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Data Visualization: a successful design process
by Andy KirkA comprehensive yet quick guide to the best approaches to designing data visualizations, with real examples and illustrative diagrams. Whatever the desired outcome ensure success by following this expert design process. This book is for anyone who has responsibility for, or is interested in trying to find innovative and effective ways to visually analyze and communicate data. There is no skill, no knowledge and no role-based pre-requisites or expectations of anyone reading this book.
Data Warehouse Designs: Achieving ROI with Market Basket Analysis and Time Variance
by Fon SilversMarket Basket Analysis (MBA) provides the ability to continually monitor the affinities of a business and can help an organization achieve a key competitive advantage. Time Variant data enables data warehouses to directly associate events in the past with the participants in each individual event. In the past however, the use of these powerful tools in tandem led to performance degradation and resulted in unactionable and even damaging information. Data Warehouse Designs: Achieving ROI with Market Basket Analysis and Time Variance presents an innovative, soup-to-nuts approach that successfully combines what was previously incompatible, without degradation, and uses the relational architecture already in place. Built around two main chapters, Market Basket Solution Definition and Time Variant Solution Definition, it provides a tangible how-to design that can be used to facilitate MBA within the context of a data warehouse. Presents a solution for creating home-grown MBA data marts Includes database design solutions in the context of Oracle, DB2, SQL Server, and Teradata relational database management systems (RDBMS) Explains how to extract, transform, and load data used in MBA and Time Variant solutions The book uses standard RDBMS platforms, proven database structures, standard SQL and hardware, and software and practices already accepted and used in the data warehousing community to fill the gaps left by most conceptual discussions of MBA. It employs a form and language intended for a data warehousing audience to explain the practicality of how data is delivered, stored, and viewed. Offering a comprehensive explanation of the applications that provide, store, and use MBA data, Data Warehouse Designs provides you with the language and concepts needed to require and receive information that is relevant and actionable.
Data Warehouse Requirements Engineering: A Decision Based Approach
by Naveen Prakash Deepika PrakashAs the first to focus on the issue of Data Warehouse Requirements Engineering, this book introduces a model-driven requirements process used to identify requirements granules and incrementally develop data warehouse fragments. In addition, it presents an approach to the pair-wise integration of requirements granules for consolidating multiple data warehouse fragments. The process is systematic and does away with the fuzziness associated with existing techniques. Thus, consolidation is treated as a requirements engineering issue. The notion of a decision occupies a central position in the decision-based approach. On one hand, information relevant to a decision must be elicited from stakeholders; modeled; and transformed into multi-dimensional form. On the other, decisions themselves are to be obtained from decision applications. For the former, the authors introduce a suite of information elicitation techniques specific to data warehousing. This information is subsequently converted into multi-dimensional form. For the latter, not only are decisions obtained from decision applications for managing operational businesses, but also from applications for formulating business policies and for defining rules for enforcing policies, respectively. In this context, the book presents a broad range of models, tools and techniques. For readers from academia, the book identifies the scientific/technological problems it addresses and provides cogent arguments for the proposed solutions; for readers from industry, it presents an approach for ensuring that the product meets its requirements while ensuring low lead times in delivery.
Data Warehousing For Dummies
by Thomas C. HammergrenData warehousing is one of the hottest business topics, and there's more to understanding data warehousing technologies than you might think. Find out the basics of data warehousing and how it facilitates data mining and business intelligence with Data Warehousing For Dummies, 2nd Edition.Data is probably your company's most important asset, so your data warehouse should serve your needs. The fully updated Second Edition of Data Warehousing For Dummies helps you understand, develop, implement, and use data warehouses, and offers a sneak peek into their future. You'll learn to:Analyze top-down and bottom-up data warehouse designsUnderstand the structure and technologies of data warehouses, operational data stores, and data martsChoose your project team and apply best development practices to your data warehousing projectsImplement a data warehouse, step by step, and involve end-users in the processReview and upgrade existing data storage to make it serve your needsComprehend OLAP, column-wise databases, hardware assisted databases, and middlewareUse data mining intelligently and find what you needMake informed choices about consultants and data warehousing productsData Warehousing For Dummies, 2nd Edition also shows you how to involve users in the testing process and gain valuable feedback, what it takes to successfully manage a data warehouse project, and how to tell if your project is on track. You'll find it's the most useful source of data on the topic!
Data Warehousing Fundamentals for IT Professionals
by Paulraj PonniahCutting-edge content and guidance from a data warehousing expert--now expanded to reflect field trendsData warehousing has revolutionized the way businesses in a wide variety of industries perform analysis and make strategic decisions. Since the first edition of Data Warehousing Fundamentals, numerous enterprises have implemented data warehouse systems and reaped enormous benefits. Many more are in the process of doing so. Now, this new, revised edition covers the essential fundamentals of data warehousing and business intelligence as well as significant recent trends in the field.The author provides an enhanced, comprehensive overview of data warehousing together with in-depth explanations of critical issues in planning, design, deployment, and ongoing maintenance. IT professionals eager to get into the field will gain a clear understanding of techniques for data extraction from source systems, data cleansing, data transformations, data warehouse architecture and infrastructure, and the various methods for information delivery.This practical Second Edition highlights the areas of data warehousing and business intelligence where high-impact technological progress has been made. Discussions on developments include data marts, real-time information delivery, data visualization, requirements gathering methods, multi-tier architecture, OLAP applications, Web clickstream analysis, data warehouse appliances, and data mining techniques. The book also contains review questions and exercises for each chapter, appropriate for self-study or classroom work, industry examples of real-world situations, and several appendices with valuable information.Specifically written for professionals responsible for designing, implementing, or maintaining data warehousing systems, Data Warehousing Fundamentals presents agile, thorough, and systematic development principles for the IT professional and anyone working or researching in information management.
Data Warehousing and Analytics: Fueling the Data Engine (Data-Centric Systems and Applications)
by David Taniar Wenny RahayuThis textbook covers all central activities of data warehousing and analytics, including transformation, preparation, aggregation, integration, and analysis. It discusses the full spectrum of the journey of data from operational/transactional databases, to data warehouses and data analytics; as well as the role that data warehousing plays in the data processing lifecycle. It also explains in detail how data warehouses may be used by data engines, such as BI tools and analytics algorithms to produce reports, dashboards, patterns, and other useful information and knowledge.The book is divided into six parts, ranging from the basics of data warehouse design (Part I - Star Schema, Part II - Snowflake and Bridge Tables, Part III - Advanced Dimensions, and Part IV - Multi-Fact and Multi-Input), to more advanced data warehousing concepts (Part V - Data Warehousing and Evolution) and data analytics (Part VI - OLAP, BI, and Analytics).This textbook approaches data warehousing from the case study angle. Each chapter presents one or more case studies to thoroughly explain the concepts and has different levels of difficulty, hence learning is incremental. In addition, every chapter has also a section on further readings which give pointers and references to research papers related to the chapter. All these features make the book ideally suited for either introductory courses on data warehousing and data analytics, or even for self-studies by professionals. The book is accompanied by a web page that includes all the used datasets and codes as well as slides and solutions to exercises.
Data Warehousing for Biomedical Informatics
by Richard E. BiehlData Warehousing for Biomedical Informatics is a step-by-step how-to guide for designing and building an enterprise-wide data warehouse across a biomedical or healthcare institution, using a four-iteration lifecycle and standardized design pattern. It enables you to quickly implement a fully-scalable generic data architecture that supports your organization's clinical, operational, administrative, financial, and research data. By following the guidelines in this book, you will be able to successfully progress through the Alpha, Beta, and Gamma versions, plus fully implement your first production release in about a year.
Data Warehousing with SAP BW7 BI in SAP Netweaver 2004s
by Christian Mehrwald Sabine MorlockBI in SAP NetWeaver 2004s is the official abbreviation for the successor of the Business Information Warehouse (BW) which has been completely revised by SAP with its latest release. Core elements of this comprehensive suite for decision making applications are functions for extraction, transformation and data management. With this new release, these functions aim more heavily at company-wide data warehousing. The book focuses on these core tasks of SAP BW and gives well-founded insights into the system architecture. As practical handbook and well-structured reference book, the book is for SAP consultants and IT staff that are responsible for or planning a BW-based data warehouse implementation. Apart from system architecture, the book focuses on detailed descriptions of data management (data models and Analytical Engine) as well as the Staging Engine which have been completely revised and deal with new data transfer process technology. The design of the controlled operations has been substantially expanded and besides a comprehensive description of automization techniques by using process chains, regular maintenance and administration tasks are also discussed (model trimming, technical validation). The book emphasizes a comprehensive view on aspects to manageability and system performance which are discussed in individual chapters but also implicitly in all other ranges of topics.
Data Wise, Revised and Expanded Edition: A Step-by-Step Guide to Using Assessment Results to Improve Teaching and Learning
by Kathryn Parker BoudettData Wise, Third Edition will be available in August 2025 ***Data Wise, Revised and Expanded Edition presents a continuous, sustainable process that allows school leaders to harness classroom metrics to inform educational practice. At its core, the Data Wise method fosters effective collaboration among educators, enabling teams to study a wide range of evidence and then use what they learn to enrich school culture and climate and ensure that each student thrives. Kathryn Parker Boudett, Elizabeth A. City, and Richard J. Murnane offer clear guidance for enacting all stages of the Data Wise improvement process and for integrating data inquiry into long-term institutional practice. They begin with actions that lay the groundwork for collaboration: advancing assessment literacy among contributors, building productive professional learning communities, and identifying targets for change. They continue with advice on evaluating progress and boosting accountability. Throughout the book, the authors recommend practical tools and proven practices, such as the plus/delta protocol and the ACE Habits of Mind (focusing on action, collaboration, and evidence), that help school leaders optimize the quality of meetings, especially those in which educators analyze data. They also provide tips for how to make best use of developments in education and technology, from Common Core State Standards to online collaboration tools. The field-tested strategies of the Data Wise improvement process have been used to great success in schools around the world, showing that careful examination of test scores, classroom data, and other educational evaluations can become a catalyst for important schoolwide conversations and transformations.
Data Without Labels: Practical unsupervised machine learning
by Vaibhav VerdhanDiscover all-practical implementations of the key algorithms and models for handling unlabeled data. Full of case studies demonstrating how to apply each technique to real-world problems.In Data Without Labels you&’ll learn: • Fundamental building blocks and concepts of machine learning and unsupervised learning • Data cleaning for structured and unstructured data like text and images • Clustering algorithms like K-means, hierarchical clustering, DBSCAN, Gaussian Mixture Models, and Spectral clustering • Dimensionality reduction methods like Principal Component Analysis (PCA), SVD, Multidimensional scaling, and t-SNE • Association rule algorithms like aPriori, ECLAT, SPADE • Unsupervised time series clustering, Gaussian Mixture models, and statistical methods • Building neural networks such as GANs and autoencoders • Dimensionality reduction methods like Principal Component Analysis and multidimensional scaling • Association rule algorithms like aPriori, ECLAT, and SPADE • Working with Python tools and libraries like sci-kit learn, numpy, Pandas, matplotlib, Seaborn, Keras, TensorFlow, and Flask • How to interpret the results of unsupervised learning • Choosing the right algorithm for your problem • Deploying unsupervised learning to production • Maintenance and refresh of an ML solution Data Without Labels introduces mathematical techniques, key algorithms, and Python implementations that will help you build machine learning models for unannotated data. You&’ll discover hands-off and unsupervised machine learning approaches that can still untangle raw, real-world datasets and support sound strategic decisions for your business. Don&’t get bogged down in theory—the book bridges the gap between complex math and practical Python implementations, covering end-to-end model development all the way through to production deployment. You&’ll discover the business use cases for machine learning and unsupervised learning, and access insightful research papers to complete your knowledge. Foreword by Ravi Gopalakrishnan. About the technology Generative AI, predictive algorithms, fraud detection, and many other analysis tasks rely on cheap and plentiful unlabeled data. Machine learning on data without labels—or unsupervised learning—turns raw text, images, and numbers into insights about your customers, accurate computer vision, and high-quality datasets for training AI models. This book will show you how. About the book Data Without Labels is a comprehensive guide to unsupervised learning, offering a deep dive into its mathematical foundations, algorithms, and practical applications. It presents practical examples from retail, aviation, and banking using fully annotated Python code. You&’ll explore core techniques like clustering and dimensionality reduction along with advanced topics like autoencoders and GANs. As you go, you&’ll learn where to apply unsupervised learning in business applications and discover how to develop your own machine learning models end-to-end. What's inside • Master unsupervised learning algorithms • Real-world business applications • Curate AI training datasets • Explore autoencoders and GANs applications About the reader Intended for data science professionals. Assumes knowledge of Python and basic machine learning. About the author Vaibhav Verdhan is a seasoned data science professional with extensive experience working on data science projects in a large pharmaceutical company. Table of Contents Part 1 1 Introduction to machine learning 2 Clustering techniques 3 Dimensionality reduction Part 2 4 Association rules 5 Clustering 6 Dimensionality reduction<
Data Wrangling with JavaScript
by Ashley DavisSummaryData Wrangling with JavaScript is hands-on guide that will teach you how to create a JavaScript-based data processing pipeline, handle common and exotic data, and master practical troubleshooting strategies.Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.About the TechnologyWhy not handle your data analysis in JavaScript? Modern libraries and data handling techniques mean you can collect, clean, process, store, visualize, and present web application data while enjoying the efficiency of a single-language pipeline and data-centric web applications that stay in JavaScript end to end.About the BookData Wrangling with JavaScript promotes JavaScript to the center of the data analysis stage! With this hands-on guide, you'll create a JavaScript-based data processing pipeline, handle common and exotic data, and master practical troubleshooting strategies. You'll also build interactive visualizations and deploy your apps to production. Each valuable chapter provides a new component for your reusable data wrangling toolkit.What's insideEstablishing a data pipelineAcquisition, storage, and retrievalHandling unusual data setsCleaning and preparing raw dataInteractive visualizations with D3About the ReaderWritten for intermediate JavaScript developers. No data analysis experience required.About the AuthorAshley Davis is a software developer, entrepreneur, author, and the creator of Data-Forge and Data-Forge Notebook, software for data transformation, analysis, and visualization in JavaScript.Table of ContentsGetting started: establishing your data pipelineGetting started with Node.jsAcquisition, storage, and retrievalWorking with unusual dataExploratory codingClean and prepareDealing with huge data filesWorking with a mountain of dataPractical data analysisBrowser-based visualizationServer-side visualizationLive dataAdvanced visualization with D3Getting to production
Data Wrangling with Python: Creating actionable data from raw sources
by Tirthajyoti Sarkar Shubhadeep RoychowdhurySoftware professionals, web developers, database engineers, and business analysts who want to movetowards a career of full-fledged data scientist/analytics expert or whoever wants to use data analytics/machine learning to enrich their current personal or professional projects.Prior experience with Python is not an absolute requirement, however the knowledge of at least oneobject-oriented programming language (e.g. C/C++/Java/JavaScript), and high school level math is highlypreferred. It is a bonus if you have rudimentary idea about relational database and SQL.Even seasoned Python app/web developers can benefit from this book as it focuses on data engineering aspects
Data Wrangling with Python: Tips and Tools to Make Your Life Easier
by Katharine Jarmul Jacqueline KazilHow do you take your data analysis skills beyond Excel to the next level? By learning just enough Python to get stuff done. This hands-on guide shows non-programmers like you how to process information that’s initially too messy or difficult to access. You don't need to know a thing about the Python programming language to get started.Through various step-by-step exercises, you’ll learn how to acquire, clean, analyze, and present data efficiently. You’ll also discover how to automate your data process, schedule file- editing and clean-up tasks, process larger datasets, and create compelling stories with data you obtain.Quickly learn basic Python syntax, data types, and language conceptsWork with both machine-readable and human-consumable dataScrape websites and APIs to find a bounty of useful informationClean and format data to eliminate duplicates and errors in your datasetsLearn when to standardize data and when to test and script data cleanupExplore and analyze your datasets with new Python libraries and techniquesUse Python solutions to automate your entire data-wrangling process
Data Wrangling with R
by Bradley C. BoehmkeThis guide for practicing statisticians, data scientists, and R users and programmers will teach the essentials of preprocessing: data leveraging the R programming language to easily and quickly turn noisy data into usable pieces of information. Data wrangling, which is also commonly referred to as data munging, transformation, manipulation, janitor work, etc., can be a painstakingly laborious process. Roughly 80% of data analysis is spent on cleaning and preparing data; however, being a prerequisite to the rest of the data analysis workflow (visualization, analysis, reporting), it is essential that one become fluent and efficient in data wrangling techniques.This book will guide the user through the data wrangling process via a step-by-step tutorial approach and provide a solid foundation for working with data in R. The author's goal is to teach the user how to easily wrangle data in order to spend more time on understanding the content of the data. By the end of the book, the user will have learned: How to work with different types of data such as numerics, characters, regular expressions, factors, and datesThe difference between different data structures and how to create, add additional components to, and subset each data structureHow to acquire and parse data from locations previously inaccessibleHow to develop functions and use loop control structures to reduce code redundancyHow to use pipe operators to simplify code and make it more readableHow to reshape the layout of data and manipulate, summarize, and join data sets
Data Wrangling with R: Load, explore, transform and visualize data for modeling with tidyverse libraries
by Gustavo R SantosTake your data wrangling skills to the next level by gaining a deep understanding of tidyverse libraries and effectively prepare your data for impressive analysisPurchase of the print or Kindle book includes a free PDF eBookKey FeaturesExplore state-of-the-art libraries for data wrangling in R and learn to prepare your data for analysisFind out how to work with different data types such as strings, numbers, date, and timeBuild your first model and visualize data with ease through advanced plot types and with ggplot2Book DescriptionIn this information era, where large volumes of data are being generated every day, companies want to get a better grip on it to perform more efficiently than before. This is where skillful data analysts and data scientists come into play, wrangling and exploring data to generate valuable business insights. In order to do that, you'll need plenty of tools that enable you to extract the most useful knowledge from data.Data Wrangling with R will help you to gain a deep understanding of ways to wrangle and prepare datasets for exploration, analysis, and modeling. This data book enables you to get your data ready for more optimized analyses, develop your first data model, and perform effective data visualization.The book begins by teaching you how to load and explore datasets. Then, you'll get to grips with the modern concepts and tools of data wrangling. As data wrangling and visualization are intrinsically connected, you'll go over best practices to plot data and extract insights from it. The chapters are designed in a way to help you learn all about modeling, as you will go through the construction of a data science project from end to end, and become familiar with the built-in RStudio, including an application built with Shiny dashboards.By the end of this book, you'll have learned how to create your first data model and build an application with Shiny in R.What you will learnDiscover how to load datasets and explore data in RWork with different types of variables in datasetsCreate basic and advanced visualizationsFind out how to build your first data modelCreate graphics using ggplot2 in a step-by-step way in Microsoft Power BIGet familiarized with building an application in R with ShinyWho this book is forIf you are a professional data analyst, data scientist, or beginner who wants to learn more about data wrangling, this book is for you. Familiarity with the basic concepts of R programming or any other object-oriented programming language will help you to grasp the concepts taught in this book. Data analysts looking to improve their data manipulation and visualization skills will also benefit immensely from this book.
Data and AI Driving Smart Cities (Studies in Big Data #128)
by Ursula Eicker Arturo Molina Pedro Ponce Troy McDaniel Therese Peffer Juana Isabel Mendez Garduno 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 Analytics Strategy for Business: Unlock Data Assets and Increase Innovation with a Results-Driven Data Strategy
by Simon Asplen-TaylorFor many organizations data is a by-product, but for the smarter ones it is the heartbeat of their business. Most businesses have a wealth of data buried in their systems which, if used effectively, could increase revenue, reduce costs and risk and improve customer satisfaction and employee experience. Beginning with how to choose projects which reflect your organization's goals and how to make the business case for investing in data, this book then takes the reader through the five 'waves' of organizational data maturity. It takes the reader from getting started on the data journey with some quick wins, to how data can help your business become a leading innovator which systematically outperforms competitors.Data and Analytics Strategy for Business outlines how to build consistent, high-quality sources of data which will create business value and explores how automation, AI and machine learning can improve performance and decision making. Filled with real-world examples and case studies, this book is a stage-by-stage guide to designing and implementing a results-driven data strategy.
Data and Applications Security and Privacy XXIX: 29th Annual IFIP WG 11.3 Working Conference, DBSec 2015, Fairfax, VA, USA, July 13-15, 2015, Proceedings (Lecture Notes in Computer Science #9149)
by Pierangela SamaratiThis book constitutes the refereed proceedings of the 29th Annual IFIP WG 11. 3 International Working Conference on Data and Applications Security and Privacy, DBSec 2015, held in Fairfax, VA, USA, in July 2015. The 18 revised full papers and 6 short papers presented were carefully reviewed and selected from 45 submissions. The papers are organized in the following topical sections: data anonymization and computation; access control and authorization; user privacy; authentication and information integration; privacy and trust; access control and usage policies; network and internet security; and information flow and inference.
Data and Applications Security and Privacy XXX: 30th Annual IFIP WG 11.3 Conference, DBSec 2016, Trento, Italy, July 18-20, 2016. Proceedings (Lecture Notes in Computer Science #9766)
by Vipin Swarup Silvio RaniseThis book constitutes the refereed proceedings of the 30th Annual IFIP WG 11. 3 International Working Conference on Data and Applications Security and Privacy, DBSec 2016, held in trento, Itlay, in July 2016. The 17 full papers and 7 short papers presented were carefully reviewed and selected from 54 submissions. Their topics cover a wide range of data and application security and privacy problems including those of mobile devices, collaborative systems, databases, big data, virtual systems, cloud computing, and social networks. The program also included twoinvited talks.
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.
Data and Applications Security and Privacy XXXIV: 34th Annual IFIP WG 11.3 Conference, DBSec 2020, Regensburg, Germany, June 25–26, 2020, Proceedings (Lecture Notes in Computer Science #12122)
by Anoop Singhal Jaideep VaidyaThis book constitutes the refereed proceedings of the 34th Annual IFIP WG 11.3 Conference on Data and Applications Security and Privacy, DBSec 2020, held in Regensburg, Germany, in June 2020.* The 14 full papers and 8 short papers presented were carefully reviewed and selected from 39 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 named network and cyber-physical systems security; information flow and access control; privacy-preserving computation; visualization and analytics for security; spatial systems and crowdsourcing security; and secure outsourcing and privacy. *The conference was held virtually due to the COVID-19 pandemic.
Data and Applications Security and Privacy XXXIX: 39th IFIP WG 11.3 Annual Conference on Data and Applications Security and Privacy, DBSec 2025, Gjøvik, Norway, June 23-24, 2025, Proceedings (Lecture Notes in Computer Science #15722)
by Sokratis Katsikas Basit ShafiqThis book constitutes the refereed proceedings of the 39th IFIP WG 11.3 Annual Conference on Data and Applications Security and Privacy XXXIX, DBSec 2025, held in Gjøvik, Norway, during June 23-24, 2025. The 19 full papers and 5 short papers included in this book were carefully reviewed and selected from 59 submissions. They were organized in topical sections as follows: AI applications in security and privacy; User and data privacy; Database and storage security; Differential privacy; Attackers and attack detection; Access control & Internal Controls and Audit process; and Cryptography for security and privacy.
Data and Applications Security and Privacy XXXV: 35th Annual IFIP WG 11.3 Conference, DBSec 2021, Calgary, Canada, July 19–20, 2021, Proceedings (Lecture Notes in Computer Science #12840)
by Ken Barker Kambiz GhazinourThis book constitutes the refereed proceedings of the 35th Annual IFIP WG 11.3 Conference on Data and Applications Security and Privacy, DBSec 2021, held in Calgary, Canada, in July 2021.*The 15 full papers and 8 short papers presented were carefully reviewed and selected from 45 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 named differential privacy, cryptology, machine learning, access control and others.*The conference was held virtually due to the COVID-19 pandemic.