LEADER 05838nam 2200757Ia 450 001 9910462476203321 005 20200520144314.0 010 $a1-281-60428-3 010 $a9786613784971 010 $a0-12-397817-3 035 $a(CKB)2670000000212646 035 $a(EBL)974385 035 $a(OCoLC)801365181 035 $a(SSID)ssj0000736984 035 $a(PQKBManifestationID)12239575 035 $a(PQKBTitleCode)TC0000736984 035 $a(PQKBWorkID)10782363 035 $a(PQKB)11151579 035 $a(MiAaPQ)EBC974385 035 $a(CaSebORM)9780123944252 035 $a(PPN)17025156X 035 $a(Au-PeEL)EBL974385 035 $a(CaPaEBR)ebr10582119 035 $a(CaONFJC)MIL378497 035 $a(OCoLC)808344064 035 $a(EXLCZ)992670000000212646 100 $a20120625d2012 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aData virtualization for business intelligence architectures$b[electronic resource] $erevolutionizing data integration for data warehouses /$fRick F. van der Lans 205 $a1st edition 210 $aAmsterdam ;$aBoston $cElsevier/MK$dc2012 215 $a1 online resource (296 p.) 225 1 $aThe Morgan Kaufmann Series on Business Intelligence 300 $aDescription based upon print version of record. 311 $a0-12-394425-2 320 $aIncludes bibliographical references and index. 327 $aFront 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 327 $a1.5.3 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 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 327 $a1.15 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 327 $a2.6.4 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 327 $a2.11 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 327 $a3.9 Publishing Virtual Tables 330 $aData virtualization can help you accomplish your goals with more flexibility and agility. Learn what it is and how and why it should be used with Data Virtualization for Business Intelligence Systems. In this book, expert author Rick van der Lans explains how data virtualization servers work, what techniques to use to optimize access to various data sources and how these products can be applied in different projects. You'll learn the difference is between this new form of data integration and older forms, such as ETL and replication, and gain a clear understanding of how data virtual 410 0$aMorgan Kaufmann Series on Business Intelligence 606 $aBusiness intelligence 606 $aData warehousing 606 $aManagement information systems 606 $aVirtual computer systems 608 $aElectronic books. 615 0$aBusiness intelligence. 615 0$aData warehousing. 615 0$aManagement information systems. 615 0$aVirtual computer systems. 676 $a005.74/5 700 $aLans$b Rick F. van der$067506 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910462476203321 996 $aData virtualization for business intelligence architectures$92292052 997 $aUNINA LEADER 03724nam 2200661Ia 450 001 9910973391703321 005 20251116220630.0 010 $a0-309-17967-X 010 $a1-281-11003-5 010 $a9786611110031 010 $a0-309-11227-3 035 $a(CKB)1000000000481367 035 $a(EBL)3378306 035 $a(SSID)ssj0000264459 035 $a(PQKBManifestationID)11192257 035 $a(PQKBTitleCode)TC0000264459 035 $a(PQKBWorkID)10291212 035 $a(PQKB)10045129 035 $a(MiAaPQ)EBC3378306 035 $a(Au-PeEL)EBL3378306 035 $a(CaPaEBR)ebr10201100 035 $a(CaONFJC)MIL111003 035 $a(OCoLC)923277686 035 $a(BIP)53855009 035 $a(BIP)14742154 035 $a(EXLCZ)991000000000481367 100 $a20071104d2007 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aUnderstanding interventions that encourage minorities to pursue research careers $esummary of a workshop /$fSteven Olson and Adam P. Fagen ; Board on Life Sciences, Division on Earth and Life Studies, National Research Council of the National Academies 205 $a1st ed. 210 $aWashington, D.C. $cNational Academies Press$d2007 215 $a1 online resource (101 p.) 300 $aDescription based upon print version of record. 311 08$a0-309-11226-5 320 $aIncludes bibliographical references. 327 $a""Front Matter""; ""Acknowledgments""; ""Preface""; ""Contents""; ""1 The Nature of the Problem""; ""2 Examples of Previous Research""; ""3 The Elements of Effective Research""; ""4 Developing a Research Agenda""; ""Appendixes""; ""Appendix A: Statement of Task""; ""Appendix B: Workshop Information""; ""Appendix C: Biographical Sketches of Planning Committee and Staff"" 330 $aMinority groups are severely underrepresented in the scientific workforce. To encourage minorities to pursue careers in research, a variety of "intervention programs" have been created at the pre-college, college, and graduate school levels. While there is a belief that these programs often achieve their goals, there is relatively little understanding of the factors that contribute to that success. The Division of Minority Opportunities in Research (MORE) at the National Institute of General Medical Sciences of the National Institutes of Health has established a grant program to support research to better understand the factors that contribute to the success of intervention programs. The MORE Division asked the National Academies to organize a workshop focusing on issues addressed by the grant program. This workshop summary presents examples of previous research on intervention programs, describes ways to formulate effective research questions and conduct research to identify the key elements that lead to successful intervention programs, and outlines ways to foster a community of researchers in this area. 606 $aMinorities$xEducation (Higher)$zUnited States 606 $aMinorities in science$zUnited States 606 $aMinorities$xVocational guidance$zUnited States 615 0$aMinorities$xEducation (Higher) 615 0$aMinorities in science 615 0$aMinorities$xVocational guidance 676 $a507.1173 701 $aOlson$b Steve$f1956-$0488724 701 $aFagen$b Adam P$01867819 712 02$aNational Research Council (U.S.).$bBoard on Life Sciences. 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910973391703321 996 $aUnderstanding interventions that encourage minorities to pursue research careers$94475540 997 $aUNINA