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In the age of data science, the rapidly increasing amount of data is a major concern in numerous applications of computing operations and data storage. Duplicated data or redundant data is a main challenge in the field of data science research. Data Deduplication Approaches: Concepts, Strategies, and Challenges shows readers the various methods that can be used to eliminate multiple copies of the same files as well as duplicated segments or chunks of data within the associated files. Due to ever-increasing data duplication, its deduplication has become an especially useful field of research for storage environments, in particular persistent data storage. Data Deduplication Approaches provides readers with an overview of the concepts and background of data deduplication approaches, then proceeds to demonstrate in technical detail the strategies and challenges of real-time implementations of handling big data, data science, data backup, and recovery. The book also includes future research directions, case studies, and real-world applications of data deduplication, focusing on reduced storage, backup, recovery, and reliability. - Includes data deduplication methods for a wide variety of applications - Includes concepts and implementation strategies that will help the reader to use the suggested methods - Provides a robust set of methods that will help readers to appropriately and judiciously use the suitable methods for their applications - Focuses on reduced storage, backup, recovery, and reliability, which are the most important aspects of implementing data deduplication approaches - Includes case studies
This book features papers focusing on the implementation of new and future technologies, which were presented at the International Conference on New Technologies, Development and Application, held at the Academy of Science and Arts of Bosnia and Herzegovina in Sarajevo on 23rd–25th June 2022. It covers a wide range of future technologies and technical disciplines, including complex systems such as industry 4.0; patents in industry 4.0; robotics; mechatronics systems; automation; manufacturing; cyber-physical and autonomous systems; sensors; networks; control, energy, renewable energy sources; automotive and biological systems; vehicular networking and connected vehicles; intelligent transport, effectiveness and logistics systems, smart grids, nonlinear systems, power, social and economic systems, education, IoT. The book New Technologies, Development and Application V is oriented towards Fourth Industrial Revolution “Industry 4.0”, in which implementation will improve many aspects of human life in all segments and lead to changes in business paradigms and production models. Further, new business methods are emerging, transforming production systems, transport, delivery and consumption, which need to be monitored and implemented by every company involved in the global market.
Until now, the only way to capture, store, and effectively retain constantly growing amounts of enterprise data was to add more disk space to the storage infrastructure, an approach that can quickly become cost-prohibitive as information volumes continue to grow and capital budgets for infrastructure do not. In this IBM® Redbooks® publication, we introduce data deduplication, which has emerged as a key technology in dramatically reducing the amount of, and therefore the cost associated with storing, large amounts of data. Deduplication is the art of intelligently reducing storage needs through the elimination of redundant data so that only one instance of a data set is actually stored. Deduplication reduces data an order of magnitude better than common data compression techniques. IBM has the broadest portfolio of deduplication solutions in the industry, giving us the freedom to solve customer issues with the most effective technology. Whether it is source or target, inline or post, hardware or software, disk or tape, IBM has a solution with the technology that best solves the problem. This IBM Redbooks publication covers the current deduplication solutions that IBM has to offer: IBM ProtecTIER® Gateway and Appliance IBM Tivoli® Storage Manager IBM System Storage® N series Deduplication
Explore the methodologies and reasons behind successful legacy application moves to a hyper-space cloud, specifically Azure Purchase of the print or Kindle book includes a free PDF eBook Key Features Discover tips and tricks to help you avoid common pitfalls and get up and running quickly Gain in-depth end-to-end knowledge of all things cloud to smoothen your learning journey Explore everything from formulating a plan to governing the cloud over the long term Book Description You've heard about the benefits of the cloud and you want to get on board, but you're not sure where to start, what services to use, or how to make sure your data is safe. Making the decision to move to the cloud can be daunting and it's easy to get overwhelmed, but if you're not careful, you can easily make mistakes that cost you time and money. Azure Cloud Adoption Framework Handbook is here to help. This guide will take you step-by-step through the process of making the switch to the Microsoft Azure cloud. You'll learn everything from foundational cloud concepts and planning workload migration through to upskilling and organization transformation. As you advance, you'll find out how to identify and align your business goals with the most suitable cloud technology options available. The chapters are designed in a way to enable you to plan for a smooth transition, while minimizing disruption to your day-to-day operations. You'll also discover how the cloud can help drive innovation in your business or enable modern software development practices such as microservices and CI/CD. Throughout the chapters, you'll see how decision makers can interact with other internal stakeholders to achieve success through the power of collaboration. By the end of this book, you'll be more informed and less overwhelmed about moving your business to the cloud. What you will learn Understand cloud adoption and digital transformation generally Get to grips with the real-world, day-to-day running of a cloud platform Discover how to plan and execute the cloud adoption journey Guide all levels of the organization through cloud adoption Innovate with the business goals in mind in a fast and agile way Become familiar with advanced topics such as cloud governance, security, and reliability Who this book is for This book provides actionable strategies for anyone looking to optimize their organization's cloud adoption journey or get back on course, from IT managers and system architects to CXOs and program managers. Whether you're an enterprise or a fledgling start-up, this handbook has everything you need to get started with your cloud journey. General IT knowledge and a basic understanding of the cloud, modern software development practices, and organizational change management concepts are all prerequisites.
The first volume of this popular handbook mirrors the modern taxonomy of computer science and software engineering as described by the Association for Computing Machinery (ACM) and the IEEE Computer Society (IEEE-CS). Written by established leading experts and influential young researchers, it examines the elements involved in designing and implementing software, new areas in which computers are being used, and ways to solve computing problems. The book also explores our current understanding of software engineering and its effect on the practice of software development and the education of software professionals.
Data quality is one of the most important problems in data management. A database system typically aims to support the creation, maintenance, and use of large amount of data, focusing on the quantity of data. However, real-life data are often dirty: inconsistent, duplicated, inaccurate, incomplete, or stale. Dirty data in a database routinely generate misleading or biased analytical results and decisions, and lead to loss of revenues, credibility and customers. With this comes the need for data quality management. In contrast to traditional data management tasks, data quality management enables the detection and correction of errors in the data, syntactic or semantic, in order to improve the quality of the data and hence, add value to business processes. While data quality has been a longstanding problem for decades, the prevalent use of the Web has increased the risks, on an unprecedented scale, of creating and propagating dirty data. This monograph gives an overview of fundamental issues underlying central aspects of data quality, namely, data consistency, data deduplication, data accuracy, data currency, and information completeness. We promote a uniform logical framework for dealing with these issues, based on data quality rules. The text is organized into seven chapters, focusing on relational data. Chapter One introduces data quality issues. A conditional dependency theory is developed in Chapter Two, for capturing data inconsistencies. It is followed by practical techniques in Chapter 2b for discovering conditional dependencies, and for detecting inconsistencies and repairing data based on conditional dependencies. Matching dependencies are introduced in Chapter Three, as matching rules for data deduplication. A theory of relative information completeness is studied in Chapter Four, revising the classical Closed World Assumption and the Open World Assumption, to characterize incomplete information in the real world. A data currency model is presented in Chapter Five, to identify the current values of entities in a database and to answer queries with the current values, in the absence of reliable timestamps. Finally, interactions between these data quality issues are explored in Chapter Six. Important theoretical results and practical algorithms are covered, but formal proofs are omitted. The bibliographical notes contain pointers to papers in which the results were presented and proven, as well as references to materials for further reading. This text is intended for a seminar course at the graduate level. It is also to serve as a useful resource for researchers and practitioners who are interested in the study of data quality. The fundamental research on data quality draws on several areas, including mathematical logic, computational complexity and database theory. It has raised as many questions as it has answered, and is a rich source of questions and vitality. Table of Contents: Data Quality: An Overview / Conditional Dependencies / Cleaning Data with Conditional Dependencies / Data Deduplication / Information Completeness / Data Currency / Interactions between Data Quality Issues
The new edition of this SAGE Handbook builds on the success of the first by providing a fully updated and expanded overview of the field of human resource management. Bringing together contributions from leading international scholars - and with brand new chapters on key emerging topics such as talent management, engagement , e-HRM and big data - the Handbook focuses on familiarising the reader with the fundamentals of applied human resource management, while contextualizing practice within wider theoretical considerations. Internationally minded chapters combine a critical overview with discussion of key debates and research, as well as comprehensively dealing with important emerging interests. The second edition of this Handbook remains an indispensable resource for advanced students and researchers in the field. PART 01: Context of Human Resource Management PART 02: Fundamentals of Human Resource Management PART 03: Contemporary Issues
When you hear IBM® Tivoli® Storage Manager, the first thing that you typically think of is data backup. Tivoli Storage Manager is the premier storage management solution for mixed platform environments. Businesses face a tidal wave of information and data that seems to increase daily. The ability to successfully and efficiently manage information and data has become imperative. The Tivoli Storage Manager family of products helps businesses successfully gain better control and efficiently manage the information tidal wave through significant enhancements in multiple facets of data protection. Tivoli Storage Manager is a highly scalable and available data protection solution. It takes data protection scalability to the next level with a relational database, which is based on IBM DB2® technology. Greater availability is delivered through enhancements such as online, automated database reorganization. This IBM Redbooks® publication describes the evolving set of data-protection challenges and how capabilities in Tivoli Storage Manager can best be used to address those challenges. This book is more than merely a description of new and changed functions in Tivoli Storage Manager; it is a guide to use for your overall data protection solution.
Prepare for Microsoft Exam 70-767–and help demonstrate your real-world mastery of skills for managing data warehouses. This exam is intended for Extract, Transform, Load (ETL) data warehouse developers who create business intelligence (BI) solutions. Their responsibilities include data cleansing as well as ETL and data warehouse implementation. The reader should have experience installing and implementing a Master Data Services (MDS) model, using MDS tools, and creating a Master Data Manager database and web application. The reader should understand how to design and implement ETL control flow elements and work with a SQL Service Integration Services package. Focus on the expertise measured by these objectives: • Design, and implement, and maintain a data warehouse • Extract, transform, and load data • Build data quality solutionsThis Microsoft Exam Ref: • Organizes its coverage by exam objectives • Features strategic, what-if scenarios to challenge you • Assumes you have working knowledge of relational database technology and incremental database extraction, as well as experience with designing ETL control flows, using and debugging SSIS packages, accessing and importing or exporting data from multiple sources, and managing a SQL data warehouse. Implementing a SQL Data Warehouse About the Exam Exam 70-767 focuses on skills and knowledge required for working with relational database technology. About Microsoft Certification Passing this exam earns you credit toward a Microsoft Certified Professional (MCP) or Microsoft Certified Solutions Associate (MCSA) certification that demonstrates your mastery of data warehouse management Passing this exam as well as Exam 70-768 (Developing SQL Data Models) earns you credit toward a Microsoft Certified Solutions Associate (MCSA) SQL 2016 Business Intelligence (BI) Development certification. See full details at: microsoft.com/learning