Quality of Protection: Security Measurements and Metrics is an edited volume based on the Quality of Protection Workshop in Milano, Italy (September 2005). This volume discusses how security research can progress towards quality of protection in security comparable to quality of service in networking and software measurements, and metrics in empirical software engineering. Information security in the business setting has matured in the last few decades. Standards such as IS017799, the Common Criteria (ISO15408), and a number of industry certifications and risk analysis methodologies have raised the bar for good security solutions from a business perspective. Designed for a professional audience composed of researchers and practitioners in industry, Quality of Protection: Security Measurements and Metrics is also suitable for advanced-level students in computer science. Information security in the business setting has matured in the last few decades. Standards, such as IS017799, the Common Criteria s, and a number of industry and academic certifications and risk analysis methodologies, have raised the bar on what is considered good security solution, from a business perspective. Yet, the evaluation of security solutions has largely a qualitative flavor. Notions such as Security Metrics, Quality of Protection (QoP) or Protection Level Agreement (PLA) have only surfaced in the literature. Quality of Protection: Security Measurements and Metrics is an edited volume based on the Quality of Protection Workshop at ESORICS 2005, the flagship European Symposium on Research in Computer Security. This book discusses how security research can progress towards a notion of quality of protection in security, comparable to the notion of quality of service in networking and software measurements and metrics, in empirical software engineering. Quality of Protection: Security Measurements and Metrics is designed for a professional audience, composed of researchers and practitioners in industry. This book is also suitable for graduate-level students in computer science and telecommunications.
Re-architect relational applications to NoSQL, integrate relational database management systems with the Hadoop ecosystem, and transform and migrate relational data to and from Hadoop components. This book covers the best-practice design approaches to re-architecting your relational applications and transforming your relational data to optimize concurrency, security, denormalization, and performance. Winner of IBM´s 2012 Gerstner Award for his implementation of big data and data warehouse initiatives and author of Practical Hadoop Security , author Bhushan Lakhe walks you through the entire transition process. First, he lays out the criteria for deciding what blend of re-architecting, migration, and integration between RDBMS and HDFS best meets your transition objectives. Then he demonstrates how to design your transition model. Lakhe proceeds to cover the selection criteria for ETL tools, the implementation steps for migration with SQOOP- and Flume-based data transfers, and transition optimization techniques for tuning partitions, scheduling aggregations, and redesigning ETL. Finally, he assesses the pros and cons of data lakes and Lambda architecture as integrative solutions and illustrates their implementation with real-world case studies. Hadoop/NoSQL solutions do not offer by default certain relational technology features such as role-based access control, locking for concurrent updates, and various tools for measuring and enhancing performance. Practical Hadoop Migration shows how to use open-source tools to emulate such relational functionalities in Hadoop ecosystem components. What You´ll Learn Decide whether you should migrate your relational applications to big data technologies or integrate them Transition your relational applications to Hadoop/NoSQL platforms in terms of logical design and physical implementation Discover RDBMS-to-HDFS integration, data transformation, and optimization techniques Consider when to use Lambda architecture and data lake solutions Select and implement Hadoop-based components and applications to speed transition, optimize integrated performance, and emulate relational functionalities Who This Book Is For Database developers, database administrators, enterprise architects, Hadoop/NoSQL developers, and IT leaders. Its secondary readership is project and program managers and advanced students of database and management information systems.
This text examines the goals of data analysis with respect to enhancing knowledge, and identifies data summarization and correlation analysis as the core issues. Data summarization, both quantitative and categorical, is treated within the encoder-decoder paradigm bringing forward a number of mathematically supported insights into the methods and relations between them. Two Chapters describe methods for categorical summarization: partitioning, divisive clustering and separate cluster finding and another explain the methods for quantitative summarization, Principal Component Analysis and PageRank. Features: · An in-depth presentation of K-means partitioning including a corresponding Pythagorean decomposition of the data scatter. · Advice regarding such issues as clustering of categorical and mixed scale data, similarity and network data, interpretation aids, anomalous clusters, the number of clusters, etc. · Thorough attention to data-driven modelling including a number of mathematically stated relations between statistical and geometrical concepts including those between goodness-of-fit criteria for decision trees and data standardization, similarity and consensus clustering, modularity clustering and uniform partitioning. New edition highlights: · Inclusion of ranking issues such as Google PageRank, linear stratification and tied rankings median, consensus clustering, semi-average clustering, one-cluster clustering · Restructured to make the logics more straightforward and sections self-contained Core Data Analysis: Summarization, Correlation and Visualization is aimed at those who are eager to participate in developing the field as well as appealing to novices and practitioners.
Today, worldwide we can find a few measurement models for assessing the quality and certification of the EHR systems. Particularly, prominent are American certification programs and European quality labeling models. None of these models are compulsory for use in the wider area, nor do they include an assessment of all essential important software functionalities. In case of Croatian healthcare system, actual certification criteria are still based solely on the necessary legislative or communication criteria. Other important functionalities remain uneven and largely left to the will of the software developers. The situation is mostly similar in other European countries. The objective of this study was to develop a novel and comprehensive assessment model for evaluation of the functionalities of the EHR software for managing family medicine practices based on current world standards, models and projects, as well as on actual user satisfaction and their requirements. Resulting novel model could be used to assess the user perceived quality of almost all forms of the ambulatory EHR software and therefore useful to all stakeholders in this area of the healthcare informatization.
This book comprehensively covers the topic of recommender systems, which provide personalized recommendations of products or services to users based on their previous searches or purchases. Recommender system methods have been adapted to diverse applications including query log mining, social networking, news recommendations, and computational advertising. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. The chapters of this book are organized into three categories: Algorithms and evaluation: These chapters discuss the fundamental algorithms in recommender systems, including collaborative filtering methods, content-based methods, knowledge-based methods, ensemble-based methods, and evaluation. Recommendations in specific domains and contexts: the context of a recommendation can be viewed as important side information that affects the recommendation goals. Different types of context such as temporal data, spatial data, social data, tagging data, and trustworthiness are explored. Advanced topics and applications: Various robustness aspects of recommender systems, such as shilling systems, attack models, and their defenses are discussed. In addition, recent topics, such as learning to rank, multi-armed bandits, group systems, multi-criteria systems, and active learning systems, are introduced together with applications. Although this book primarily serves as a textbook, it will also appeal to industrial practitioners and researchers due to its focus on applications and references. Numerous examples and exercises have been provided, and a solution manual is available for instructors.