesting questions, while reserving more abstract, complex concepts for later chapters.Provides a step-by-step guide to analyzing real-world data using the powerful, open-source statistical program R, which is free for everyone to use. The datasets are provided on the book&’s website so that readers can learn how to analyze data by following along with the exercises in the book on their own computer.Assumes no prior knowledge of statistics or coding.Specifically designed to accommodate students with a variety of math backgrounds. It includes supplemental materials for students with minimal knowledge of math and clearly identifies sections with more advanced material so that readers can skip them if they so choose.Provides cheatsheets of statistical concepts and R code.Comes with instructor materials (upon request), including sample syllabi, lecture slides, and additional replication-style exercises with solutions and with the real-world datasets analyzed. Looking for a more advanced introduction? Consider Quantitative Social Science by Kosuke Imai. In addition to covering the material in Data Analysis for Social Science, it teaches diffs-in-diffs models, heterogeneous effects, text analysis, and regression discontinuity designs, among other things.
Data Analysis for Social Science: A Friendly and Practical Introduction
by Elena Llaudet Kosuke ImaiAn ideal textbook for an introductory course on quantitative methods for social scientists—assumes no prior knowledge of statistics or coding <p><p>Data Analysis for Social Science provides a friendly introduction to the statistical concepts and programming skills needed to conduct and evaluate social scientific studies. Using plain language and assuming no prior knowledge of statistics and coding, the book provides a step-by-step guide to analyzing real-world data with the statistical program R for the purpose of answering a wide range of substantive social science questions. It teaches not only how to perform the analyses but also how to interpret results and identify strengths and limitations. This one-of-a-kind textbook includes supplemental materials to accommodate students with minimal knowledge of math and clearly identifies sections with more advanced material so that readers can skip them if they so choose. <p><p>Analyzes real-world data using the powerful, open-sourced statistical program R, which is free for everyone to use <p><p>Teaches how to measure, predict, and explain quantities of interest based on data <p><p>Shows how to infer population characteristics using survey research, predict outcomes using linear models, and estimate causal effects with and without randomized experiments <p><p>Assumes no prior knowledge of statistics or coding <p><p>Specifically designed to accommodate students with a variety of math backgrounds <p><p>Provides cheatsheets of statistical concepts and R code <p><p>Supporting materials available online, including real-world datasets and the code to analyze them, plus—for instructor use—sample syllabi, sample lecture slides, additional datasets, and additional exercises with solutions <p><p>Looking for a more advanced introduction? Consider Quantitative Social Science by Kosuke Imai. In addition to covering the material in Data Analysis for Social Science, it teaches diffs-in-diffs models, heterogeneous effects, text analysis, and regression discontinuity designs, among other things.
Data Analysis for the Social Sciences: Integrating Theory and Practice
by Professor Douglas Bors'This book fosters in-depth understanding of the logic underpinning the most common statistical tests within the behavioural sciences. By emphasising the shared ground between these tests, the author provides crucial scaffolding for students as they embark upon their research journey.' —Ruth Horry, Psychology, Swansea University 'This unique text presents the conceptual underpinnings of statistics as well as the computation and application of statistics to real-life situations--a combination rarely covered in one book. A must-have for students learning statistical techniques and a go-to handbook for experienced researchers.' —Barbra Teater, Social Work, College of Staten Island, City University of New York Accessible, engaging, and informative, this book will help any social science student approach statistics with confidence. With a well-paced and well-judged integrated approach rather than a simple linear trajectory, this book progresses at a realistic speed that matches the pace at which statistics novices actually learn. Packed with global, interdisciplinary examples that ground statistical theory and concepts in real-world situations, it shows students not only how to apply newfound knowledge using IBM SPSS Statistics, but also why they would want to. Spanning statistics basics like variables, constants, and sampling through to t-tests, multiple regression and factor analysis, it builds statistical literacy while also covering key research principles like research questions, error types and results reliability. It shows you how to: Describe data with graphs, tables, and numbers Calculate probability and value distributions Test a priori and post hoc hypotheses Conduct Chi-squared tests and observational studies Structure ANOVA, ANCOVA, and factorial designs Supported by lots of visuals and a website with interactive demonstrations, author video, and practice datasets, this book is the student-focused companion to support students through their statistics journeys.
Data Analysis for the Social Sciences: Integrating Theory and Practice
by Professor Douglas Bors'This book fosters in-depth understanding of the logic underpinning the most common statistical tests within the behavioural sciences. By emphasising the shared ground between these tests, the author provides crucial scaffolding for students as they embark upon their research journey.' —Ruth Horry, Psychology, Swansea University 'This unique text presents the conceptual underpinnings of statistics as well as the computation and application of statistics to real-life situations--a combination rarely covered in one book. A must-have for students learning statistical techniques and a go-to handbook for experienced researchers.' —Barbra Teater, Social Work, College of Staten Island, City University of New York Accessible, engaging, and informative, this book will help any social science student approach statistics with confidence. With a well-paced and well-judged integrated approach rather than a simple linear trajectory, this book progresses at a realistic speed that matches the pace at which statistics novices actually learn. Packed with global, interdisciplinary examples that ground statistical theory and concepts in real-world situations, it shows students not only how to apply newfound knowledge using IBM SPSS Statistics, but also why they would want to. Spanning statistics basics like variables, constants, and sampling through to t-tests, multiple regression and factor analysis, it builds statistical literacy while also covering key research principles like research questions, error types and results reliability. It shows you how to: Describe data with graphs, tables, and numbers Calculate probability and value distributions Test a priori and post hoc hypotheses Conduct Chi-squared tests and observational studies Structure ANOVA, ANCOVA, and factorial designs Supported by lots of visuals and a website with interactive demonstrations, author video, and practice datasets, this book is the student-focused companion to support students through their statistics journeys.
Data Analysis in Business Research: A Step-By-Step Nonparametric Approach (Response Books)
by D IsraelWhile there are books focusing on parametric tests, the domain of nonparametric tests is mostly unexplored. Data Analysis in Business Research: A Step by Step Nonparametric Approach brings under one umbrella all the major nonparametric statistical tools that can be used by undergraduate and postgraduate students of all disciplines, especially students of Research Methods in Social Sciences and Management Studies, in their dissertation work. Students face difficulty in analyzing data collected from small samples; they end up reporting mere percentage analysis which results in the loss of information collected. Hence there is a need to create awareness among students and researchers about the application of major nonparametric tools that can be applied confidently without worrying about sample size, scale of measurement, normality assumptions or other parameters of that nature. The lucid presentation of the step-by-step procedures, explaining in simple English how to perform each of the major nonparametric tests, is a major attraction of the book. The book, which also has a comprehensive question bank, assumes minimal or little knowledge of statistics on the part of the reader. This book will also be informative for Marketing Research professionals and organisations, consultancies and organisations of economic research.
Data Analysis in Management with SPSS Software
by J. P. VermaThis book provides readers with a greater understanding of a variety of statistical techniques along with the procedure to use the most popular statistical software package SPSS. It strengthens the intuitive understanding of the material, thereby increasing the ability to successfully analyze data in the future. The book provides more control in the analysis of data so that readers can apply the techniques to a broader spectrum of research problems. This book focuses on providing readers with the knowledge and skills needed to carry out research in management, humanities, social and behavioural sciences by using SPSS.
Data Analysis Using SAS Enterprise Guide
by Lawrence S. Meyers Glenn Gamst A. J. Guarino Lawrence S. Meyers Glenn GamstThis book presents the basic procedures for utilizing SAS Enterprise Guide to analyze statistical data. SAS Enterprise Guide is a graphical user interface (point and click) to the main SAS application. Each chapter contains a brief conceptual overview and then guides the reader through concrete step-by-step examples to complete the analyses. The eleven sections of the book cover a wide range of statistical procedures including descriptive statistics, correlation and simple regression, t tests, one-way chi square, data transformations, multiple regression, analysis of variance, analysis of covariance, multivariate analysis of variance, factor analysis, and canonical correlation analysis. Designed to be used either as a stand-alone resource or as an accompaniment to a statistics course, the book offers a smooth path to statistical analysis with SAS Enterprise Guide for advanced undergraduate and beginning graduate students, as well as professionals in psychology, education, business, health, social work, sociology, and many other fields.
Data Analysis Using SAS Enterprise Guide
by Lawrence S. Meyers Glenn Gamst A. J. GuarinoThis book presents the basic procedures for utilizing SAS Enterprise Guide to analyze statistical data. SAS Enterprise Guide is a graphical user interface (point and click) to the main SAS application. Each chapter contains a brief conceptual overview and then guides the reader through concrete step-by-step examples to complete the analyses. The eleven sections of the book cover a wide range of statistical procedures including descriptive statistics, correlation and simple regression, t tests, one-way chi square, data transformations, multiple regression, analysis of variance, analysis of covariance, multivariate analysis of variance, factor analysis, and canonical correlation analysis. Designed to be used either as a stand-alone resource or as an accompaniment to a statistics course, the book offers a smooth path to statistical analysis with SAS Enterprise Guide for advanced undergraduate and beginning graduate students, as well as professionals in psychology, education, business, health, social work, sociology, and many other fields.
Data Analysis with Machine Learning for Psychologists: Crash Course to Learn Python 3 and Machine Learning in 10 hours
by Chandril GhoshThe power of data drives the digital economy of the 21st century. It has been argued that data is as vital a resource as oil was during the industrial revolution. An upward trend in the number of research publications using machine learning in some of the top journals in combination with an increasing number of academic recruiters within psychology asking for Python knowledge from applicants indicates a growing demand for these skills in the market. While there are plenty of books covering data science, rarely, if ever, books in the market address the need of social science students with no computer science background. They are typically written by engineers or computer scientists for people of their discipline. As a result, often such books are filled with technical jargon and examples irrelevant to psychological studies or projects. In contrast, this book was written by a psychologist in a simple, easy-to-understand way that is brief and accessible. The aim for this book was to make the learning experience on this topic as smooth as possible for psychology students/researchers with no background in programming or data science. Completing this book will also open up an enormous amount of possibilities for quantitative researchers in psychological science, as it will enable them to explore newer types of research questions.
Data Analytics and Digital Transformation (Business and Digital Transformation)
by Erik Beulen Marla A. DansUnderstanding the significance of data analytics is paramount for digital transformation but in many organizations they are separate units without fully aligned goals. As organizations are applying digital transformations to be adaptive and agile in a competitive environment, data analytics can play a critical role in their success. This book explores the crossroads between them and how to leverage their connection for improved business outcomes. The need to collaborate and share data is becoming an integral part of digital transformation. This not only creates new opportunities but also requires well-considered and continuously assessed decision-making as competitiveness is at stake. This book details approaches, concepts, and frameworks, as well as actionable insights and good practices, including combined data management and agile concepts. Critical issues are discussed such as data quality and data governance, as well as compliance, privacy, and ethics. It also offers insights into how both private and public organizations can innovate and keep up with growing data volumes and increasing technological developments in the short, mid, and long term. This book will be of direct appeal to global researchers and students across a range of business disciplines, including technology and innovation management, organizational studies, and strategic management. It is also relevant for policy makers, regulators, and executives of private and public organizations looking to implement successful transformation policies.
Data and AI Driving Smart Cities (Studies in Big Data #128)
by Pedro Ponce Therese Peffer Juana Isabel Mendez Garduno Ursula Eicker Arturo Molina Troy McDaniel Edgard D. Musafiri Mimo Ramanunni Parakkal Menon Kathryn Kaspar Sadam HussainThis book illustrates how the advanced technology developed for smart cities requires increasing interaction with citizens to motivate and incentive them. Megacities' needs have been encouraging for the creation of smart cities in which the needs of inhabitants are collected using virtualization and digitalization systems. On the other hand, machine learning algorithms have been implemented to provide better solutions for diverse areas in smart cities, such as transportation and health. Besides, conventional electric grids have transformed into smart grids, improving energy quality. Gamification, serious games, machine learning, dynamic interfaces, and social networks are some elements integrated holistically to provide novel solutions to design and develop smart cities. Also, this book presents in a friendly way the concept of social devices that are incorporated into smart homes and buildings. This book is used to understand and design smart cities where citizens are strongly interconnected so the demand response time can be reduced.
Data and Applications Security and Privacy XXXI: 31st Annual IFIP WG 11.3 Conference, DBSec 2017, Philadelphia, PA, USA, July 19-21, 2017, Proceedings (Lecture Notes in Computer Science #10359)
by Giovanni Livraga and Sencun ZhuThis book constitutes the refereed proceedings of the 31st Annual IFIP WG 11.3 International Working Conference on Data and Applications Security and Privacy, DBSec 2017, held in Philadelphia, PA, USA, in July 2017.The 21 full papers and 9 short papers presented were carefully reviewed and selected from 59 submissions. The papers are organized in topical sections on access control, privacy, cloud security, secure storage in the cloud, secure systems, and security in networks and Web.
Data and Applications Security and Privacy XXXII: 32nd Annual IFIP WG 11.3 Conference, DBSec 2018, Bergamo, Italy, July 16–18, 2018, Proceedings (Lecture Notes in Computer Science #10980)
by Florian Kerschbaum Stefano ParaboschiThis book constitutes the refereed proceedings of the 32nd Annual IFIP WG 11.3 International Working Conference on Data and Applications Security and Privacy, DBSec 2018, held in Bergamo, Italy, in July 2018. The 16 full papers and 5 short papers presented were carefully reviewed and selected from 50 submissions. The papers present high-quality original research from academia, industry, and government on theoretical and practical aspects of information security. They are organized in topical sections on administration, access control policies, privacy-preserving access and computation, integrity and user interaction, security analysis and private evaluation, fixing vulnerabilities, and networked systems.
Data and Applications Security and Privacy XXXIII: 33rd Annual IFIP WG 11.3 Conference, DBSec 2019, Charleston, SC, USA, July 15–17, 2019, Proceedings (Lecture Notes in Computer Science #11559)
by Simon N. FoleyThis book constitutes the refereed proceedings of the 33rd Annual IFIP WG 11.3 Conference on Data and Applications Security and Privacy, DBSec 2019, held in Charleston, SC, USA, in July 2018.The 21 full papers presented were carefully reviewed and selected from 52 submissions. The papers present high-quality original research from academia, industry, and government on theoretical and practical aspects of information security. They are organized in topical sections on attacks, mobile and Web security, privacy, security protocol practices, distributed systems, source code security, and malware.