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Record Nr. |
UNINA9910961704503321 |
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Autore |
Lans Rick F. van der |
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Titolo |
Data virtualization for business intelligence architectures : revolutionizing data integration for data warehouses / / Rick F. van der Lans |
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Pubbl/distr/stampa |
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Amsterdam ; ; Boston, : Elsevier/MK, c2012 |
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ISBN |
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9786613784971 |
9781281604286 |
1281604283 |
9780123978172 |
0123978173 |
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Edizione |
[1st edition] |
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Descrizione fisica |
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1 online resource (296 p.) |
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Collana |
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The Morgan Kaufmann Series on Business Intelligence |
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Disciplina |
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Soggetti |
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Business intelligence |
Data warehousing |
Management information systems |
Virtual computer systems |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Front Cover; Data Virtualization for Business Intelligence Systems; Copyright Page; Contents; Foreword; Preface; Introduction; Who Should Read This Book?; Prerequisite Knowledge; Terms and Definitions; And Finally ...; About the Author; 1 Introduction to Data Virtualization; 1.1 Introduction; 1.2 The World of Business Intelligence Is Changing; 1.3 Introduction to Virtualization; 1.4 What Is Data Virtualization?; 1.5 Data Virtualization and Related Concepts; 1.5.1 Data Virtualization versus Encapsulation and Information Hiding; 1.5.2 Data Virtualization versus Abstraction |
1.5.3 Data Virtualization versus Data Federation1.5.4 Data Virtualization versus Data Integration; 1.5.5 Data Virtualization versus Enterprise Information Integration; 1.6 Definition of Data Virtualization; 1.7 Technical Advantages of Data Virtualization; 1.8 Different Implementations of Data Virtualization; 1.9 Overview of Data |
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Virtualization Servers; 1.10 Open versus Closed Data Virtualization Servers; 1.11 Other Forms of Data Integration; 1.12 The Modules of a Data Virtualization Server; 1.13 The History of Data Virtualization; 1.14 The Sample Database: World Class Movies |
1.15 Structure of This Book2 Business Intelligence and Data Warehousing; 2.1 Introduction; 2.2 What Is Business Intelligence?; 2.3 Management Levels and Decision Making; 2.4 Business Intelligence Systems; 2.5 The Data Stores of a Business Intelligence System; 2.5.1 The Data Warehouse; 2.5.2 The Data Marts; 2.5.3 The Data Staging Area; 2.5.4 The Operational Data Store; 2.5.5 The Personal Data Stores; 2.5.6 A Comparison of the Different Types of Data Stores; 2.6 Normalized Schemas, Star Schemas, and Snowflake Schemas; 2.6.1 Normalized Schemas; 2.6.2 Denormalized Schemas; 2.6.3 Star Schemas |
2.6.4 Snowflake Schemas2.7 Data Transformation with Extract Transform Load, Extract Load Transform, and Replication; 2.7.1 Extract Transform Load; 2.7.2 Extract Load Transform; 2.7.3 Replication; 2.8 Overview of Business Intelligence Architectures; 2.9 New Forms of Reporting and Analytics; 2.9.1 Operational Reporting and Analytics; 2.9.2 Deep and Big Data Analytics; 2.9.3 Self-Service Reporting and Analytics; 2.9.4 Unrestricted Ad-Hoc Analysis; 2.9.5 360-Degree Reporting; 2.9.6 Exploratory Analysis; 2.9.7 Text-Based Analysis; 2.10 Disadvantages of Classic Business Intelligence Systems |
2.11 Summary3 Data Virtualization Server: The Building Blocks; 3.1 Introduction; 3.2 The High-Level Architecture of a Data Virtualization Server; 3.3 Importing Source Tables and Defining Wrappers; 3.4 Defining Virtual Tables and Mappings; 3.5 Examples of Virtual Tables and Mappings; 3.6 Virtual Tables and Data Modeling; 3.7 Nesting Virtual Tables and Shared Specifications; 3.8 Importing Nonrelational Data; 3.8.1 XML and JSON Documents; 3.8.2 Web Services; 3.8.3 Spreadsheets; 3.8.4 NoSQL Databases; 3.8.5 Multidimensional Cubes and MDX; 3.8.6 Semistructured Data; 3.8.7 Unstructured Data |
3.9 Publishing Virtual Tables |
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Sommario/riassunto |
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Data virtualization can help you accomplish your goals with more flexibility and agility. Learn what it is and how and why it should be used with Data Virtualization for Business Intelligence Systems. In this book, expert author Rick van der Lans explains how data virtualization servers work, what techniques to use to optimize access to various data sources and how these products can be applied in different projects. You'll learn the difference is between this new form of data integration and older forms, such as ETL and replication, and gain a clear understanding of how data virtual |
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2. |
Record Nr. |
UNINA9910145962103321 |
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Autore |
Bauer Eric |
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Titolo |
Practical system reliability |
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Pubbl/distr/stampa |
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Piscataway, NJ, : IEEE Press |
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Hoboken, N.J., : Wiley, c2009 |
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ISBN |
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9786612113796 |
9781282113794 |
1282113798 |
9780470455401 |
0470455403 |
9780470455388 |
0470455381 |
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Descrizione fisica |
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1 online resource (303 p.) |
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Disciplina |
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Soggetti |
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Telecommunication systems - Reliability |
Telecommunication |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references (p. 265-277) and index. |
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Nota di contenuto |
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Preface -- Acknowledgments -- 1 Introduction -- 2 System Availability -- 2.1 Availability, Service and Elements -- 2.2 Classical View -- 2.3 Customers' View -- 2.4 Standards View -- 3 Conceptual Model of Reliability and Availability -- 3.1 Concept of Highly Available Systems -- 3.2 Conceptual Model of System Availability -- 3.3 Failures -- 3.4 Outage Resolution -- 3.5 Downtime Budgets -- 4 Why Availability Varies Between Customers -- 4.1 Causes of Variation in Outage Event Reporting -- 4.2 Causes of Variation in Outage Duration -- 5 Modeling Availability -- 5.1 Overview of Modeling Techniques -- 5.2 Modeling Definitions -- 5.3 Practical Modeling -- 5.4 Widget Example -- 5.5 Alignment with Industry Standards -- 6 Estimating Parameters and Availability from Field Data -- 6.1 Self-Maintaining Customers -- 6.2 Analyzing Field Outage Data -- 6.3 Analyzing Performance and Alarm Data -- 6.4 Coverage Factor and Failure Rate -- 6.5 Uncovered Failure Recovery Time -- 6.6 Covered Failure Detection and Recovery Time -- |
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7 Estimating Input Parameters from Lab Data -- 7.1 Hardware Failure Rate -- 7.2 Software Failure Rate -- 7.3 Coverage Factors -- 7.4 Timing Parameters -- 7.5 System-Level Parameters -- 8 Estimating Input Parameters in the Architecture/Design Stage -- 8.1 Hardware Parameters -- 8.2 System-Level Parameters -- 8.3 Sensitivity Analysis -- 9 Prediction Accuracy -- 9.1 How Much Field Data Is Enough? -- 9.2 How Does One Measure Sampling and Prediction Errors? -- 9.3 What Causes Prediction Errors? -- 10 Connecting the Dots -- 10.1 Set Availability Requirements -- 10.2 Incorporate Architectural and Design Techniques -- 10.3 Modeling to Verify Feasibility -- 10.4 Testing -- 10.5 Update Availability Prediction -- 10.6 Periodic Field Validation and Model Update -- 10.7 Building an Availability Roadmap -- 10.8 Reliability Report -- 11 Summary -- Appendix A System Reliability Report outline -- 1 Executive Summary -- 2 Reliability Requirements -- 3 Unplanned Downtime Model and Results. |
Annex A Reliability Definitions -- Annex B References -- Annex C Markov Model State-Transition Diagrams -- Appendix B Reliability and Availability Theory -- 1 Reliability and Availability Definitions -- 2 Probability Distributions in Reliability Evaluation -- 3 Estimation of Confidence Intervals -- Appendix C Software Reliability Growth Models -- 1 Software Characteristic Models -- 2 Nonhomogeneous Poisson Process Models -- Appendix D Acronyms and Abbreviations -- Appendix E Bibliography -- Index -- About the Authors. |
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Sommario/riassunto |
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Learn how to model, predict, and manage system reliability/availability throughout the development life cycle Written by a panel of authors with a wealth of industry experience, the methods and concepts presented here give readers a solid understanding of modeling and managing system and software availability and reliability through the development of real applications and products. The modeling and prediction techniques and tools are customer-focused and data-driven, and are also aligned with industry standards (Telcordia, TL 9000, ISO, etc.). Readers will get a clear understanding about what real-world reliability and availability mean through step-by-step discussions of: . System availability. Conceptual model of reliability and availability. Why availability varies between customers. Modeling availability. Estimating parameters and availability from field data. Estimating input parameters from laboratory data. Estimating input parameters in the architecture/design stage. Prediction accuracy. Connecting the dots This book can be used by system architects, engineers, and developers to better understand and manage the reliability/availability of their products; quality engineers to grasp how software and hardware quality relate to system availability; and engineering students as part of a short course on system availability and software reliability. |
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