{"title":"Data warehousing Books","description":"","products":[{"product_id":"product-analytics-9780135258521","title":"Product Analytics","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cstrong\u003eJoanne Rodrigues\u003c\/strong\u003e is an experienced data scientist with master's degrees in mathematics, political science, and demography. She has six years of experience in statistical computing and R programming, as well as experience with Python for data science applications. Her management experience at enterprise companies leverages her ability to understand human behavior by using economic and sociological theory in the context of complex mathematical models.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cul\u003e\n\u003cli\u003ePart I: Qualitative Methodology\u003c\/li\u003e\n\u003cli\u003eChapter 1: Data in Action: A Model of a Dinner Party\u003c\/li\u003e\n\u003cli\u003eChapter 2: Building a Theory of the Universe–The Social Universe\u003c\/li\u003e\n\u003cli\u003eChapter 3: The Coveted Goal Post: How to Change User Behavior\u003c\/li\u003e\n\u003cli\u003ePart II: Basic Statistical Methods\u003c\/li\u003e\n\u003cli\u003eChapter 4: Distributions in User Analytics\u003c\/li\u003e\n\u003cli\u003eChapter 5: Retained? Metric Creation and Interpretation\u003c\/li\u003e\n\u003cli\u003eChapter 6: Why Are My Users Leaving? The Ins and Outs of A\/B Testing\u003c\/li\u003e\n\u003cli\u003ePart III: Predictive Methods\u003c\/li\u003e\n\u003cli\u003eChapter 7: Modeling the User Space: k-Means and PCA\u003c\/li\u003e\n\u003cli\u003eChapter 8: Predicting User Behavior: Regression, Decision Trees, and Support Vector Machines\u003c\/li\u003e\n\u003cli\u003eChapter 9: Forecasting Population Changes in Product: Demographic Projections\u003c\/li\u003e\n\u003cli\u003ePart IV: Causal Inference Methods\u003c\/li\u003e\n\u003cli\u003eChapter 10: In Pursuit of the Experiment: Natural Experiments and the Difference-in-Difference Design\u003c\/li\u003e\n\u003cli\u003eChapter 11: In Pursuit of the Experiment Continued: Regression Discontinuity, Time Series Modelling, and Interrupted Time Series Approaches\u003c\/li\u003e\n\u003cli\u003eChapter 12: Developing Heuristics in Practice: Statistical Matching and Hill’s Causality Conditions\u003c\/li\u003e\n\u003cli\u003eChapter 13: Uplift Modeling\u003c\/li\u003e\n\u003cli\u003ePart V: Basic, Predictive, and Causal Inference Methods in R\u003c\/li\u003e\n\u003cli\u003eChapter 14: Metrics in R\u003c\/li\u003e\n\u003cli\u003eChapter 15: A\/B Testing, Predictive Modeling, and Population Projection in R\u003c\/li\u003e\n\u003cli\u003eChapter 16: Regression Discontinuity, Matching, and Uplift in R\u003c\/li\u003e\n\u003cli\u003eConclusion\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"Pearson Education (US)","offers":[{"title":"Default Title","offer_id":48732340289879,"sku":"9780135258521","price":36.09,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780135258521.jpg?v=1719996479"},{"product_id":"foundational-python-for-data-science-9780136624356","title":"Foundational Python for Data Science","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cdiv\u003e  \u003cb\u003eKennedy Behrman\u003c\/b\u003e is a veteran software and data engineer. He first used Python writing asset management systems in the Visual Effects industry. He then moved into the startup world, using Python at startups using machine learning to characterize videos and predict the social media power of athletes. \u003c\/div\u003e \u003cdiv\u003e  \u003cbr\u003e \u003c\/div\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePreface xiii  \u003cbr\u003e  \u003cb\u003eI:  Learning Python in a Notebook Environment 1\u003c\/b\u003e \u003cbr\u003e   1  Introduction to Notebooks 3  \u003cbr\u003e2  Fundamentals of Python 13  \u003cbr\u003e3  Sequences 25  \u003cbr\u003e4  Other Data Structures 37  \u003cbr\u003e5  Execution Control 55  \u003cbr\u003e6  Functions 67  \u003cbr\u003e  \u003cb\u003eII: Data Science Libraries 83\u003c\/b\u003e \u003cbr\u003e   7  NumPy 85  \u003cbr\u003e8  SciPy 103  \u003cbr\u003e9  Pandas 113  \u003cbr\u003e10  Visualization Libraries 135  \u003cbr\u003e11  Machine Learning Libraries 153  \u003cbr\u003e12  Natural Language Toolkit 159  \u003cbr\u003e  \u003cb\u003eIII: Intermediate Python 171\u003c\/b\u003e \u003cbr\u003e   13  Functional Programming 173  \u003cbr\u003e14  Object-Oriented Programming 187  \u003cbr\u003e15  Other Topics 201  \u003cbr\u003eA  Answers to End-of-Chapter Questions 215  \u003cbr\u003e    Index 221  \u003cbr\u003e","brand":"Pearson Education (US)","offers":[{"title":"Default Title","offer_id":48732341109079,"sku":"9780136624356","price":42.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780136624356.jpg?v=1719996483"},{"product_id":"the-microsoft-data-warehouse-toolkit-with-sql-server-2008-r2-and-the-microsoft-business-intelligence-toolset-9780470640388","title":"The Microsoft Data Warehouse Toolkit With SQL","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThe techniques pioneered by the Kimball Group have become the industry standard for data warehouse design, development, and management. In this new edition of the   Microsoft Data Warehouse Toolkit, the authors share best practices for using these techniques in SQL Server 2008 R2 and Office 2010.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eForeword xxvii\u003c\/p\u003e \u003cp\u003eIntroduction xxix\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 1 Requirements, Realities, and Architecture 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 Defining Business Requirements 3\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Most Important Determinant of Long-Term Success 5\u003c\/p\u003e \u003cp\u003eAdventure Works Cycles Introduction 6\u003c\/p\u003e \u003cp\u003eUncovering Business Value 6\u003c\/p\u003e \u003cp\u003eObtaining Sponsorship 7\u003c\/p\u003e \u003cp\u003eDefining Enterprise-Level Business Requirements 8\u003c\/p\u003e \u003cp\u003ePrioritizing the Business Requirements 22\u003c\/p\u003e \u003cp\u003eRevisiting the Project Planning 25\u003c\/p\u003e \u003cp\u003eGathering Project-Level Requirements 26\u003c\/p\u003e \u003cp\u003eSummary 28\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 Designing the Business Process Dimensional Model 29\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDimensional Modeling Concepts and Terminology 30\u003c\/p\u003e \u003cp\u003eFacts 31\u003c\/p\u003e \u003cp\u003eDimensions 33\u003c\/p\u003e \u003cp\u003eBringing Facts and Dimensions Together 34\u003c\/p\u003e \u003cp\u003eThe Bus Matrix, Conformed Dimensions, and Drill Across 36\u003c\/p\u003e \u003cp\u003eAdditional Design Concepts and Techniques 38\u003c\/p\u003e \u003cp\u003eSurrogate Keys 38\u003c\/p\u003e \u003cp\u003eSlowly Changing Dimensions 39\u003c\/p\u003e \u003cp\u003eDates 42\u003c\/p\u003e \u003cp\u003eDegenerate Dimensions 43\u003c\/p\u003e \u003cp\u003eSnowflaking 43\u003c\/p\u003e \u003cp\u003eMany-to-Many or Multivalued Dimensions 44\u003c\/p\u003e \u003cp\u003eHierarchies 47\u003c\/p\u003e \u003cp\u003eAggregate Dimensions 49\u003c\/p\u003e \u003cp\u003eJunk Dimensions 51\u003c\/p\u003e \u003cp\u003eThe Three Fact Table Types 52\u003c\/p\u003e \u003cp\u003eAggregates 53\u003c\/p\u003e \u003cp\u003eThe Dimensional Modeling Process 54\u003c\/p\u003e \u003cp\u003ePreparation 55\u003c\/p\u003e \u003cp\u003eData Profiling and Research 60\u003c\/p\u003e \u003cp\u003eBuilding Dimensional Models 63\u003c\/p\u003e \u003cp\u003eDeveloping the Detailed Dimensional Model 66\u003c\/p\u003e \u003cp\u003eTesting and Refining the Model 68\u003c\/p\u003e \u003cp\u003eReviewing and Validating the Model 68\u003c\/p\u003e \u003cp\u003eCase Study: The Adventure Works Cycles Orders Dimensional Model 69\u003c\/p\u003e \u003cp\u003eThe Orders Fact Table 69\u003c\/p\u003e \u003cp\u003eThe Dimensions 69\u003c\/p\u003e \u003cp\u003eIdentifying Dimension Attributes and Facts for the Orders Business Process 72\u003c\/p\u003e \u003cp\u003eThe Final Draft of the Initial Orders Model 74\u003c\/p\u003e \u003cp\u003eDetailed Orders Dimensional Model Development 75\u003c\/p\u003e \u003cp\u003eFinal Dimensional Model 77\u003c\/p\u003e \u003cp\u003eSummary 77\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 The Toolset 79\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Microsoft DW\/BI Toolset 80\u003c\/p\u003e \u003cp\u003eWhy Use the Microsoft Toolset? 82\u003c\/p\u003e \u003cp\u003eArchitecture of a Microsoft DW\/BI System 83\u003c\/p\u003e \u003cp\u003eWhy Analysis Services? 84\u003c\/p\u003e \u003cp\u003eWhy a Relational Store? 86\u003c\/p\u003e \u003cp\u003eETL Is Not Optional 86\u003c\/p\u003e \u003cp\u003eThe Role of Master Data Services 88\u003c\/p\u003e \u003cp\u003eDelivering BI Applications 88\u003c\/p\u003e \u003cp\u003eOverview of the Microsoft Tools 89\u003c\/p\u003e \u003cp\u003eWhich Products Do You Need? 90\u003c\/p\u003e \u003cp\u003eSQL Server Development and Management Tools 92\u003c\/p\u003e \u003cp\u003eSummary 97\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 System Setup 99\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSystem Sizing Considerations 100\u003c\/p\u003e \u003cp\u003eCalculating Data Volumes 101\u003c\/p\u003e \u003cp\u003eDetermining Usage Complexity 102\u003c\/p\u003e \u003cp\u003eEstimating Simultaneous Users 104\u003c\/p\u003e \u003cp\u003eAssessing System Availability Requirements 105\u003c\/p\u003e \u003cp\u003eHow Big Will It Be? 105\u003c\/p\u003e \u003cp\u003eSystem Configuration Considerations 105\u003c\/p\u003e \u003cp\u003eMemory 106\u003c\/p\u003e \u003cp\u003eMonolithic or Distributed? 106\u003c\/p\u003e \u003cp\u003eStorage System Considerations 110\u003c\/p\u003e \u003cp\u003eProcessors 113\u003c\/p\u003e \u003cp\u003eSetting Up for High Availability 114\u003c\/p\u003e \u003cp\u003eSoftware Installation and Configuration 115\u003c\/p\u003e \u003cp\u003eDevelopment Environment Software Requirements 116\u003c\/p\u003e \u003cp\u003eTest and Production Software Requirements 120\u003c\/p\u003e \u003cp\u003eOperating Systems 122\u003c\/p\u003e \u003cp\u003eSQL Server Relational Database Setup 122\u003c\/p\u003e \u003cp\u003eAnalysis Services Setup 126\u003c\/p\u003e \u003cp\u003eIntegration Services Setup 129\u003c\/p\u003e \u003cp\u003eReporting Services Setup 130\u003c\/p\u003e \u003cp\u003eSummary 131\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 2 Building and Populating the Databases 133\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 Creating the Relational Data Warehouse 135\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eGetting Started 136\u003c\/p\u003e \u003cp\u003eComplete the Physical Design 137\u003c\/p\u003e \u003cp\u003eSurrogate Keys 138\u003c\/p\u003e \u003cp\u003eString Columns 138\u003c\/p\u003e \u003cp\u003eTo Null, or Not to Null? 140\u003c\/p\u003e \u003cp\u003eHousekeeping Columns 140\u003c\/p\u003e \u003cp\u003eTable and Column Extended Properties 142\u003c\/p\u003e \u003cp\u003eDefine Storage and Create Constraints and Supporting Objects 142\u003c\/p\u003e \u003cp\u003eCreate Files and Filegroups 142\u003c\/p\u003e \u003cp\u003eData Compression 144\u003c\/p\u003e \u003cp\u003eEntity and Referential Integrity Constraints 145\u003c\/p\u003e \u003cp\u003eInitial Indexing and Database Statistics 147\u003c\/p\u003e \u003cp\u003eAggregate Tables 150\u003c\/p\u003e \u003cp\u003eCreate Table Views 151\u003c\/p\u003e \u003cp\u003eInsert an Unknown Member Row 152\u003c\/p\u003e \u003cp\u003eExample CREATE TABLE Statement 152\u003c\/p\u003e \u003cp\u003ePartitioned Tables 153\u003c\/p\u003e \u003cp\u003eFinishing Up 163\u003c\/p\u003e \u003cp\u003eStaging Tables 163\u003c\/p\u003e \u003cp\u003eMetadata Setup 163\u003c\/p\u003e \u003cp\u003eSummary 164\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 Master Data Management 165\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eManaging Master Reference Data 166\u003c\/p\u003e \u003cp\u003eIncomplete Attributes 167\u003c\/p\u003e \u003cp\u003eData Integration 168\u003c\/p\u003e \u003cp\u003eSystems Integration 170\u003c\/p\u003e \u003cp\u003eMaster Data Management Systems and the Data Warehouse 171\u003c\/p\u003e \u003cp\u003eIntroducing SQL Server Master Data Services 171\u003c\/p\u003e \u003cp\u003eModel Definition Features 172\u003c\/p\u003e \u003cp\u003eData Management Features 174\u003c\/p\u003e \u003cp\u003eUser Interface: Exploring and Managing the Master Data 174\u003c\/p\u003e \u003cp\u003eImporting and Updating Data 176\u003c\/p\u003e \u003cp\u003eExporting Data 177\u003c\/p\u003e \u003cp\u003eFull Versioning of All Attributes 179\u003c\/p\u003e \u003cp\u003eCreating a Simple Application 179\u003c\/p\u003e \u003cp\u003eSummary 186\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 Designing and Developing the ETL System 187\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eRound Up the Requirements 188\u003c\/p\u003e \u003cp\u003eDevelop the ETL Plan 191\u003c\/p\u003e \u003cp\u003eIntroducing SQL Server Integration Services 192\u003c\/p\u003e \u003cp\u003eControl Flow and Data Flow 194\u003c\/p\u003e \u003cp\u003eSSIS Package Architecture 197\u003c\/p\u003e \u003cp\u003eThe Major Subsystems of ETL 198\u003c\/p\u003e \u003cp\u003eExtracting Data 199\u003c\/p\u003e \u003cp\u003eSubsystem 1: Data Profiling 199\u003c\/p\u003e \u003cp\u003eSubsystem 2: Change Data Capture System 200\u003c\/p\u003e \u003cp\u003eSubsystem 3: Extract System 202\u003c\/p\u003e \u003cp\u003eCleaning and Conforming Data 206\u003c\/p\u003e \u003cp\u003eSubsystem 4: Data Cleaning System 206\u003c\/p\u003e \u003cp\u003eSubsystem 5: Error Event Schema 214\u003c\/p\u003e \u003cp\u003eSubsystem 6: Audit Dimension Assembler 215\u003c\/p\u003e \u003cp\u003eSubsystem 7: Deduplication System 216\u003c\/p\u003e \u003cp\u003eSubsystem 8: Conforming System 217\u003c\/p\u003e \u003cp\u003eDelivering Data for Presentation 218\u003c\/p\u003e \u003cp\u003eSubsystem 9: Slowly Changing Dimension Manager 218\u003c\/p\u003e \u003cp\u003eSubsystem 10: Surrogate Key Generator 223\u003c\/p\u003e \u003cp\u003eSubsystem 11: Hierarchy Manager 223\u003c\/p\u003e \u003cp\u003eSubsystem 12: Special Dimensions Manager 224\u003c\/p\u003e \u003cp\u003eSubsystem 13: Fact Table Builders 225\u003c\/p\u003e \u003cp\u003eSubsystem 14: Surrogate Key Pipeline 229\u003c\/p\u003e \u003cp\u003eSubsystem 15: Multi-Valued Dimension Bridge Table Builder 235\u003c\/p\u003e \u003cp\u003eSubsystem 16: Late Arriving Data Handler 235\u003c\/p\u003e \u003cp\u003eSubsystem 17: Dimension Manager 238\u003c\/p\u003e \u003cp\u003eSubsystem 18: Fact Provider System 238\u003c\/p\u003e \u003cp\u003eSubsystem 19: Aggregate Builder 239\u003c\/p\u003e \u003cp\u003eSubsystem 20: OLAP Cube Builder 239\u003c\/p\u003e \u003cp\u003eSubsystem 21: Data Propagation Manager 240\u003c\/p\u003e \u003cp\u003eManaging the ETL Environment 240\u003c\/p\u003e \u003cp\u003eSummary 243\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 The Core Analysis Services OLAP Database 245\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eOverview of Analysis Services OLAP 247\u003c\/p\u003e \u003cp\u003eWhy Use Analysis Services? 247\u003c\/p\u003e \u003cp\u003eWhy Not Analysis Services? 249\u003c\/p\u003e \u003cp\u003eDesigning the OLAP Structure 250\u003c\/p\u003e \u003cp\u003ePlanning 251\u003c\/p\u003e \u003cp\u003eGetting Started 253\u003c\/p\u003e \u003cp\u003eCreate a Project and a Data Source View 255\u003c\/p\u003e \u003cp\u003eDimension Designs 257\u003c\/p\u003e \u003cp\u003eCreating and Editing Dimensions 261\u003c\/p\u003e \u003cp\u003eCreating and Editing the Cube 274\u003c\/p\u003e \u003cp\u003ePhysical Design Considerations 291\u003c\/p\u003e \u003cp\u003eUnderstanding Storage Modes 293\u003c\/p\u003e \u003cp\u003eDeveloping the Partitioning Plan 294\u003c\/p\u003e \u003cp\u003eDesigning Performance Aggregations 296\u003c\/p\u003e \u003cp\u003ePlanning for Deployment 298\u003c\/p\u003e \u003cp\u003eProcessing the Full Cube 299\u003c\/p\u003e \u003cp\u003eDeveloping the Incremental Processing Plan 299\u003c\/p\u003e \u003cp\u003eSummary 304\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 Design Requirements for Real-Time BI 305\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eReal-Time Triage 306\u003c\/p\u003e \u003cp\u003eWhat Does Real-Time Mean? 306\u003c\/p\u003e \u003cp\u003eWho Needs Real Time? 307\u003c\/p\u003e \u003cp\u003eReal-Time Tradeoffs 308\u003c\/p\u003e \u003cp\u003eScenarios and Solutions 311\u003c\/p\u003e \u003cp\u003eExecuting Reports in Real Time 313\u003c\/p\u003e \u003cp\u003eServing Reports from a Cache 313\u003c\/p\u003e \u003cp\u003eCreating an ODS with Mirrors and Snapshots 314\u003c\/p\u003e \u003cp\u003eCreating an ODS with Replication 314\u003c\/p\u003e \u003cp\u003eBuilding a BizTalk Application 315\u003c\/p\u003e \u003cp\u003eBuilding a Real-Time Relational Partition 315\u003c\/p\u003e \u003cp\u003eQuerying Real-Time Data in the Relational Database 317\u003c\/p\u003e \u003cp\u003eUsing Analysis Services to Query Real-Time Data 318\u003c\/p\u003e \u003cp\u003eSummary 319\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 3 Developing the BI Applications 321\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10 Building BI Applications in Reporting Services 323\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA Brief Overview of BI Applications 324\u003c\/p\u003e \u003cp\u003eTypes of BI Applications 325\u003c\/p\u003e \u003cp\u003eThe Value of Business Intelligence Applications 326\u003c\/p\u003e \u003cp\u003eA High-Level Architecture for Reporting 328\u003c\/p\u003e \u003cp\u003eReviewing Business Requirements for Reporting 328\u003c\/p\u003e \u003cp\u003eExamining the Reporting Services Architecture 330\u003c\/p\u003e \u003cp\u003eUsing Reporting Services as a Standard Reporting Tool 332\u003c\/p\u003e \u003cp\u003eReporting Services Assessment 339\u003c\/p\u003e \u003cp\u003eThe Reporting System Design and Development Process 340\u003c\/p\u003e \u003cp\u003eReporting System Design 341\u003c\/p\u003e \u003cp\u003eReporting System Development 348\u003c\/p\u003e \u003cp\u003eBuilding and Delivering Reports 351\u003c\/p\u003e \u003cp\u003ePlanning and Preparation 351\u003c\/p\u003e \u003cp\u003eCreating Reports 354\u003c\/p\u003e \u003cp\u003eReporting Operations 368\u003c\/p\u003e \u003cp\u003eAd Hoc Reporting Options 369\u003c\/p\u003e \u003cp\u003eThe Report Model 370\u003c\/p\u003e \u003cp\u003eShared Datasets 371\u003c\/p\u003e \u003cp\u003eReport Parts 371\u003c\/p\u003e \u003cp\u003eSummary 372\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11 PowerPivot and Excel 375\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUsing Excel for Analysis and Reporting 376\u003c\/p\u003e \u003cp\u003eThe PowerPivot Architecture: Excel on Steroids 378\u003c\/p\u003e \u003cp\u003eCreating and Using PowerPivot Databases 380\u003c\/p\u003e \u003cp\u003eGetting Started 381\u003c\/p\u003e \u003cp\u003ePowerPivot Table Design 381\u003c\/p\u003e \u003cp\u003eCreating Analytics with PowerPivot 385\u003c\/p\u003e \u003cp\u003eObservations and Guidelines on PowerPivot for Excel 392\u003c\/p\u003e \u003cp\u003ePowerPivot for SharePoint 394\u003c\/p\u003e \u003cp\u003eThe PowerPivot SharePoint User Experience 394\u003c\/p\u003e \u003cp\u003eServer-Level Resources 397\u003c\/p\u003e \u003cp\u003ePowerPivot Monitoring and Management 397\u003c\/p\u003e \u003cp\u003ePowerPivot’s Role in a Managed DW\/BI Environment 400\u003c\/p\u003e \u003cp\u003eSummary 401\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 12 The BI Portal and SharePoint 403\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe BI Portal 404\u003c\/p\u003e \u003cp\u003ePlanning the BI Portal 405\u003c\/p\u003e \u003cp\u003eImpact on Design 406\u003c\/p\u003e \u003cp\u003eBusiness Process Categories 407\u003c\/p\u003e \u003cp\u003eAdditional Functions 408\u003c\/p\u003e \u003cp\u003eBuilding the BI Portal 409\u003c\/p\u003e \u003cp\u003eUsing SharePoint as the BI Portal 411\u003c\/p\u003e \u003cp\u003eArchitecture and Concepts 412\u003c\/p\u003e \u003cp\u003eSetting Up SharePoint 417\u003c\/p\u003e \u003cp\u003eSummary 426\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 13 Incorporating Data Mining 429\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDefining Data Mining 430\u003c\/p\u003e \u003cp\u003eBasic Data Mining Terminology 432\u003c\/p\u003e \u003cp\u003eBusiness Uses of Data Mining 433\u003c\/p\u003e \u003cp\u003eRoles and Responsibilities 440\u003c\/p\u003e \u003cp\u003eSQL Server Data Mining Architecture Overview 440\u003c\/p\u003e \u003cp\u003eThe Data Mining Design Environment 442\u003c\/p\u003e \u003cp\u003eBuild, Deploy, and Process 442\u003c\/p\u003e \u003cp\u003eAccessing the Mining Models 443\u003c\/p\u003e \u003cp\u003eIntegration Services and Data Mining 443\u003c\/p\u003e \u003cp\u003eAdditional Features 444\u003c\/p\u003e \u003cp\u003eArchitecture Summary 445\u003c\/p\u003e \u003cp\u003eMicrosoft Data Mining Algorithms 445\u003c\/p\u003e \u003cp\u003eDecision Trees 446\u003c\/p\u003e \u003cp\u003eNaïve Bayes 447\u003c\/p\u003e \u003cp\u003eClustering 448\u003c\/p\u003e \u003cp\u003eSequence Clustering 448\u003c\/p\u003e \u003cp\u003eTime Series 449\u003c\/p\u003e \u003cp\u003eAssociation 449\u003c\/p\u003e \u003cp\u003eNeural Network 449\u003c\/p\u003e \u003cp\u003eThe Data Mining Process 450\u003c\/p\u003e \u003cp\u003eThe Business Phase 451\u003c\/p\u003e \u003cp\u003eThe Data Mining Phase 453\u003c\/p\u003e \u003cp\u003eThe Operations Phase 460\u003c\/p\u003e \u003cp\u003eMetadata 462\u003c\/p\u003e \u003cp\u003eData Mining Examples 463\u003c\/p\u003e \u003cp\u003eCase Study: Categorizing Cities 463\u003c\/p\u003e \u003cp\u003eCase Study: Product Recommendations 472\u003c\/p\u003e \u003cp\u003eSummary 488\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 4 Deploying and Managing the DW\/BI System 491\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 14 Designing and Implementing Security 493\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIdentifying the Security Manager 494\u003c\/p\u003e \u003cp\u003eSecuring the Hardware and Operating System 495\u003c\/p\u003e \u003cp\u003eSecuring the Operating System 495\u003c\/p\u003e \u003cp\u003eUsing Windows Integrated Security 496\u003c\/p\u003e \u003cp\u003eSecuring the Development Environment 497\u003c\/p\u003e \u003cp\u003eSecuring the Data 498\u003c\/p\u003e \u003cp\u003eProviding Open Access for Internal Users 498\u003c\/p\u003e \u003cp\u003eItemizing Sensitive Data 500\u003c\/p\u003e \u003cp\u003eSecuring Various Types of Data Access 500\u003c\/p\u003e \u003cp\u003eSecuring the Components of the DW\/BI System 502\u003c\/p\u003e \u003cp\u003eReporting Services Security 502\u003c\/p\u003e \u003cp\u003eAnalysis Services Security 505\u003c\/p\u003e \u003cp\u003eRelational DW Security 514\u003c\/p\u003e \u003cp\u003eIntegration Services Security 520\u003c\/p\u003e \u003cp\u003eUsage Monitoring 521\u003c\/p\u003e \u003cp\u003eSummary 521\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 15 Metadata Plan 523\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eMetadata Basics 524\u003c\/p\u003e \u003cp\u003eThe Purpose of Metadata 524\u003c\/p\u003e \u003cp\u003eMetadata Categories 525\u003c\/p\u003e \u003cp\u003eThe Metadata Repository 526\u003c\/p\u003e \u003cp\u003eMetadata Standards 526\u003c\/p\u003e \u003cp\u003eSQL Server 2008 R2 Metadata 527\u003c\/p\u003e \u003cp\u003eCross-Tool Components 528\u003c\/p\u003e \u003cp\u003eRelational Engine Metadata 532\u003c\/p\u003e \u003cp\u003eAnalysis Services 532\u003c\/p\u003e \u003cp\u003eIntegration Services 533\u003c\/p\u003e \u003cp\u003eReporting Services 533\u003c\/p\u003e \u003cp\u003eMaster Data Services 534\u003c\/p\u003e \u003cp\u003eSharePoint 534\u003c\/p\u003e \u003cp\u003eExternal Metadata Sources 534\u003c\/p\u003e \u003cp\u003eLooking to the Future 535\u003c\/p\u003e \u003cp\u003eA Practical Metadata Approach 535\u003c\/p\u003e \u003cp\u003eCreating the Metadata Strategy 536\u003c\/p\u003e \u003cp\u003eBusiness Metadata Reporting 538\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003eProcess Metadata Reporting 541\u003c\/p\u003e \u003cp\u003eTechnical Metadata Reporting 542\u003c\/p\u003e \u003cp\u003eOngoing Metadata Management 543\u003c\/p\u003e \u003cp\u003eSummary 543\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 16 Deployment 545\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSetting Up the Environments 546\u003c\/p\u003e \u003cp\u003eTesting 550\u003c\/p\u003e \u003cp\u003eDevelopment Testing 551\u003c\/p\u003e \u003cp\u003eSystem Testing 555\u003c\/p\u003e \u003cp\u003eData Quality Assurance Testing 557\u003c\/p\u003e \u003cp\u003ePerformance Testing 559\u003c\/p\u003e \u003cp\u003eUsability Testing 562\u003c\/p\u003e \u003cp\u003eTesting Summary 563\u003c\/p\u003e \u003cp\u003eDeploying to Production 564\u003c\/p\u003e \u003cp\u003eRelational Database Deployment 565\u003c\/p\u003e \u003cp\u003eIntegration Services Package Deployment 567\u003c\/p\u003e \u003cp\u003eAnalysis Services Database Deployment 568\u003c\/p\u003e \u003cp\u003eReporting Services Report Deployment 571\u003c\/p\u003e \u003cp\u003eMaster Data Services Deployment 572\u003c\/p\u003e \u003cp\u003eData Warehouse and BI Documentation 573\u003c\/p\u003e \u003cp\u003eCore Descriptions 573\u003c\/p\u003e \u003cp\u003eAdditional Documentation 575\u003c\/p\u003e \u003cp\u003eUser Training 576\u003c\/p\u003e \u003cp\u003eUser Support 579\u003c\/p\u003e \u003cp\u003eDesktop Readiness and Configuration 580\u003c\/p\u003e \u003cp\u003eSummary 581\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 17 Operations and Maintenance 583\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eProviding User Support 584\u003c\/p\u003e \u003cp\u003eMaintaining the BI Portal 585\u003c\/p\u003e \u003cp\u003eExtending the BI Applications 586\u003c\/p\u003e \u003cp\u003eSystem Management 587\u003c\/p\u003e \u003cp\u003eGoverning the DW\/BI System 588\u003c\/p\u003e \u003cp\u003ePerformance Monitoring 593\u003c\/p\u003e \u003cp\u003eUsage Monitoring 600\u003c\/p\u003e \u003cp\u003eManaging Disk Space 602\u003c\/p\u003e \u003cp\u003eService and Availability Management 603\u003c\/p\u003e \u003cp\u003ePerformance Tuning the DW\/BI System 604\u003c\/p\u003e \u003cp\u003eBackup and Recovery 606\u003c\/p\u003e \u003cp\u003eExecuting the ETL Packages 611\u003c\/p\u003e \u003cp\u003eSummary 611\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 18 Present Imperatives and Future Outlook 613\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eGrowing the DW\/BI System 613\u003c\/p\u003e \u003cp\u003eLifecycle Review with Common Problems 615\u003c\/p\u003e \u003cp\u003ePhase I — ​Requirements, Realities, Plans, and Designs 616\u003c\/p\u003e \u003cp\u003ePhase II — ​Developing the Databases 616\u003c\/p\u003e \u003cp\u003ePhase III — ​Developing the BI Applications and Portal Environment 617\u003c\/p\u003e \u003cp\u003ePhase IV — ​Deploying and Managing the DW\/BI System 618\u003c\/p\u003e \u003cp\u003eIteration and Growth 618\u003c\/p\u003e \u003cp\u003eWhat We Like in the Microsoft BI Toolset 619\u003c\/p\u003e \u003cp\u003eFuture Directions: Room for Improvement 620\u003c\/p\u003e \u003cp\u003eConclusion 623\u003c\/p\u003e \u003cp\u003eIndex 625\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":48733787455831,"sku":"9780470640388","price":999.99,"currency_code":"GBP","in_stock":false}]},{"product_id":"the-cloud-data-lake-9781098116583","title":"The Cloud Data Lake","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eAuthor Rukmani Gopalan, a product management leader and data enthusiast, guides data architects and engineers through the major aspects of working with a cloud data lake, from design considerations and best practices to data format optimizations, performance optimization, cost management, and governance.","brand":"O'Reilly Media","offers":[{"title":"Default Title","offer_id":48738226635095,"sku":"9781098116583","price":39.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781098116583.jpg?v=1723811836"},{"product_id":"oracle-database-upgrade-and-migration-methods-9781484223277","title":"Oracle Database Upgrade and Migration Methods","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cdiv\u003e\n\u003cp\u003eLearn all of the available upgrade and migration methods in detail to move to Oracle Database version 12c. You will become familiar with database upgrade best practices to complete the upgrade in an effective manner and understand the Oracle Database 12c patching process.\u003c\/p\u003e\n\u003cp\u003eSo it''s time to upgrade Oracle Database to version 12c and you need to choose the appropriate method while considering issues such as downtime. This book explains all of the available upgrade and migration methods so you can choose the one that suits your environment. You will be aware of the practical issues and proactive measures to take to upgrade successfully and reduce unexpected issues. \u003cbr\u003e\u003c\/p\u003e\n\u003cp\u003eWith every release of Oracle Database there are new features and fixes to bugs identified in previous versions. As each release becomes obsolete, existing databases need to be upgraded. \u003cstrong\u003e\u003cem\u003eOracle Database Upgrade and Migration Methods\u003c\/em\u003e\u003c\/strong\u003e explains each method along \u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\n\u003cp\u003ePART I: packetC Background 1\u003c\/p\u003e  \u003cp\u003eCHAPTER 1: Getting Started 3\u003c\/p\u003e  \u003cp\u003eIntroduction to Database upgrade          \u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003eNecessities of Database upgrade           \u003c\/p\u003e  \u003cp\u003eBenefits of Database upgrade              \u003c\/p\u003e  Hurdles that affect Database upgrade \u003cp\u003e\u003c\/p\u003e  \u003cp\u003edecision                                  \u003c\/p\u003e  \u003cp\u003eTypes of Database upgrade                 \u003c\/p\u003e  \u003cp\u003eThings to consider before upgrade         \u003c\/p\u003e  \u003cp\u003eEngineers involved in upgrade activity    \u003c\/p\u003e  \u003cp\u003eUpgrade compatibility matrix              \u003c\/p\u003e  \u003cp\u003e                Best practices of Database upgrade\u003c\/p\u003e                  Database Migration\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e                Situations demand Migration\u003c\/p\u003e  \u003cp\u003e                Things to consider before migration\u003c\/p\u003e  \u003cp\u003e                                summary\u003c\/p\u003e                                                                                                                                          \u003cp\u003e\u003c\/p\u003e  \u003cp\u003ePART II: Language Reference 53\u003c\/p\u003e  \u003cp\u003en CHAPTER 2: Database Upgrade methods 55\u003c\/p\u003e  \u003cp\u003eDBUA                                      \u003c\/p\u003e  \u003cp\u003eManual\/Command line upgrade               \u003c\/p\u003e  \u003cp\u003eExport\/Import                             \u003c\/p\u003e  \u003cp\u003eDatapump                                  \u003c\/p\u003e  Transportable Tablespace                  \u003cp\u003e\u003c\/p\u003e  \u003cp\u003eGolden Gate                               \u003c\/p\u003e  \u003cp\u003eCreate Table as Select (CTAS)             \u003c\/p\u003e  \u003cp\u003eTransient Logical Standby                 \u003c\/p\u003e  \u003cp\u003e          Full Transportable Tablespace\u003c\/p\u003e  \u003cp\u003e                Summary\u003c\/p\u003e  \u003cp\u003en CHAPTER 3: Comparison between upgrade methods 151\u003c\/p\u003e  \u003cp\u003eComparison between methods                9\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003eReal Application testing (RAT)            10\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003eHow to choose best Upgrade method         11\u003c\/p\u003e                  Summary\u003cp\u003e\u003c\/p\u003e  \u003cp\u003en CHAPTER 4: Upgrade using Database backup    159\u003c\/p\u003e  \u003cp\u003eCold backup                               \u003c\/p\u003e  \u003cp\u003eHot backup (User-Managed)                 \u003c\/p\u003e  \u003cp\u003eLogical backup (expdp\/impdp)              \u003c\/p\u003e  RMAN backup (using duplicate option)      \u003cp\u003e\u003c\/p\u003e  \u003cp\u003eSummary                                   \u003c\/p\u003e  \u003cp\u003en CHAPTER 5: Database Migration methods 171\u003c\/p\u003e  \u003cp\u003eExport\/Import                             \u003c\/p\u003e  Datapump                                  \u003cp\u003e\u003c\/p\u003e  \u003cp\u003eTransportable Tablespace (TTS)            \u003c\/p\u003e  \u003cp\u003eGolden Gate                               \u003c\/p\u003e  \u003cp\u003eCopy table as select (CTAS)               \u003c\/p\u003e  \u003cp\u003eTransport Database                        \u003c\/p\u003e  \u003cp\u003eHeterogenous Standby database             \u003c\/p\u003e  Oracle Streams                            \u003cp\u003e\u003c\/p\u003e  \u003cp\u003e                Summary\u003c\/p\u003e  \u003cp\u003en CHAPTER 6: Migration of Oracle database from Non-ASM to ASM 175\u003c\/p\u003e  \u003cp\u003eIntroduction                              \u003c\/p\u003e  \u003cp\u003eMoving Datafiles Online from NON-ASM \u0026lt; \u003c\/p\u003e\n\u003cp\u003eto ASM                                    \u003c\/p\u003e  \u003cp\u003e\u003cstrong\u003eMigrating Oracle 12c CDB with PDBs from \u003c\/strong\u003e\u003c\/p\u003e  \u003cp\u003e\u003cstrong\u003eNON ASM to ASM using EM Cloud Control 13c ............... \u003c\/strong\u003e\u003c\/p\u003e  \u003cp\u003e\u003cstrong\u003eMigrating Oracle 12c CDB with PDBs from \u003c\/strong\u003e\u003c\/p\u003e  \u003cp\u003e\u003cstrong\u003eNON ASM to ASM using RMAN\u003c\/strong\u003e                 \u003c\/p\u003e  \u003cp\u003eSummary                                   \u003c\/p\u003e  \u003cp\u003en CHAPTER 7: GI and DB upgrade in RAC environment 205\u003c\/p\u003e  \u003cp\u003eIntroduction                              \u003c\/p\u003e  CVU Pre-Upgrade Check tool                \u003cp\u003e\u003c\/p\u003e  \u003cp\u003eExecution Steps for ORAchk                \u003c\/p\u003e  \u003cp\u003eRolling upgrade for Oracle GI             \u003c\/p\u003e  \u003cp\u003eUpgrading 11g RAC to 12c RAC using DBUA   \u003c\/p\u003e  \u003cp\u003eUpgrading 11g RAC to 12c RAC Manual       \u003c\/p\u003e  \u003cp\u003eUpgrading 11g RAC to 12c RAC using EM 13c \u003c\/p\u003e  \u003cp\u003eSummary                                   \u003c\/p\u003e  \u003cp\u003e \u003c\/p\u003e  PART III: Developing Applications 215\u003cp\u003e\u003c\/p\u003e  \u003cp\u003en CHAPTER 8: Database upgrade in DG environment  217\u003c\/p\u003e  \u003cp\u003eDummy Text                                \u003c\/p\u003e  Dummy Text                                \u003cp\u003e\u003c\/p\u003e  \u003cp\u003eVirtual Dummy Text                        \u003c\/p\u003e  \u003cp\u003eDummy Text                                \u003c\/p\u003e  \u003cp\u003eDummy Text                                \u003c\/p\u003e  \u003cp\u003eDummy Text Flow                           \u003c\/p\u003e  Dummy Text                                \u003cp\u003e\u003c\/p\u003e  \u003cp\u003eSummary                                   \u003c\/p\u003e  \u003cp\u003en CHAPTER 9: Database upgrade in EBS environment 223\u003c\/p\u003e  \u003cp\u003ePrerequisite steps                        \u003c\/p\u003e  Preupgrade steps                          \u003cp\u003e\u003c\/p\u003e  \u003cp\u003eUpgrade steps                             \u003c\/p\u003e  \u003cp\u003ePost upgrade steps                        \u003c\/p\u003e  \u003cp\u003eSummary                                   \u003c\/p\u003e\n\u003cp\u003eCHAPTER 10: Database upgrade in 12c Multitenant environment\u003c\/p\u003e  \u003cp\u003eMigrate lower version database to Multitenant architecture \u003c\/p\u003e  Container database upgrade                \u003cp\u003e\u003c\/p\u003e  \u003cp\u003ePluggable database upgrade                \u003c\/p\u003e  \u003cp\u003eSummary                                   \u003c\/p\u003e  \u003cp\u003en CHAPTER 11: Databases migrate in Multitenant environment 237\u003c\/p\u003e  \u003cp\u003ePluggable database migrate                \u003c\/p\u003e  Need for Migrate                          \u003cp\u003e\u003c\/p\u003e  \u003cp\u003eMigration steps                           \u003c\/p\u003e  \u003cp\u003eSummary                                   \u003c\/p\u003e  \u003cp\u003en CHAPTER 12: Oracle Database Patching Stratergies 245\u003c\/p\u003e  \u003cp\u003ePatching Introduction                     \u003c\/p\u003e  \u003cp\u003eOpatch tool                               \u003c\/p\u003e  \u003cp\u003eTypes of patches                          \u003c\/p\u003e  \u003cp\u003ePatch apply stratergies (online and offline patching).... \u003c\/p\u003e  \u003cp\u003ePSU and SPU patching                      \u003c\/p\u003e  \u003cp\u003ePatch apply in RAC and DG environment     \u003c\/p\u003e  Datapatch                                 \u003cp\u003e\u003c\/p\u003e  \u003cp\u003eQueryable patch inventory                 \u003c\/p\u003e  \u003cp\u003e          Summary\u003c\/p\u003e  \u003cp\u003en CHAPTER 13: Database Downgrade 263\u003c\/p\u003e  Introduction                              \u003cp\u003e\u003c\/p\u003e  \u003cp\u003eLimitations of Oracle database downgrade  \u003c\/p\u003e  \u003cp\u003eDatabase downgrade steps                  \u003c\/p\u003e  \u003cp\u003eDowngrade using database flashback        \u003c\/p\u003e  \u003cp\u003eSummary                                   \u003c\/p\u003e  \u003cp\u003en CHAPTER 14: Database upgrade in 12.2 281\u003c\/p\u003e  Preupgrade checks                         \u003cp\u003e\u003c\/p\u003e  \u003cp\u003eUpgrade Emulation                         \u003c\/p\u003e  \u003cp\u003eDBUA                                      \u003c\/p\u003e  \u003cp\u003eManual Database upgrade                   \u003c\/p\u003e  \u003cp\u003ePluggable database upgrade                \u003c\/p\u003e  Downgrade 12.2 database to earlier version............... \u003cp\u003e\u003c\/p\u003e  \u003cp\u003eSummary                                   \u003c\/p\u003e  \u003cp\u003en  \u003c\/p\u003e  \u003cp\u003en APPENDIX A: Reference Tables 383\u003c\/p\u003e  \u003cp\u003en APPENDIX B: Dummy Text 395\u003c\/p\u003e  \u003cp\u003e\u003cb\u003e \u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003eINDEX 433\u003c\/p\u003e\n\u003c\/div\u003e","brand":"APress","offers":[{"title":"Default Title","offer_id":48739662266711,"sku":"9781484223277","price":44.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781484223277.jpg?v=1720052846"},{"product_id":"pandas-for-everyone-9780137891153","title":"Pandas for Everyone","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cstrong\u003eDaniel Chen\u003c\/strong\u003e is a graduate student in the Interdisciplinary PhD program in Genetics, Bioinformatics \u0026amp; Computational Biology (GBCB) at Virginia Polytechnic Institute and State University (Virginia Tech). He is involved with Software Carpentry as an instructor, Mentoring Committee Member, and currently serves as the Assessment Committee Chair. He completed his Masters in Public Health at Columbia University Mailman School of Public Health in Epidemiology with a certificate in Advanced Epidemiology and currently extending his Master's thesis work in the Social and Decision Analytics Laboratory under the Virginia Bioinformatics Institute on attitude diffusion in social networks.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cem\u003eForeword by Anne M. Brown\u003c\/em\u003e     xxiii\u003c\/p\u003e \u003cp\u003e\u003cem\u003eForeword by Jared Lander\u003c\/em\u003e     xxv\u003c\/p\u003e \u003cp\u003e\u003cem\u003ePreface\u003c\/em\u003e     xxvii\u003c\/p\u003e \u003cp\u003e\u003cem\u003eChanges in the Second Edition\u003c\/em\u003e     xxxix\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cstrong\u003ePart I: Introduction\u003c\/strong\u003e    1\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eChapter 1. Pandas DataFrame Basics\u003c\/strong\u003e     3\u003c\/p\u003e \u003cp\u003e       Learning Objectives      3\u003c\/p\u003e \u003cp\u003e       1.1 Introduction      3\u003c\/p\u003e \u003cp\u003e       1.2 Load Your First Data Set      4\u003c\/p\u003e \u003cp\u003e       1.3 Look at Columns, Rows, and Cells      6\u003c\/p\u003e \u003cp\u003e       1.4 Grouped and Aggregated Calculations      23\u003c\/p\u003e \u003cp\u003e       1.5 Basic Plot      27\u003c\/p\u003e \u003cp\u003e       Conclusion      28\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eChapter 2. Pandas Data Structures Basics\u003c\/strong\u003e      31\u003c\/p\u003e \u003cp\u003e       Learning Objectives      31\u003c\/p\u003e \u003cp\u003e       2.1 Create Your Own Data      31\u003c\/p\u003e \u003cp\u003e       2.2 The Series      33\u003c\/p\u003e \u003cp\u003e       2.3 The DataFrame      42\u003c\/p\u003e \u003cp\u003e       2.4 Making Changes to Series and DataFrames      45\u003c\/p\u003e \u003cp\u003e       2.5 Exporting and Importing Data      52\u003c\/p\u003e \u003cp\u003e       Conclusion      63\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eChapter 3. Plotting Basics\u003c\/strong\u003e      65\u003c\/p\u003e \u003cp\u003e       Learning Objectives      65\u003c\/p\u003e \u003cp\u003e       3.1 Why Visualize Data?       65\u003c\/p\u003e \u003cp\u003e       3.2 Matplotlib Basics      66\u003c\/p\u003e \u003cp\u003e       3.3 Statistical Graphics Using matplotlib      72\u003c\/p\u003e \u003cp\u003e       3.4 Seaborn      78\u003c\/p\u003e \u003cp\u003e       3.5 Pandas Plotting Method      111\u003c\/p\u003e \u003cp\u003e       Conclusion      115\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eChapter 4. Tidy Data\u003c\/strong\u003e      117\u003c\/p\u003e \u003cp\u003e       Learning Objectives      117\u003c\/p\u003e \u003cp\u003e       Note About This Chapter       117\u003c\/p\u003e \u003cp\u003e       4.1 Columns Contain Values, Not Variables      118\u003c\/p\u003e \u003cp\u003e       4.2 Columns Contain Multiple Variables      122\u003c\/p\u003e \u003cp\u003e       4.3 Variables in Both Rows and Columns      126\u003c\/p\u003e \u003cp\u003e       Conclusion      129\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eChapter 5. Apply Functions\u003c\/strong\u003e      131\u003c\/p\u003e \u003cp\u003e       Learning Objectives      131\u003c\/p\u003e \u003cp\u003e       Note About This Chapter      131\u003c\/p\u003e \u003cp\u003e       5.1 Primer on Functions      131\u003c\/p\u003e \u003cp\u003e       5.2 Apply (Basics)       133\u003c\/p\u003e \u003cp\u003e       5.3 Vectorized Functions      138\u003c\/p\u003e \u003cp\u003e       5.4 Lambda Functions (Anonymous Functions)       141\u003c\/p\u003e \u003cp\u003e       Conclusion      142\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cstrong\u003ePart II: Data Processing\u003c\/strong\u003e     143\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eChapter 6. Data Assembly\u003c\/strong\u003e      145\u003c\/p\u003e \u003cp\u003e       Learning Objectives      145\u003c\/p\u003e \u003cp\u003e       6.1 Combine Data Sets      145\u003c\/p\u003e \u003cp\u003e       6.2 Concatenation      146\u003c\/p\u003e \u003cp\u003e       6.3 Observational Units Across Multiple Tables      154\u003c\/p\u003e \u003cp\u003e       6.4 Merge Multiple Data Sets      160\u003c\/p\u003e \u003cp\u003e       Conclusion      167\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eChapter 7. Data Normalization\u003c\/strong\u003e      169\u003c\/p\u003e \u003cp\u003e       Learning Objectives      169\u003c\/p\u003e \u003cp\u003e       7.1 Multiple Observational Units in a Table (Normalization)     169\u003c\/p\u003e \u003cp\u003e       Conclusion      173\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eChapter 8. Groupby Operations: Split-Apply-Combine\u003c\/strong\u003e      175\u003c\/p\u003e \u003cp\u003e       Learning Objectives      175\u003c\/p\u003e \u003cp\u003e       8.1 Aggregate      176\u003c\/p\u003e \u003cp\u003e       8.2 Transform      184\u003c\/p\u003e \u003cp\u003e       8.3 Filter      188\u003c\/p\u003e \u003cp\u003e       8.4 The pandas.core.groupby.DataFrameGroupBy object      190\u003c\/p\u003e \u003cp\u003e       8.5 Working with a MultiIndex      195\u003c\/p\u003e \u003cp\u003e       Conclusion      199\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cstrong\u003ePart III: Data Types\u003c\/strong\u003e    203\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eChapter 9. Missing Data\u003c\/strong\u003e      203\u003c\/p\u003e \u003cp\u003e       Learning Objectives      203\u003c\/p\u003e \u003cp\u003e       9.1 What Is a NaN Value?       203\u003c\/p\u003e \u003cp\u003e       9.2 Where Do Missing Values Come From?       205\u003c\/p\u003e \u003cp\u003e       9.3 Working with Missing Data      210\u003c\/p\u003e \u003cp\u003e       9.4 Pandas Built-In NA Missing      216\u003c\/p\u003e \u003cp\u003e       Conclusion      218\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eChapter 10. Data Types\u003c\/strong\u003e      219\u003c\/p\u003e \u003cp\u003e       Learning Objectives      219\u003c\/p\u003e \u003cp\u003e       10.1 Data Types      219\u003c\/p\u003e \u003cp\u003e       10.2 Converting Types      220\u003c\/p\u003e \u003cp\u003e       10.3 Categorical Data      225\u003c\/p\u003e \u003cp\u003e       Conclusion      227\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eChapter 11. Strings and Text Data\u003c\/strong\u003e      229\u003c\/p\u003e \u003cp\u003e       Introduction      229\u003c\/p\u003e \u003cp\u003e       Learning Objectives      229\u003c\/p\u003e \u003cp\u003e       11.1 Strings      229\u003c\/p\u003e \u003cp\u003e       11.2 String Methods      233\u003c\/p\u003e \u003cp\u003e       11.3 More String Methods      234\u003c\/p\u003e \u003cp\u003e       11.4 String Formatting (F-Strings)       236\u003c\/p\u003e \u003cp\u003e       11.5 Regular Expressions (RegEx)      239\u003c\/p\u003e \u003cp\u003e       11.6 The regex Library      247\u003c\/p\u003e \u003cp\u003e       Conclusion      247\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eChapter 12. Dates and Times \u003c\/strong\u003e     249\u003c\/p\u003e \u003cp\u003e       Learning Objectives      249\u003c\/p\u003e \u003cp\u003e       12.1 Python's datetime Object      249\u003c\/p\u003e \u003cp\u003e       12.2 Converting to datetime      250\u003c\/p\u003e \u003cp\u003e       12.3 Loading Data That Include Dates      253\u003c\/p\u003e \u003cp\u003e       12.4 Extracting Date Components      254\u003c\/p\u003e \u003cp\u003e       12.5 Date Calculations and Timedeltas      257\u003c\/p\u003e \u003cp\u003e       12.6 Datetime Methods      259\u003c\/p\u003e \u003cp\u003e       12.7 Getting Stock Data      261\u003c\/p\u003e \u003cp\u003e       12.8 Subsetting Data Based on Dates      263\u003c\/p\u003e \u003cp\u003e       12.9 Date Ranges      266\u003c\/p\u003e \u003cp\u003e       12.10 Shifting Values      270\u003c\/p\u003e \u003cp\u003e       12.11 Resampling      276\u003c\/p\u003e \u003cp\u003e       12.12 Time Zones      278\u003c\/p\u003e \u003cp\u003e       12.13 Arrow for Better Dates and Times      280\u003c\/p\u003e \u003cp\u003e       Conclusion      280\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cstrong\u003ePart IV: Data Modeling\u003c\/strong\u003e    281\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eChapter 13. Linear Regression (Continuous Outcome Variable)\u003c\/strong\u003e      283\u003c\/p\u003e \u003cp\u003e       13.1 Simple Linear Regression      283\u003c\/p\u003e \u003cp\u003e       13.2 Multiple Regression      287\u003c\/p\u003e \u003cp\u003e       13.3 Models with Categorical Variables      289\u003c\/p\u003e \u003cp\u003e       13.4 One-Hot Encoding in scikit-learn with Transformer Pipelines      294\u003c\/p\u003e \u003cp\u003e       Conclusion      296\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eChapter 14. Generalized Linear Models\u003c\/strong\u003e      297\u003c\/p\u003e \u003cp\u003e       About This Chapter      297\u003c\/p\u003e \u003cp\u003e       14.1 Logistic Regression (Binary Outcome Variable)       297\u003c\/p\u003e \u003cp\u003e       14.2 Poisson Regression (Count Outcome Variable)       304\u003c\/p\u003e \u003cp\u003e       14.3 More Generalized Linear Models      308\u003c\/p\u003e \u003cp\u003e       Conclusion      309\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eChapter 15. Survival Analysis\u003c\/strong\u003e      311\u003c\/p\u003e \u003cp\u003e       15.1 Survival Data      311\u003c\/p\u003e \u003cp\u003e       15.2 Kaplan Meier Curves      312\u003c\/p\u003e \u003cp\u003e       15.3 Cox Proportional Hazard Model      314\u003c\/p\u003e \u003cp\u003e       Conclusion      317\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eChapter 16. Model Diagnostics\u003c\/strong\u003e      319\u003c\/p\u003e \u003cp\u003e       16.1 Residuals      319\u003c\/p\u003e \u003cp\u003e       16.2 Comparing Multiple Models      324\u003c\/p\u003e \u003cp\u003e       16.3 k-Fold Cross-Validation      329\u003c\/p\u003e \u003cp\u003e       Conclusion      334\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eChapter 17. Regularization\u003c\/strong\u003e      335\u003c\/p\u003e \u003cp\u003e       17.1 Why Regularize?       335\u003c\/p\u003e \u003cp\u003e       17.2 LASSO Regression      337\u003c\/p\u003e \u003cp\u003e       17.3 Ridge Regression      338\u003c\/p\u003e \u003cp\u003e       17.4 Elastic Net      340\u003c\/p\u003e \u003cp\u003e       17.5 Cross-Validation      341\u003c\/p\u003e \u003cp\u003e       Conclusion      343\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eChapter 18. Clustering\u003c\/strong\u003e      345\u003c\/p\u003e \u003cp\u003e       18.1 k-Means      345\u003c\/p\u003e \u003cp\u003e       18.2 Hierarchical Clustering      351\u003c\/p\u003e \u003cp\u003e       Conclusion     356\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cstrong\u003ePart V. Conclusion\u003c\/strong\u003e    357\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eChapter 19. Life Outside of Pandas\u003c\/strong\u003e      359\u003c\/p\u003e \u003cp\u003e       19.1 The (Scientific) Computing Stack      359\u003c\/p\u003e \u003cp\u003e       19.2 Performance      360\u003c\/p\u003e \u003cp\u003e       19.3 Dask      360\u003c\/p\u003e \u003cp\u003e       19.4 Siuba      360\u003c\/p\u003e \u003cp\u003e       19.5 Ibis      361\u003c\/p\u003e \u003cp\u003e       19.6 Polars      361\u003c\/p\u003e \u003cp\u003e       19.7 PyJanitor      361\u003c\/p\u003e \u003cp\u003e       19.8 Pandera      361\u003c\/p\u003e \u003cp\u003e       19.9 Machine Learning      361\u003c\/p\u003e \u003cp\u003e       19.10 Publishing      362\u003c\/p\u003e \u003cp\u003e       19.11 Dashboards      362\u003c\/p\u003e \u003cp\u003e       Conclusion      362\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eChapter 20. It's Dangerous To Go Alone!\u003c\/strong\u003e      363\u003c\/p\u003e \u003cp\u003e       20.1 Local Meetups      363\u003c\/p\u003e \u003cp\u003e       20.2 Conferences      363\u003c\/p\u003e \u003cp\u003e       20.3 The Carpentries      364\u003c\/p\u003e \u003cp\u003e       20.4 Podcasts      364\u003c\/p\u003e \u003cp\u003e       20.5 Other Resources      365\u003c\/p\u003e \u003cp\u003e       Conclusion      365\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eAppendices\u003c\/strong\u003e      367\u003c\/p\u003e \u003cp\u003eA.      Concept Maps      369\u003cbr\u003eB.      Installation and Setup     373\u003cbr\u003eC.      Command Line     377\u003cbr\u003eD.      Project Templates     379\u003cbr\u003eE.      Using Python       381\u003cbr\u003eF.       Working Directories       383\u003cbr\u003eG.      Environments       385\u003cbr\u003eH.      Install Packages       389\u003cbr\u003eI.       Importing Libraries       391\u003cbr\u003eJ.       Code Style       393\u003cbr\u003eK.      Containers: Lists, Tuples, and Dictionaries       395\u003cbr\u003eL.      Slice Values       399\u003cbr\u003eM.     Loops       401\u003cbr\u003eN.     Comprehensions       403\u003cbr\u003eO.     Functions       405\u003cbr\u003eP.      Ranges and Generators       409\u003cbr\u003eQ.     Multiple Assignment       413\u003cbr\u003eR.     Numpy ndarray       415\u003cbr\u003eS.     Classes       417\u003cbr\u003eT.      SettingWithCopyWarning       419\u003cbr\u003eU.     Method Chaining       423\u003cbr\u003eV.      Timing Code       427\u003cbr\u003eW.     String Formatting       429\u003cbr\u003eX.      Conditionals (if-elif-else)        433\u003cbr\u003eY.      New York ACS Logistic Regression Example       435\u003cbr\u003eZ.      Replicating Results in R       443\u003cbr\u003e\u003cbr\u003e\u003c\/p\u003e \u003cp\u003e\u003cem\u003eIndex\u003c\/em\u003e      451\u003c\/p\u003e","brand":"Pearson Education (US)","offers":[{"title":"Default Title","offer_id":48864176734551,"sku":"9780137891153","price":34.19,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780137891153.jpg?v=1722270750"},{"product_id":"efficient-mysql-performance-9781098105099","title":"Efficient MySQL Performance","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis practical book bridges the gap by teaching software engineers mid-level MySQL knowledge beyond the fundamentals, but well shy of deep-level internals required by database administrators (DBAs).","brand":"O'Reilly Media","offers":[{"title":"Default Title","offer_id":48866330870103,"sku":"9781098105099","price":39.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781098105099.jpg?v=1722278163"},{"product_id":"data-pipelines-pocket-reference-9781492087830","title":"Data Pipelines Pocket Reference","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eData pipelines are the foundation for success in data analytics. Moving data from numerous diverse sources and transforming it to provide context is the difference between having data and actually gaining value from it. This pocket reference defines data pipelines and explains how they work in today's modern data stack.","brand":"O'Reilly Media","offers":[{"title":"Default Title","offer_id":48867310502231,"sku":"9781492087830","price":20.39,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781492087830.jpg?v=1722282726"},{"product_id":"data-storage-systems-management-security-issues-9781536128277","title":"Data Storage: Systems, Management \u0026 Security","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e","brand":"Nova Science Publishers Inc","offers":[{"title":"Default Title","offer_id":48886075687255,"sku":"9781536128277","price":83.29,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781536128277.jpg?v=1722538729"},{"product_id":"graph-databases-in-action-9781617296376","title":"Graph Databases in Action","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003ci\u003eGraph Databases in Action\u003c\/i\u003e teaches readers everything they need to know to begin building and running applications powered by graph databases. Right off the bat, seasoned graph database experts introduce readers to just enough graph theory, the graph database ecosystem, and a variety of datastores. They also explore modelling basics in action with real-world examples, then go hands-on with querying, coding traversals, parsing results, and other essential tasks as readers build their own graph-backed social network app complete with a recommendation engine!\u003c\/p\u003e \u003cp\u003e\u003cb\u003e \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eKey Features\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e·   Graph database fundamentals\u003c\/p\u003e \u003cp\u003e·   An overview of the graph database ecosystem\u003c\/p\u003e \u003cp\u003e·   Relational vs. graph database modelling\u003c\/p\u003e \u003cp\u003e·   Querying graphs using Gremlin\u003c\/p\u003e \u003cp\u003e·   Real-world common graph use cases\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003eFor readers with basic Java and application development skills building in RDBMS systems such as Oracle, SQL Server, MySQL, and Postgres. No experience with graph databases is required.\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cb\u003eAbout the technology \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eGraph databases store interconnected data in a more natural form, making them superior tools for representing data with rich relationships. Unlike in relational database management systems (RDBMS), where a more rigid view of data connections results in the loss of valuable insights, in graph databases, data connections are first priority.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003e\u003cb\u003eDave Bechberger\u003c\/b\u003e has extensive experience using graph databases as a product architect and a consultant. He’s spent his career leveraging cutting-edge technologies to build software in complex data domains such as bioinformatics, oil and gas, and supply chain management. He’s an active member of the graph community and has presented on a wide variety of graph-related topics at national and international conferences.\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cb\u003eJosh Perryman\u003c\/b\u003e is technologist with over two decades of diverse experience building and maintaining complex systems, including high performance computing (HPC) environments. Since 2014 he has focused on graph databases, especially in distributed or big data environments, and he regularly blogs and speaks at conferences about graph databases.\u003c\/p\u003e","brand":"Manning Publications","offers":[{"title":"Default Title","offer_id":48886899769687,"sku":"9781617296376","price":37.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781617296376.jpg?v=1722542086"},{"product_id":"data-lake-architecture-designing-the-data-lake-and-avoiding-the-garbage-dump-9781634621175","title":"Data Lake Architecture: Designing the Data Lake","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eOrganizations invest incredible amounts of time and money obtaining and then storing big data in data stores called data lakes. But how many of these organizations can actually get the data back out in a useable form? Very few can turn the data lake into an information gold mine. 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With more than 200,000 copies in print worldwide, his books have become international bestsellers and have been formally endorsed by senior members of major IT organizations, such as IBM, Microsoft, Oracle, Intel, Accenture, IEEE, HL7, MITRE, SAP, CISCO, HP and many others. As CEO of Arcitura Education Inc., Thomas has led the development of curricula for the internationally recognized Big Data Science Certified Professional (BDSCP), Cloud Certified Professional (CCP) and SOA Certified Professional (SOACP) accreditation programs, which have established a series of formal, vendor-neutral industry certifications obtained by thousands of IT professionals around the world. Thomas has toured more than 20 countries as a speaker and instructor. More than 100 articles and interviews by Thomas have been published in numerous publica\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eAcknowledgments     xvii\u003cbr\u003eReader Services     xviii\u003cbr\u003ePART I: THE FUNDAMENTALS OF BIG DATA\u003cbr\u003eChapter 1: Understanding Big Data     3\u003c\/b\u003e \u003cbr\u003eConcepts and Terminology     5 \u003cbr\u003eDatasets     5 \u003cbr\u003eData Analysis     6 \u003cbr\u003eData Analytics     6 \u003cbr\u003eDescriptive Analytics     8 \u003cbr\u003eDiagnostic Analytics     9 \u003cbr\u003ePredictive Analytics     10 \u003cbr\u003ePrescriptive Analytics     11 \u003cbr\u003eBusiness Intelligence (BI)     12 \u003cbr\u003eKey Performance Indicators (KPI)     12 \u003cbr\u003eBig Data Characteristics     13 \u003cbr\u003eVolume     14 \u003cbr\u003eVelocity     14 \u003cbr\u003eVariety     15 \u003cbr\u003eVeracity     16 \u003cbr\u003eValue     16 \u003cbr\u003eDifferent Types of Data     17 \u003cbr\u003eStructured Data     18 \u003cbr\u003eUnstructured Data     19 \u003cbr\u003eSemi-structured Data     19 \u003cbr\u003eMetadata     20 \u003cbr\u003eCase Study Background     20 \u003cbr\u003eHistory     20 \u003cbr\u003eTechnical Infrastructure and Automation Environment     21 \u003cbr\u003eBusiness Goals and Obstacles     22 \u003cbr\u003eCase Study Example     24 \u003cbr\u003eIdentifying Data Characteristics     26 \u003cbr\u003eVolume     26 \u003cbr\u003eVelocity     26 \u003cbr\u003eVariety     26 \u003cbr\u003eVeracity     26 \u003cbr\u003eValue     27 \u003cbr\u003eIdentifying Types of Data     27 \u003cbr\u003e \u003cb\u003eChapter 2: Business Motivations and Drivers for Big Data Adoption     29\u003c\/b\u003e \u003cbr\u003eMarketplace Dynamics     30 \u003cbr\u003eBusiness Architecture     33 \u003cbr\u003eBusiness Process Management     36 \u003cbr\u003eInformation and Communications Technology     37 \u003cbr\u003eData Analytics and Data Science     37 \u003cbr\u003eDigitization     38 \u003cbr\u003eAffordable Technology and Commodity Hardware     38 \u003cbr\u003eSocial Media     39 \u003cbr\u003eHyper-Connected Communities and Devices     40 \u003cbr\u003eCloud Computing     40 \u003cbr\u003eInternet of Everything (IoE)     42 \u003cbr\u003eCase Study Example     43 \u003cbr\u003e \u003cb\u003eChapter 3: Big Data Adoption and Planning Considerations     47\u003c\/b\u003e \u003cbr\u003eOrganization Prerequisites     49 \u003cbr\u003eData Procurement     49 \u003cbr\u003ePrivacy     49 \u003cbr\u003eSecurity     50 \u003cbr\u003eProvenance     51 \u003cbr\u003eLimited Realtime Support     52 \u003cbr\u003eDistinct Performance Challenges     53 \u003cbr\u003eDistinct Governance Requirements     53 \u003cbr\u003eDistinct Methodology     53 \u003cbr\u003eClouds     54 \u003cbr\u003eBig Data Analytics Lifecycle     55 \u003cbr\u003eBusiness Case Evaluation     56 \u003cbr\u003eData Identification     57 \u003cbr\u003eData Acquisition and Filtering     58 \u003cbr\u003eData Extraction     60 \u003cbr\u003eData Validation and Cleansing     62 \u003cbr\u003eData Aggregation and Representation     64 \u003cbr\u003eData Analysis     66 \u003cbr\u003eData Visualization     68 \u003cbr\u003eUtilization of Analysis Results     69 \u003cbr\u003eCase Study Example     71 \u003cbr\u003eBig Data Analytics Lifecycle     73 \u003cbr\u003eBusiness Case Evaluation     73 \u003cbr\u003eData Identification     74 \u003cbr\u003eData Acquisition and Filtering     74 \u003cbr\u003eData Extraction     74 \u003cbr\u003eData Validation and Cleansing     75 \u003cbr\u003eData Aggregation and Representation     75 \u003cbr\u003eData Analysis     75 \u003cbr\u003eData Visualization     76 \u003cbr\u003eUtilization of Analysis Results     76 \u003cbr\u003e \u003cb\u003eChapter 4: Enterprise Technologies and Big Data Business Intelligence     77\u003c\/b\u003e \u003cbr\u003eOnline Transaction Processing (OLTP)     78 \u003cbr\u003eOnline Analytical Processing (OLAP)     79 \u003cbr\u003eExtract Transform Load (ETL)     79 \u003cbr\u003eData Warehouses     80 \u003cbr\u003eData Marts     81 \u003cbr\u003eTraditional BI     82 \u003cbr\u003eAd-hoc Reports     82 \u003cbr\u003eDashboards     82 \u003cbr\u003eBig Data BI     84 \u003cbr\u003eTraditional Data Visualization     84 \u003cbr\u003eData Visualization for Big Data     85 \u003cbr\u003eCase Study Example     86 \u003cbr\u003eEnterprise Technology     86 \u003cbr\u003eBig Data Business Intelligence     87 \u003cbr\u003e \u003cb\u003ePART II: STORING AND ANALYZING BIG DATA\u003cbr\u003eChapter 5: Big Data Storage Concepts     91\u003c\/b\u003e \u003cbr\u003eClusters     93 \u003cbr\u003eFile Systems and Distributed File Systems     93 \u003cbr\u003eNoSQL     94 \u003cbr\u003eSharding     95 \u003cbr\u003eReplication     97 \u003cbr\u003eMaster-Slave     98 \u003cbr\u003ePeer-to-Peer     100 \u003cbr\u003eSharding and Replication     103 \u003cbr\u003eCombining Sharding and Master-Slave Replication     104 \u003cbr\u003eCombining Sharding and Peer-to-Peer Replication     105 \u003cbr\u003eCAP Theorem     106 \u003cbr\u003eACID     108 \u003cbr\u003eBASE     113 \u003cbr\u003eCase Study Example     117 \u003cbr\u003e \u003cb\u003eChapter 6: Big Data Processing Concepts     119\u003c\/b\u003e \u003cbr\u003eParallel Data Processing     120 \u003cbr\u003eDistributed Data Processing     121 \u003cbr\u003eHadoop     122 \u003cbr\u003eProcessing Workloads     122 \u003cbr\u003eBatch     123 \u003cbr\u003eTransactional     123 \u003cbr\u003eCluster     124 \u003cbr\u003eProcessing in Batch Mode     125 \u003cbr\u003eBatch Processing with MapReduce     125 \u003cbr\u003eMap and Reduce Tasks     126 \u003cbr\u003eMap     127 \u003cbr\u003eCombine     127 \u003cbr\u003ePartition     129 \u003cbr\u003eShuffle and Sort     130 \u003cbr\u003eReduce     131 \u003cbr\u003eA Simple MapReduce Example     133 \u003cbr\u003eUnderstanding MapReduce Algorithms     134 \u003cbr\u003eProcessing in Realtime Mode     137 \u003cbr\u003eSpeed Consistency Volume (SCV)     137 \u003cbr\u003eEvent Stream Processing     140 \u003cbr\u003eComplex Event Processing     141 \u003cbr\u003eRealtime Big Data Processing and SCV     141 \u003cbr\u003eRealtime Big Data Processing and MapReduce     142 \u003cbr\u003eCase Study Example     143 \u003cbr\u003eProcessing Workloads     143 \u003cbr\u003eProcessing in Batch Mode     143 \u003cbr\u003eProcessing in Realtime     144 \u003cbr\u003e \u003cb\u003eChapter 7: Big Data Storage Technology     145\u003c\/b\u003e \u003cbr\u003eOn-Disk Storage Devices     147 \u003cbr\u003eDistributed File Systems     147 \u003cbr\u003eRDBMS Databases     149 \u003cbr\u003eNoSQL Databases     152 \u003cbr\u003eCharacteristics     152 \u003cbr\u003eRationale     153 \u003cbr\u003eTypes     154 \u003cbr\u003eKey-Value     156 \u003cbr\u003eDocument     157 \u003cbr\u003eColumn-Family     159 \u003cbr\u003eGraph     160 \u003cbr\u003eNewSQL Databases     163 \u003cbr\u003eIn-Memory Storage Devices     163 \u003cbr\u003eIn-Memory Data Grids     166 \u003cbr\u003eRead-through     170 \u003cbr\u003eWrite-through     170 \u003cbr\u003eWrite-behind     172 \u003cbr\u003eRefresh-ahead     172 \u003cbr\u003eIn-Memory Databases     175 \u003cbr\u003eCase Study Example     179 \u003cbr\u003e \u003cb\u003eChapter 8: Big Data Analysis Techniques     181\u003c\/b\u003e \u003cbr\u003eQuantitative Analysis     183 \u003cbr\u003eQualitative Analysis     184 \u003cbr\u003eData Mining     184 \u003cbr\u003eStatistical Analysis     184 \u003cbr\u003eA\/B Testing     185 \u003cbr\u003eCorrelation     186 \u003cbr\u003eRegression     188 \u003cbr\u003eMachine Learning     190 \u003cbr\u003eClassification (Supervised Machine Learning)     190 \u003cbr\u003eClustering (Unsupervised Machine Learning)     191 \u003cbr\u003eOutlier Detection     192 \u003cbr\u003eFiltering     193 \u003cbr\u003eSemantic Analysis     195 \u003cbr\u003eNatural Language Processing     195 \u003cbr\u003eText Analytics     196 \u003cbr\u003eSentiment Analysis     197 \u003cbr\u003eVisual Analysis     198 \u003cbr\u003eHeat Maps     198 \u003cbr\u003eTime Series Plots     200 \u003cbr\u003eNetwork Graphs     201 \u003cbr\u003eSpatial Data Mapping     202 \u003cbr\u003eCase Study Example     204 \u003cbr\u003eCorrelation     204 \u003cbr\u003eRegression     204 \u003cbr\u003eTime Series Plot     205 \u003cbr\u003eClustering     205 \u003cbr\u003eClassification     205 \u003cbr\u003e \u003cb\u003eAppendix A: Case Study Conclusion     207\u003cbr\u003eAbout the Authors     211\u003c\/b\u003e \u003cbr\u003eThomas Erl     211 \u003cbr\u003eWajid Khattak     211 \u003cbr\u003ePaul Buhler     212 \u003cbr\u003e \u003cb\u003eIndex     213\u003c\/b\u003e \u003cbr\u003e","brand":"Pearson Education (US)","offers":[{"title":"Default Title","offer_id":49369127846231,"sku":"9780134291079","price":26.54,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780134291079.jpg?v=1730128503"},{"product_id":"exam-ref-dp600-implementing-analytics-solutions-using-microsoft-fabric-9780135336021","title":"Exam Ref DP600 Implementing Analytics Solutions","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cstrong\u003eDaniil Maslyuk\u003c\/strong\u003e is an independent business intelligence consultant, trainer, and speaker who specializes in Microsoft Power BI. 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In his spare time, he runs a blog called \u003cem\u003eDataMeerkat\u003c\/em\u003e, where he focuses on topics related to\u003c\/p\u003e","brand":"Pearson Education","offers":[{"title":"Default Title","offer_id":49369130238295,"sku":"9780135336021","price":35.99,"currency_code":"GBP","in_stock":true}]},{"product_id":"the-herschel-objects-and-how-to-observe-them-9780387681245","title":"The Herschel Objects and How to Observe Them","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eAmateur astronomers are always on the lookout for new observing challenges. This is a practical guide to locating and viewing the most impressive of Herschel’s star clusters, nebulae and galaxies, cataloging more than 600 of the brightest objects, and offering detailed descriptions and images of 150 to 200 of the best.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eFrom the reviews:\u003c\/p\u003e \u003cp\u003e\u003c\/p\u003e \u003cp\u003e\"Mullaney packs an incredible amount of information into this 166-page book. … All in all, The Herschel Objects, and how to observe them is engaging, challenging, well-written, and comprehensive. So, if you love deep-sky observing – and even if you’ve observed the Astronomical League’s Herschel 400 – Mullaney’s book offers a new list with several hundred additional objects you’ll enjoy.\" (Michael Bakich, Astronomy Magazine, October, 2007)\u003c\/p\u003e \u003cp\u003e\"The Herschel Objects and How to Observe Them is a fine addition to the Springer series of observing guides. Mullaney has been observing the Herschel objects for many years and his passion for them clearly comes across. … Overall though, this is a book that will be a useful addition to any deep-sky observer’s library.\" (Paul Money, BBC Sky at Night, February, 2008)\u003c\/p\u003e \u003cp\u003e\"Mullaney begins with a well-written biographical sketch of Herschel and his family, and explains the significance of the work of this great observational astronomer. … the objects are illustrated with excellent images obtained using a modern charge-coupled device (CCD) system. The book concludes with a list of 618 targets that would provide for a lifetime of study. The book will be of greatest interest to experienced observers who wish to push on to the most challenging deep sky objects. … Summing Up: Recommended. General readers.\" (D. E. Hogg, CHOICE, Vol. 45 (6), February, 2008)\u003c\/p\u003e \u003cp\u003e\"The book opens with a few short chapters on Herschel himself together with a brief introduction to observing techniques … . rounded out with some objects that the author regards as showpieces that were not discovered by Herschel. Any collection of these will of course be very subjective. … I found the book’s reproductions to be a cut above the usual Springer ones and the book does offers something sufficiently different … and the Astronomical League guides to make it worth adding to your collection.\" (Owen Brazell, The Observatory, Vol. 128 (1203), 2008)\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eWilliam Herschel's Life, Telescopes and Catalogs.- Herschel's Telescopes.- Herschel's Catalogs and Classes.- Observing Techniques.- Exploring The Herschel Showpieces.- Showpieces of Class I.- Showpieces of Class IV.- Showpieces of Class V.- Showpieces of Class VI.- Showpieces of Class VII.- Showpieces of Class VIII.- Samples of Classes II \u0026amp; III.- Showpieces Missed by Herschel.- The “Missing” Herschel Objects.- Conclusion.","brand":"Springer-Verlag New York Inc.","offers":[{"title":"Default Title","offer_id":49401971409239,"sku":"9780387681245","price":23.74,"currency_code":"GBP","in_stock":true}]},{"product_id":"dark-data-9780691182377","title":"Dark Data","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"[A] penetrating study of missing (‘dark’) data and its impacts on decisions—skewing stats, enabling fraud, embedding inequity and triggering preventable catastrophes. Advocating ‘data science judo,’ Hand offers expert training, from recognizing when facts are being cherry-picked to designing randomized trials. A book illuminating shadowed corners in science, medicine and policy.\"\u003cb\u003e---Barbara Kiser, \u003ci\u003eNature\u003c\/i\u003e\u003c\/b\u003e\u003cbr\u003e\"A tour de force. . . . Hand is a good and able guide to take us through the many aspects of dark data that are potentially skewing our understanding of real world observations and potential scientific breakthroughs. 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Advocating ‘data science judo,’ Hand offers expert training, from recognizing when facts are being cherry-picked to designing randomized trials. A book illuminating shadowed corners in science, medicine and policy.\"\u003cb\u003e---Barbara Kiser, \u003ci\u003eNature\u003c\/i\u003e\u003c\/b\u003e\u003cbr\u003e\"A tour de force. . . . Hand is a good and able guide to take us through the many aspects of dark data that are potentially skewing our understanding of real world observations and potential scientific breakthroughs. He writes in an accessible and understandable way too.\"\u003cb\u003e---Simon Cocking, \u003ci\u003eIrish Tech News\u003c\/i\u003e\u003c\/b\u003e\u003cbr\u003e\"Well-written and accessible.\"\u003cb\u003e---Tim Harford, \u003ci\u003eUndercover Economist\u003c\/i\u003e\u003c\/b\u003e\u003cbr\u003e\"You need to read [\u003ci\u003eDark Data\u003c\/i\u003e], and be convinced by David’s reasoning and his examples of cases in which unseen or unreported data play a critical and sometimes even a fatal role. You are likely to walk away with the feeling that the term \u003ci\u003edark data\u003c\/i\u003e is indeed a very effective one to arouse both curiosity and suspicion, mixed with happiness that finally a great term was coined by a statistician—and sadness that the statistician is not you.\"\u003cb\u003e---Xiao-Li Meng, \u003ci\u003eIMS Bulletin\u003c\/i\u003e\u003c\/b\u003e\u003cbr\u003e\"An exploration of a major problem in data analysis with an attempt of classification, analysing causes, mechanisms, and to some extent also suggest mitigations.\"\u003cb\u003e---Adhemar Bultheel, \u003ci\u003eEuropean Mathematical Society\u003c\/i\u003e\u003c\/b\u003e\u003cbr\u003e\"An excellent guide to the many reasons for caution in interpreting data.\"\u003cb\u003e---Diane Coyle, \u003ci\u003eEnlightened Economist\u003c\/i\u003e\u003c\/b\u003e","brand":"Princeton University Press","offers":[{"title":"Default Title","offer_id":49403920384343,"sku":"9780691234465","price":15.29,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780691234465.jpg?v=1730484889"},{"product_id":"fundamentals-of-data-observability-9781098133290","title":"Fundamentals of Data Observability","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e","brand":"O'Reilly Media","offers":[{"title":"Default Title","offer_id":49406793056599,"sku":"9781098133290","price":39.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781098133290.jpg?v=1730497128"},{"product_id":"amazon-redshift-the-definitive-guide-9781098135300","title":"Amazon Redshift The Definitive Guide","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis practical guide thoroughly examines this managed service and demonstrates how you can use it to extract value from your data immediately, rather than go through the heavy lifting required to run a typical data warehouse.","brand":"O'Reilly Media","offers":[{"title":"Default Title","offer_id":49406793220439,"sku":"9781098135300","price":47.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781098135300.jpg?v=1730497129"},{"product_id":"how-to-make-things-faster-9781098147068","title":"How To Make Things Faster","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book explains in a clear and thoughtful voice why systems perform the way they do. It's for anybody who's curious about how computer programs and other processes use their time and about what you can do to improve them.","brand":"O'Reilly Media","offers":[{"title":"Default Title","offer_id":49406794236247,"sku":"9781098147068","price":33.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781098147068.jpg?v=1730497132"},{"product_id":"kimballs-data-warehouse-toolkit-classics-3-volume-set-9781118875186","title":"Kimballs Data Warehouse Toolkit Classics 3 Volume","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49406938775895,"sku":"9781118875186","price":104.5,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781118875186.jpg?v=1730497623"},{"product_id":"data-smart-9781119931386","title":"Data Smart","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eIntroduction xix\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Everything You Ever Needed to Know About Spreadsheets but Were Too Afraid to Ask 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSome Sample Data 2\u003c\/p\u003e \u003cp\u003eAccessing Quick Descriptive Statistics 3\u003c\/p\u003e \u003cp\u003eExcel Tables 4\u003c\/p\u003e \u003cp\u003eFiltering and Sorting 5\u003c\/p\u003e \u003cp\u003eTable Formatting 7\u003c\/p\u003e \u003cp\u003eStructured References 7\u003c\/p\u003e \u003cp\u003eAdding Table Columns 10\u003c\/p\u003e \u003cp\u003eLookup Formulas 11\u003c\/p\u003e \u003cp\u003eVLOOKUP 11\u003c\/p\u003e \u003cp\u003eINDEX\/MATCH 13\u003c\/p\u003e \u003cp\u003eXLOOKUP 15\u003c\/p\u003e \u003cp\u003ePivotTables 16\u003c\/p\u003e \u003cp\u003eUsing Array Formulas 19\u003c\/p\u003e \u003cp\u003eSolving Stuff with Solver 20\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Set It and Forget It: An Introduction to Power Query 27\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhat Is Power Query? 27\u003c\/p\u003e \u003cp\u003eSample Data 28\u003c\/p\u003e \u003cp\u003eStarting Power Query 29\u003c\/p\u003e \u003cp\u003eFiltering Rows 32\u003c\/p\u003e \u003cp\u003eRemoving Columns 33\u003c\/p\u003e \u003cp\u003eFind \u0026amp; Replace 34\u003c\/p\u003e \u003cp\u003eClose \u0026amp; Load to Table 35\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Naïve Bayes and the Incredible Lightness of Being an Idiot 39\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe World's Fastest Intro to Probability Theory 39\u003c\/p\u003e \u003cp\u003eTotaling Conditional Probabilities 40\u003c\/p\u003e \u003cp\u003eJoint Probability, the Chain Rule, and Independence 40\u003c\/p\u003e \u003cp\u003eWhat Happens in a Dependent Situation? 41\u003c\/p\u003e \u003cp\u003eBayes Rule 42\u003c\/p\u003e \u003cp\u003eSeparating the Signal and the Noise 43\u003c\/p\u003e \u003cp\u003eUsing the Bayes Rule to Create an AI Model 44\u003c\/p\u003e \u003cp\u003eHigh-Level Class Probabilities Are Often Assumed to Be Equal 45\u003c\/p\u003e \u003cp\u003eA Couple More Odds and Ends 46\u003c\/p\u003e \u003cp\u003eLet's Get This Excel Party Started 47\u003c\/p\u003e \u003cp\u003eCleaning the Data with Power Query 48\u003c\/p\u003e \u003cp\u003eSplitting on Spaces: Giving Each Word Its Due 50\u003c\/p\u003e \u003cp\u003eCounting Tokens and Calculating Probabilities 55\u003c\/p\u003e \u003cp\u003eWe Have a Model! Let's Use It 58\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Cluster Analysis Part 1: Using K-Means to Segment Your Customer Base 65\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDances at Summer Camp 65\u003c\/p\u003e \u003cp\u003eGetting Real: K-Means Clustering Subscribers in Email Marketing 70\u003c\/p\u003e \u003cp\u003eThe Initial Dataset 71\u003c\/p\u003e \u003cp\u003eDetermining What to Measure 72\u003c\/p\u003e \u003cp\u003eStart with Four Clusters 75\u003c\/p\u003e \u003cp\u003eEuclidean Distance: Measuring Distances as the Crow Flies 76\u003c\/p\u003e \u003cp\u003eSolving for the Cluster Centers 80\u003c\/p\u003e \u003cp\u003eMaking Sense of the Results 82\u003c\/p\u003e \u003cp\u003eGetting the Top Deals by Cluster 83\u003c\/p\u003e \u003cp\u003eThe Silhouette: A Good Way to Let Different K Values Duke It Out 86\u003c\/p\u003e \u003cp\u003eHow About Five Clusters? 95\u003c\/p\u003e \u003cp\u003eSolving for Five Clusters 96\u003c\/p\u003e \u003cp\u003eGetting the Top Deals for All Five Clusters 96\u003c\/p\u003e \u003cp\u003eComputing the Silhouette for 5-Means Clustering 99\u003c\/p\u003e \u003cp\u003eK-Medians Clustering and Asymmetric Distance Measurements 100\u003c\/p\u003e \u003cp\u003eUsing K-Medians Clustering 100\u003c\/p\u003e \u003cp\u003eGetting a More Appropriate Distance Metric 100\u003c\/p\u003e \u003cp\u003ePutting It All in Excel 102\u003c\/p\u003e \u003cp\u003eThe Top Deals for the 5-Medians Clusters 104\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Cluster Analysis Part II: Network Graphs and Community Detection 109\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhat Is a Network Graph? 110\u003c\/p\u003e \u003cp\u003eVisualizing a Simple Graph 110\u003c\/p\u003e \u003cp\u003eBeyond GiGraph and Adjacency Lists 115\u003c\/p\u003e \u003cp\u003eBuilding a Graph from the Wholesale Wine Data 117\u003c\/p\u003e \u003cp\u003eCreating a Cosine Similarity Matrix 118\u003c\/p\u003e \u003cp\u003eProducing an R-Neighborhood Graph 121\u003c\/p\u003e \u003cp\u003eIntroduction to Gephi 123\u003c\/p\u003e \u003cp\u003eCreating a Static Adjacency Matrix 124\u003c\/p\u003e \u003cp\u003eBringing in Your R-Neighborhood Adjacency Matrix into Gephi 124\u003c\/p\u003e \u003cp\u003eNode Degree 128\u003c\/p\u003e \u003cp\u003eTouching the Graph Data 130\u003c\/p\u003e \u003cp\u003eHow Much Is an Edge Worth? Points and Penalties in Graph Modularity 132\u003c\/p\u003e \u003cp\u003eWhat's a Point, and What's a Penalty? 133\u003c\/p\u003e \u003cp\u003eSetting Up the Score Sheet 136\u003c\/p\u003e \u003cp\u003eLet's Get Clustering! 138\u003c\/p\u003e \u003cp\u003eSplit Number 1 138\u003c\/p\u003e \u003cp\u003eSplit 2: Electric Boogaloo 143\u003c\/p\u003e \u003cp\u003eAnd. . .Split3: Split with a Vengeance 145\u003c\/p\u003e \u003cp\u003eEncoding and Analyzing the Communities 146\u003c\/p\u003e \u003cp\u003eThere and Back Again: A Gephi Tale 151\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Regression: The Granddaddy of Supervised Artificial Intelligence 157\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePredicting Pregnant Customers at RetailMart Using Linear Regression 158\u003c\/p\u003e \u003cp\u003eThe Feature Set 159\u003c\/p\u003e \u003cp\u003eAssembling the Training Data 161\u003c\/p\u003e \u003cp\u003eCreating Dummy Variables 163\u003c\/p\u003e \u003cp\u003eLet's Bake Our Own Linear Regression 165\u003c\/p\u003e \u003cp\u003eLinear Regression Statistics: R-Squared, F-Tests, t-Tests 173\u003c\/p\u003e \u003cp\u003eMaking Predictions on Some New Data and Measuring Performance 182\u003c\/p\u003e \u003cp\u003ePredicting Pregnant Customers at RetailMart Using Logistic Regression 192\u003c\/p\u003e \u003cp\u003eFirst You Need a Link Function 192\u003c\/p\u003e \u003cp\u003eHooking Up the Logistic Function and Reoptimizing 193\u003c\/p\u003e \u003cp\u003eBaking an Actual Logistic Regression 196\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Ensemble Models: A Whole Lot of Bad Pizza 203\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eGetting Started Using the Data from Chapter 6 203\u003c\/p\u003e \u003cp\u003eBagging: Randomize, Train, Repeat 204\u003c\/p\u003e \u003cp\u003eDecision Stump is Another Name for a Weak Learner 204\u003c\/p\u003e \u003cp\u003eDoesn't Seem So Weak to Me! 204\u003c\/p\u003e \u003cp\u003eYou Need More Power! 207\u003c\/p\u003e \u003cp\u003eLet's Train It 208\u003c\/p\u003e \u003cp\u003eEvaluating the Bagged Model 220\u003c\/p\u003e \u003cp\u003eBoosting: If You Get It Wrong, Just Boost and Try Again 223\u003c\/p\u003e \u003cp\u003eTraining the Model—Every Feature Gets a Shot 224\u003c\/p\u003e \u003cp\u003eEvaluating the Boosted Model 231\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Forecasting: Breathe Easy: You Can't Win 235\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Sword Trade Is Hopping 236\u003c\/p\u003e \u003cp\u003eGetting Acquainted with Time-Series Data 236\u003c\/p\u003e \u003cp\u003eStarting Slow with Simple Exponential Smoothing 238\u003c\/p\u003e \u003cp\u003eSetting Up the Simple Exponential Smoothing Forecast 240\u003c\/p\u003e \u003cp\u003eYou Might Have a Trend 249\u003c\/p\u003e \u003cp\u003eHolt's Trend-Corrected Exponential Smoothing 250\u003c\/p\u003e \u003cp\u003eSetting Up Holt's Trend-Corrected Smoothing in a Spreadsheet 252\u003c\/p\u003e \u003cp\u003eSo Are You Done? Looking at Autocorrelations 258\u003c\/p\u003e \u003cp\u003eMultiplicative Holt-Winters Exponential Smoothing 266\u003c\/p\u003e \u003cp\u003eSetting the Initial Values for Level, Trend, and Seasonality 268\u003c\/p\u003e \u003cp\u003eGetting Rolling on the Forecast 274\u003c\/p\u003e \u003cp\u003eAnd. . .Optimize! 280\u003c\/p\u003e \u003cp\u003ePutting a Prediction Interval Around the Forecast 283\u003c\/p\u003e \u003cp\u003eCreating a Fan Chart for Effect 287\u003c\/p\u003e \u003cp\u003eForecast Sheets in Excel 289\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Optimization Modeling: Because That \"Fresh-Squeezed\" Orange Juice Ain't Gonna Blend Itself 293\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWait Is This Data Science? 294\u003c\/p\u003e \u003cp\u003eStarting with a Simple Trade-Off 295\u003c\/p\u003e \u003cp\u003eRepresenting the Problem as a Polytope 296\u003c\/p\u003e \u003cp\u003eSolving by Sliding the Level Set 297\u003c\/p\u003e \u003cp\u003eThe Simplex Method: Rooting Around the Corners 298\u003c\/p\u003e \u003cp\u003eWorking in Excel 300\u003c\/p\u003e \u003cp\u003eFresh from the Grove to Your Glass with a Pit Stop Through a Blending Model 305\u003c\/p\u003e \u003cp\u003eLet's Start with Some Specs 307\u003c\/p\u003e \u003cp\u003eComing Back to Consistency 308\u003c\/p\u003e \u003cp\u003ePutting the Data into Excel 309\u003c\/p\u003e \u003cp\u003eSetting Up the Problem in Solver 311\u003c\/p\u003e \u003cp\u003eLowering Your Standards 314\u003c\/p\u003e \u003cp\u003eDead Squirrel Removal: the Minimax Formulation 317\u003c\/p\u003e \u003cp\u003eIf-Then and the \"Big M\" Constraint 320\u003c\/p\u003e \u003cp\u003eMultiplying Variables: Cranking Up the Volume to 11,000 324\u003c\/p\u003e \u003cp\u003eModeling Risk 330\u003c\/p\u003e \u003cp\u003eNormally Distributed Data 331\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Outlier Detection: Just Because They're Odd Doesn't Mean They're Unimportant 339\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eOutliers Are (Bad?) People, Too 340\u003c\/p\u003e \u003cp\u003eThe Fascinating Case of Hadlum v Hadlum 340\u003c\/p\u003e \u003cp\u003eTukey's Fences 341\u003c\/p\u003e \u003cp\u003eApplying Tukey's Fences in a Spreadsheet 342\u003c\/p\u003e \u003cp\u003eThe Limitations of This Simple Approach 345\u003c\/p\u003e \u003cp\u003eTerrible at Nothing, Bad at Everything 346\u003c\/p\u003e \u003cp\u003ePreparing Data for Graphing 347\u003c\/p\u003e \u003cp\u003eCreating a Graph 350\u003c\/p\u003e \u003cp\u003eGetting the k-Nearest Neighbors 351\u003c\/p\u003e \u003cp\u003eGraph Outlier Detection Method 1: Just Use the Indegree 352\u003c\/p\u003e \u003cp\u003eGraph Outlier Detection Method 2: Getting Nuanced with k-Distance 355\u003c\/p\u003e \u003cp\u003eGraph Outlier Detection Method 3: Local Outlier Factors Are Where It's At 358\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Moving on From Spreadsheets 363\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eGetting Up and Running with R 364\u003c\/p\u003e \u003cp\u003eA Crash Course in R-ing 366\u003c\/p\u003e \u003cp\u003eShow Me the Numbers! Vector Math and Factoring 367\u003c\/p\u003e \u003cp\u003eThe Best Data Type of Them All: the Dataframe 370\u003c\/p\u003e \u003cp\u003eHow to Ask for Help in R 371\u003c\/p\u003e \u003cp\u003eIt Gets Even Better Beyond Base R 372\u003c\/p\u003e \u003cp\u003eDoing Some Actual Data Science 374\u003c\/p\u003e \u003cp\u003eReading Data into R 374\u003c\/p\u003e \u003cp\u003eSpherical K-Means on Wine Data in Just a Few Lines 375\u003c\/p\u003e \u003cp\u003eBuilding AI Models on the Pregnancy Data 381\u003c\/p\u003e \u003cp\u003eForecasting in R 389\u003c\/p\u003e \u003cp\u003eLooking at Outlier Detection 393\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Conclusion 397\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhere Am I? What Just Happened? 397\u003c\/p\u003e \u003cp\u003eBefore You Go-Go 397\u003c\/p\u003e \u003cp\u003eGet to Know the Problem 398\u003c\/p\u003e \u003cp\u003eWe Need More Translators 398\u003c\/p\u003e \u003cp\u003eBeware the Three-Headed Geek-Monster: Tools, Performance, and Mathematical Perfection 399\u003c\/p\u003e \u003cp\u003eYou Are Not the Most Important Function of Your Organization 401\u003c\/p\u003e \u003cp\u003eGet Creative and Keep in Touch! 402\u003c\/p\u003e \u003cp\u003eIndex 403\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49407190401367,"sku":"9781119931386","price":30.39,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119931386.jpg?v=1730498496"},{"product_id":"the-enterprise-big-data-framework-9781398601741","title":"The Enterprise Big Data Framework","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eJan-Willem Middelburg\u003c\/b\u003e is a Dutch entrepreneur and author with a passion for technology and innovation. He is the CEO and co-founder of Cybiant, a global technology that company that helps to create a more sustainable world through analytics, big data and automation. He is also President and Chief Examiner of the Enterprise Big Data Framework, an independent organization dedicated to upskilling individuals with expertise in Big Data. In partnership with APMG-International, the Enterprise Big Data Framework offers vendor-neutral certifications for individuals.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"\u003ci\u003eThe Enterprise Big Data Framework\u003c\/i\u003e is relevant for everybody within an organisation engaged in driving maximum benefits from data. There is something for everybody; from the board considering governance and ethical behaviour to individuals within the organisation knowing where they fit and the value they can get from better use of their organisation's data. If you are considering a transformation project, this is an excellent guide for your project team.\" * Richard Pharro, CEO, The APM Group Limited *\u003cbr\u003e\"If you are looking for a good guide to empower your knowledge on big data and to find a framework to help you on your big data journey, then this book is for you. From learning what big data is to defining a big data strategy, Jan-Willem has built a book to empower the learner on the topic of big data.\" * Jordan Morrow, Chief Strategy \u0026amp; Transformation Officer, DataPrime and Author of Be Data Literate *\u003cbr\u003e\"This book is a master piece for those who are familiar and those who discover the world of data. It provides an \"a la carte framework\" starting with a (big) data strategy and the supporting aspects such as big data functions, architecture and algorithms. It covers in depth data platforms architectures, its management as well as data governance, data catalogue and all the required security considerations associated to the various data classifications. You will find details of data life cycle management, of various machine learning algorithms and an important chapter covering AI ethics when building and deploying sophisticated algorithms using data. The concepts covered in this book apply to on-premises and in the (public) cloud environments, making this book a must read.\" * Jean-Michel Coeur, APAC Technology Practice Lead, Data Analytics, Google Cloud *\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cul\u003e\n\u003cli\u003eSection - ONE: Introduction to Big Data;\u003c\/li\u003e\n\u003cli\u003e\u003cul\u003e\n\u003cli\u003eChapter - 01: Introduction to Big Data;\u003c\/li\u003e\n\u003cli\u003eChapter - 02: The Big Data framework;\u003c\/li\u003e\n\u003cli\u003eChapter - 03: Big Data strategy;\u003c\/li\u003e\n\u003cli\u003eChapter - 04: Big Data architecture;\u003c\/li\u003e\n\u003cli\u003eChapter - 05: Big Data algorithms;\u003c\/li\u003e\n\u003cli\u003eChapter - 06: Big Data processes;\u003c\/li\u003e\n\u003cli\u003eChapter - 07: Big Data functions;\u003c\/li\u003e\n\u003cli\u003eChapter - 08: Artificial intelligence;\u003c\/li\u003e\n\u003c\/ul\u003e\u003c\/li\u003e\n\u003cli\u003eSection - TWO: Enterprise Big Data analysis;\u003c\/li\u003e\n\u003cli\u003e\u003cul\u003e\n\u003cli\u003eChapter - 09: Introduction to Big Data analysis;\u003c\/li\u003e\n\u003cli\u003eChapter - 10: Defining the business objective;\u003c\/li\u003e\n\u003cli\u003eChapter - 11: Data ingestion – importing and reading data sets;\u003c\/li\u003e\n\u003cli\u003eChapter - 12: Data preparation – cleaning and wrangling data;\u003c\/li\u003e\n\u003cli\u003eChapter - 13: Data analysis – model building;\u003c\/li\u003e\n\u003cli\u003eChapter - 14: Data presentation;\u003c\/li\u003e\n\u003c\/ul\u003e\u003c\/li\u003e\n\u003cli\u003eSection - THREE: Enterprise Big Data engineering;\u003c\/li\u003e\n\u003cli\u003e\u003cul\u003e\n\u003cli\u003eChapter - 15: Introduction to Big Data engineering;\u003c\/li\u003e\n\u003cli\u003eChapter - 16: Data modelling;\u003c\/li\u003e\n\u003cli\u003eChapter - 17: Constructing the data lake;\u003c\/li\u003e\n\u003cli\u003eChapter - 18: Building an enterprise Big Data warehouse;\u003c\/li\u003e\n\u003cli\u003eChapter - 19: Design and structure of Big Data pipelines;\u003c\/li\u003e\n\u003cli\u003eChapter - 20: Managing data pipelines;\u003c\/li\u003e\n\u003cli\u003eChapter - 21: Cluster technology;\u003c\/li\u003e\n\u003c\/ul\u003e\u003c\/li\u003e\n\u003cli\u003eSection - FOUR: enterprise Big Data algorithm design;\u003c\/li\u003e\n\u003cli\u003e\u003cul\u003e\n\u003cli\u003eChapter - 22: Introduction to Big Data algorithm design;\u003c\/li\u003e\n\u003cli\u003eChapter - 23: Algorithm design – fundamental concepts;\u003c\/li\u003e\n\u003cli\u003eChapter - 24: Statistical machine learning algorithms;\u003c\/li\u003e\n\u003cli\u003eChapter - 25: The data science roadmap;\u003c\/li\u003e\n\u003cli\u003eChapter - 26: Programming languages 26 visualization and simple metrics;\u003c\/li\u003e\n\u003cli\u003eChapter - 27: Advanced machine learning algorithms;\u003c\/li\u003e\n\u003cli\u003eChapter - 28: Advanced machine learning classification algorithms;\u003c\/li\u003e\n\u003cli\u003eChapter - 29: Technical communication and documentation;\u003c\/li\u003e\n\u003c\/ul\u003e\u003c\/li\u003e\n\u003cli\u003eSection - FIVE: Enterprise Big Data architecture;\u003c\/li\u003e\n\u003cli\u003e\u003cul\u003e\n\u003cli\u003eChapter - 30: Introduction to the Big Data architecture;\u003c\/li\u003e\n\u003cli\u003eChapter - 31: Strength and resilience – the Big Data platform;\u003c\/li\u003e\n\u003cli\u003eChapter - 32: Design principles for Big Data architecture;\u003c\/li\u003e\n\u003cli\u003eChapter - 33: Big Data infrastructure;\u003c\/li\u003e\n\u003cli\u003eChapter - 34: Big Data platforms;\u003c\/li\u003e\n\u003cli\u003eChapter - 35: The Big Data application provider;\u003c\/li\u003e\n\u003cli\u003eChapter - 36: System orchestration in Big Data\u003c\/li\u003e\n\u003c\/ul\u003e\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"Kogan Page Ltd","offers":[{"title":"Default Title","offer_id":49407732121943,"sku":"9781398601741","price":148.5,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781398601741.jpg?v=1730500353"},{"product_id":"learning-to-love-data-science-9781491936580","title":"Learning to Love Data Science","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eToday, big data is taken seriously, and data science is considered downright sexy. With this anthology of reports from award-winning journalist Mike Barlow, you'll appreciate how data science is fundamentally altering our world, for better and for worse.","brand":"O'Reilly Media","offers":[{"title":"Default Title","offer_id":49409190101335,"sku":"9781491936580","price":16.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781491936580.jpg?v=1730505854"},{"product_id":"costeffective-data-pipelines-9781492098645","title":"CostEffective Data Pipelines","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eWith this practical guide, author Sev Leonard provides a holistic approach to designing scalable data pipelines in the cloud. Intermediate data engineers, software developers, and architects will learn how to navigate cost\/performance trade-offs and how to choose and configure compute and storage.","brand":"O'Reilly Media","offers":[{"title":"Default Title","offer_id":49409197474135,"sku":"9781492098645","price":39.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781492098645.jpg?v=1730505889"},{"product_id":"3d-recording-documentation-and-management-of-cultural-heritage-9781849951685","title":"3D Recording, Documentation and Management of","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eDocumentation of our cultural heritage is experiencing an explosion of innovation. New tools have appeared in recent decades including laser scanning, rapid prototyping, high dynamic range spherical and infrared imagery, drone photography, augmented and virtual reality and computer rendering in multiple dimensions. These give us visualisations and data that are at once interesting, intriguing and yet sometimes deceptive. This text provides an objective and integrated approach to the subject, bringing together the techniques of conservation with management, photographic methods, various modelling techniques and the use of unmanned aerial systems. This interdisciplinary approach addresses the need for knowledge about deploying advanced digital technologies and the materials and methods for the assessment, conservation, rehabilitation and maintenance of the sustainability of existing structures and designated historic buildings. Furthermore, this book actively provides the knowhow to facilitate the creation of heritage inventories, assessing risk, and addressing the need for sustainability.In so doing it becomes more feasible to mitigate the threats from inherent and external causes, not only for the built heritage but also for moveable objects and intangible heritage that suffer abandonment and negligence as well as looting and illegal trafficking. The book is written by a team of international experts based upon their practical experience and expertise. It therefore creates a unique book that encapsulates the knowledge of this discipline required by anyone working in this field.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e`...this new publication is a welcome addition, highlighting how these 3D techniques can be utilised...  ...this well-illustrated volume represents a useful contribution for scholars wishing to gain a better understanding of the underpinnings of 3D recording and documentation’. Medieval Archaeology -------------------- `...I found this book very valuable. It can reach an eclectic audience in providing a broad spectrum of the subject. This book is of major importance for Cultural Heritage 3D recording and management and...an important resource handbook’. International Institute for Conservation of Historic and Artistic Works -------------------- '...this new, richly illustrated reference publication on recording and documenting cultural heritage. ... For anyone considering a digital camera for survey purposes ... this chapter [4] is essential reading, and is rightfully one of the best references currently available on the science behind imaging. ...manages to provide what is probably the most up-to-date reference book on 3D recording, documentation and management of cultural heritage. For any heritage professional, academic, student or interested individual considering applying, acquiring, undertaking or researching digital imaging, photogrammetry, Structure-from-Motion, laser scanning, GIS, BIM or RPAS\/UAV within a conservation context, this book should be essential reading before embarking down any one of these rapidly developing technological routes'. Conservation and Management of Archaeological Sites -------------------- '...the images in this book, both in colour and high-resolution, play a critical role along with the text. This is a well produced book that is wonderful to read and view. ...I find this book exceptional for its publishing quality, content and production. It clearly includes cutting-edge knowledge, awareness and experience from many contributors involved in cultural heritage processes around the globe...would be very useful to anyone involved in cultural heritage, documentation of history and site preservation and conservation. It can readily serve as a course text in addition to being a reference text. ... I've nothing but positive things to say about this book - I think you will too'. 3D Visualization World\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eIntroduction - current trends in cultural heritage and documentation; Conservation techniques in cultural heritage; Cultural heritage management tools: The role of GIS and BIM; Basics of photography for cultural heritage imaging; Basics of image-based modelling techniques in cultural heritage 3D recording; Basics of range-based modelling techniques in cultural heritage 3D recording; Cultural heritage documentation with RPAS\/UAV","brand":"Whittles Publishing","offers":[{"title":"Default Title","offer_id":49413914755415,"sku":"9781849951685","price":80.75,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781849951685.jpg?v=1730521830"},{"product_id":"cohesive-subgraph-search-over-large-heterogeneous-information-networks-9783030975678","title":"Cohesive Subgraph Search Over Large Heterogeneous","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis SpringerBrief provides the first systematic review of the existing works of cohesive subgraph search (CSS) over large heterogeneous information networks (HINs). It also covers the research breakthroughs of this area, including models, algorithms and comparison studies in recent years. This SpringerBrief offers a list of promising future research directions of performing CSS over large HINs.\u003c\/p\u003e\u003cp\u003eThe authors first classify the existing works of CSS over HINs according to the classic cohesiveness metrics such as core, truss, clique, connectivity, density, etc., and then extensively review the specific models and their corresponding search solutions in each group. Note that since the bipartite network is a special case of HINs, all the models developed for general HINs can be directly applied to bipartite networks, but the models customized for bipartite networks may not be easily extended for other general HINs due to their restricted settings. The authors also analyze and compare these cohesive subgraph models (CSMs) and solutions systematically. Specifically, the authors compare different groups of CSMs and analyze both their similarities and differences, from multiple perspectives such as cohesiveness constraints, shared properties, and computational efficiency. Then, for the CSMs in each group, the authors further analyze and compare their model properties and high-level algorithm ideas.\u003c\/p\u003e\u003cp\u003eThis SpringerBrief targets researchers, professors, engineers and graduate students, who are working in the areas of graph data management and graph mining. Undergraduate students who are majoring in computer science, databases, data and knowledge engineering, and data science will also want to read this SpringerBrief.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. Introduction2. Preliminaries3. CSS on Bipartite Networks4. CSS on Other General HINs5. Comparison Analysis6. Related Work on CSMs and solutions7. Future Work and Conclusion","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":49415669514583,"sku":"9783030975678","price":37.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783030975678.jpg?v=1730527724"},{"product_id":"automated-taxonomy-discovery-and-exploration-9783031114045","title":"Automated Taxonomy Discovery and Exploration","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book provides a principled data-driven framework that progressively constructs, enriches, and applies taxonomies without leveraging massive human annotated data. Traditionally, people construct domain-specific taxonomies by extensive manual curations, which is time-consuming and costly. In today’s information era, people are inundated with the vast amounts of text data. Despite their usefulness, people haven’t yet exploited the full power of taxonomies due to the heavy curation needed for creating and maintaining them. To bridge this gap, the authors discuss automated taxonomy discovery and exploration, with an emphasis on label-efficient machine learning methods and their real-world usages. Taxonomy organizes entities and concepts in a hierarchy way. It is ubiquitous in our daily life, ranging from product taxonomies used by online retailers, topic taxonomies deployed by news outlets and social media, as well as scientific taxonomies deployed by digital libraries across various domains. When properly analyzed, these taxonomies can play a vital role for science, engineering, business intelligence, policy design, e-commerce, and more. Intuitive examples are used throughout enabling readers to grasp concepts more easily.\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eIntroduction.- Concept Set Expansion.- Taxonomy Construction.- Taxonomy Enrichment.- Taxonomy-Guided Classification.- Conclusions.","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":49415684161879,"sku":"9783031114045","price":44.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783031114045.jpg?v=1730527779"},{"product_id":"business-process-management-20th-international-conference-bpm-2022-munster-germany-september-11-16-2022-proceedings-9783031161025","title":"Business Process Management: 20th International","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book constitutes the refereed proceedings of the 20th International Conference on Business Process Management, BPM 2022, which took place in Münster, Germany, in September 2022. The 22 papers included in this book were carefully reviewed and selected from 98 submissions. They were organized in topical sections as follows: task mining; design methods; process mining; process mining practice; analytics; and systems. The book also includes one keynote talk in full-paper length and 5 tutorial papers. \u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eKeynote.-\u003c\/b\u003e\u003cb\u003e \u003c\/b\u003eAdvancing Business Process Science via the Co-Evolution of Substantive and Methodological Knowledge\u003cb\u003e.\u003c\/b\u003e\u003cb\u003e- Tutorials.- \u003c\/b\u003eBPM in Digital Transformation: New Tools and Productivity Challenges.- Multi-Dimensional Process Analysis.- Theory and Practice - What, With What and How Is Business Process Management Taught at German Universities.-How to Leverage Process Mining in Organizations - Towards Process Mining Capabilities.- Mastering Robotic Process Automation with Process Mining\u003cb\u003e.-\u003c\/b\u003e\u003cb\u003e \u003c\/b\u003e\u003cb\u003eTask Mining\u003c\/b\u003e.- A Reference Data Model for Process-Related User Interaction Logs.-\u003cb\u003e \u003c\/b\u003eAnalysing Variable Human Actions for Robotic Process Automation.- The SWORD is Mightier than the Interview: A Framework for Semi-automatic WORkaround Detection.- Design Methods.- Back to the Roots – Investigating the Theoretical Foundations of Business Process Maturity Models.- Applying Process Mining in Small and Medium sized IT Enterprises – Challenges and Guidelines.- A Process Mining Success Factors Model\u003cb\u003e.- \u003c\/b\u003e\u003cb\u003eProcess Mining\u003c\/b\u003e\u003cb\u003e.- \u003c\/b\u003eNo Time to Dice: Learning Execution Contexts from Event Logs for Resource-Oriented Process Mining.- A Purpose-Guided Log Generation Framework.- Conformance Checking with Uncertainty via SMT\u003cb\u003e.- \u003c\/b\u003e\u003cb\u003eProcess Mining Practice\u003c\/b\u003e.- The Dark Side of Process Mining. How Identifiable Are Users Despite Technologically Anonymized Data? A Case Study From the Health Sector.- Analyzing How Process Mining Reports Answer Time Performance Questions.- Process Mining of Knowledge-Intensive Processes: An Action Design Research Study in Manufacturing.- Process Mining Practices: Evidence from Interviews\u003cb\u003e.-\u003c\/b\u003e\u003cb\u003e \u003c\/b\u003e\u003cb\u003eAnalytics\u003c\/b\u003e.- Measuring Inconsistency in Declarative Process Specifications.- Understanding and Decomposing Control-Flow Loops in Business Process Models.- Reasoning on Labelled Petri Nets and their Dynamics in a Stochastic Setting.- Incentive Alignment through Secure Computations.- Business Process Simulation with Differentiated Resources: Does it Make a Difference.- Uncovering Object-centric Data in Classical Event Logs for the Automated Transformation from XES to OCEL.-\u003cb\u003e \u003c\/b\u003e\u003cb\u003eSystems\u003c\/b\u003e\u003cb\u003e.- \u003c\/b\u003eWhy Companies Use RPA: A Critical Reflection of Goals.- A trustworthy decentralized change propagation mechanism for declarative choreographies.- Architecture of decentralized Process Management Systems.\u003c\/p\u003e","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":49415690420567,"sku":"9783031161025","price":53.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783031161025.jpg?v=1730527803"},{"product_id":"proximity-and-epidata-attributes-and-meaning-modification-9783031170935","title":"Proximity and Epidata: Attributes and Meaning","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book provides a new model to explore discoverability and enhance the meaning of information. The authors have coined the term epidata, which includes items and circumstances that impact the expression of the data in a document, but are not part of the ordinary process of retrieval systems.  Epidata affords pathways and points to details that cast light on proximities that might otherwise go unknown.  In addition, epidata are clues to mis-and dis-information discernment.  There are many ways to find needed information; however, finding the most useable information is not an easy task.  The book explores the uses of proximity and the concept of epidata that increases the probability of  finding functional information.  The authors sketch a constellation of proximities, present examples of attempts to accomplish proximity, and provoke a discussion of the role of proximity in the field. In addition, the authors suggest that proximity is a thread between retrieval constructs based on known topics, predictable relations, and types of information seeking that lie outside constructs such as browsing, stumbling, encountering, detective work, art making, and translation.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eProximity and Clues.- More than Meets the Eye.- Epidata, Clues, Threads, and Webs.- Provocations and Invitations.\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":49415692452183,"sku":"9783031170935","price":33.24,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783031170935.jpg?v=1730527806"},{"product_id":"advances-in-information-retrieval-45th-european-conference-on-information-retrieval-ecir-2023-dublin-ireland-april-2-6-2023-proceedings-part-ii-9783031282379","title":"Advances in Information Retrieval: 45th European","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThe three-volume set LNCS 13980, 13981 and 13982 constitutes the refereed proceedings of the 45th European Conference on IR Research, ECIR 2023, held in Dublin, Ireland, during April 2-6, 2023.\u003c\/p\u003e  \u003cp\u003eThe 65 full papers, 41 short papers, 19 demonstration papers, 12 reproducibility papers consortium papers, 7 tutorial papers, and 10 doctorial consortium papers were carefully reviewed and selected from 489 submissions. The book also contains, 8 workshop summaries and 13 CLEF Lab descriptions. The accepted papers cover the state of the art in information retrieval focusing on user aspects, system and foundational aspects, machine learning, applications, evaluation, new social and technical challenges, and other topics of direct or indirect relevance to search.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eFull Papers.-\u003c\/b\u003e Automatic Summarization of Financial Earnings Calls Transcript.- Parameter-Efficient Sparse Retrievers and Rerankers using Adapters.- Feature Differentiation and Fusion for Semantic Text Matching.- Multivariate Powered Dirichlet-Hawkes Process.- Fragmented Visual Attention in Web Browsing: Weibull Analysis of Item Visit Times.- Topic-Enhanced Personalized Retrieval-based Chatbot.- Improving the Generalizability of the Dense Passage Retriever Using Generated Datasets.- SegmentCodeList: Unsupervised Representation Learning for Human Skeleton Data Retrieval.- Knowing What and How: A Multi-modal Aspect-Based Framework for Complaint Detection.- What is your cause for concern? Towards Interpretable Complaint Cause Analysis.- DeCoDE: DEtection of COgnitive Distortion and Emotion cause extraction in clinical conversations.- Domain-aligned Data Augmentation for Low-resource and Imbalanced Text Classification.- Privacy-Preserving Fair Item Ranking.- Multimodal Geolocation Estimation of News Photos.- Topics in Contextualised Attention Embeddings.- New Metrics to Encourage Innovation and Diversity in Information Retrieval Approaches.- Probing BERT for Ranking Abilities.- Clustering of Bandit with Frequency-Dependent Information Sharing.- Contrastive Graph Learning with Positional Representation for Recommendation.- Domain Adaptation for Anomaly Detection on Heterogeneous Graphs in E-Commerce.- \u003cb\u003eShort Papers\u003c\/b\u003e\u003cp\u003eImproving Neural Topic Models with Wasserstein Knowledge Distillation.- Towards Effective Paraphrasing for Information Disguise.- Generating Topic Pages for Scientific Concepts Using Scientific Publications.- Relevance Judgements for Fair Ranking.- A Study of Term-Topic Embeddings for Ranking.- Topic Refinement in Multi-Level Hate Speech Detection.- Is Cross-modal Information Retrieval Possible without Training?.- Adversarial Adaptation for French Named Entity Recognition.- Exploring Fake News Detection with Heterogeneous Social Media Context Graphs.- Justifying Multi-Label Text Classifications for Healthcare Applications.- Doc2Query–: When Less is More.- Towards Quantifying The Privacy Of Redacted Text. -Detecting Stance of Authorities towards Rumors in Arabic Tweets: A Preliminary Study.- Leveraging Comment Retrieval for Code Summarization.- CPR: Cross-domain Preference Ranking with User Transformation.- Colbert-FairPRF: Towards Fair Pseudo-Relevance Feedback in Dense Retrieval.- C2LIR: Continual Cross-lingual Transfer for Low-Resource Information Retrieval.- Joint Extraction and Classification of Danish Competences for Job Matching.- A Study on FGSM Adversarial Training for Neural Retrieval.- Dialogue-to-Video Retrieval.- Time-dependent next-basket recommendations.- Investigating the Impact of Query Representation on Medical Information Retrieval.- Where a Little Change Makes a Big Difference: A Preliminary Exploration of Children’s Queries.- Multi-document QA with GPT-3 and Neural Reranking .- Towards Detecting Interesting Ideas Expressed in Text.- Towards Linguistically Informed Multi-Objective Transformer Pre-Training for Natural Language Inference.- Dirichlet-Survival Process: Scalable Inference of Topic-Dependent Diffusion Networks.- Consumer Health Question Answering Using Off-the-shelf Components.- MOO-CMDS+NER: Named Entity Recognition-based Extractive Comment-oriented Multi-document Summarization.- Don’t Raise Your Voice, Improve Your Argument: Learning to Retrieve Convincing Arguments.- Learning Query-Space Document Representations for High-Recall Retrieval.- Investigating Conversational Search Behavior For Domain Exploration.- Evaluating Humorous Response Generation to Playful Shopping Requests.- Joint Span Segmentation and Rhetorical Role Labeling with Data Augmentation for Legal Documents.- Trigger or not Trigger: Dynamic Thresholding for Few Shot Event Detection.- The Impact of a Popularity Punishing Hyperparameter on ItemKNN Recommendation Performance.- Neural Ad hoc Retrieval Meets Information Extraction.- Augmenting Graph Convolutional Networks with Textual Data for Recommendations.- Utilising Twitter Metadata for Hate Classification.- Evolution of Filter Bubbles and Polarization in News Recommendation.- Capturing Cross-platform Interaction for Identifying Coordinated Accounts of Misinformation Campaigns.\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":49415704936791,"sku":"9783031282379","price":71.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783031282379.jpg?v=1730527848"},{"product_id":"advances-in-information-retrieval-45th-european-conference-on-information-retrieval-ecir-2023-dublin-ireland-april-2-6-2023-proceedings-part-iii-9783031282409","title":"Advances in Information Retrieval: 45th European","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThe three-volume set LNCS 13980, 13981 and 13982 constitutes the refereed proceedings of the 45th European Conference on IR Research, ECIR 2023, held in Dublin, Ireland, during April 2-6, 2023.\u003c\/p\u003e  \u003cp\u003e\u003cbr\u003e The 65 full papers, 41 short papers, 19 demonstration papers, 12 reproducibility papers consortium papers, 7 tutorial papers, and 10 doctorial consortium papers were carefully reviewed and selected from 489 submissions. The book also contains, 8 workshop summaries and 13 CLEF Lab descriptions. The accepted papers cover the state of the art in information retrieval focusing on user aspects, system and foundational aspects, machine learning, applications, evaluation, new social and technical challenges, and other topics of direct or indirect relevance to search.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eReproducibility Papers\u003c\/b\u003e\u003cb\u003e.- \u003c\/b\u003eKnowledge is Power, Understanding is Impact: Utility and Beyond Goals, Explanation Quality, and Fairness in Path Reasoning Recommendation.- Stat-weight: Improving the Estimator of Interleaved Methods Outcomes with Statistical Hypothesis Testing.- A Reproducibility Study of Question Retrieval for Clarifying Questions.- The Impact of Cross-Lingual Adjustment of Contextual Word Representations on Zero-Shot Transfer.- Scene-centric vs. Object-centric Image-Text Cross-modal Retrieval: A Reproducibility Study.- Index-Based Batch Query Processing Revisited.- A Unified Framework for Learned Sparse Retrieval.- Entity Embeddings for Entity Ranking: A Replicability Study.- Do the Findings of Document and Passage Retrieval Generalize to the Retrieval of Responses for Dialogues?.- PyGaggle: A Gaggle of Resources for Open-Domain Question Answering.- Pre-Processing Matters! Improved Wikipedia Corpora for Open-Domain Question Answering.- From Baseline to Top Performer: A Reproducibility Study of Approaches at the TREC 2021 Conversational Assistance Track.- \u003cb\u003eDemonstration Papers\u003c\/b\u003e.- Exploring Tabular Data Through Networks.- InfEval: Application for Object Detection Analysis.- The System for Efficient Indexing and Search in the Large Archives of Scanned Historical Documents.- Public News Archive: A Searchable Sub-Archive to Portuguese Past News Articles.- TweetStream2Story: Narrative Extraction from Tweets in Real Time.- SimpleRad: patient-friendly Dutch radiology reports.- Automated Extraction of Fine-Grained Standardized Product Information from Unstructured Multilingual Web Data.- Continuous Integration for Reproducible Shared Tasks with TIRA.io.- Dynamic Exploratory Search for the Information Retrieval Anthology.- Text2Storyline: Generating Enriched Storylines From Text.- Uptrendz: API-Centric Real-Time Recommendations in Multi-Domain Settings.- Clustering Without Knowing How To: Application and Evaluation.- Enticing local governments to produce FAIR freedom of information act dossiers.- Which Country is this? Automatic Country Ranking of StreetView Images.- Automatic Videography Generation from Audio Tracks.- Ablesbarkeitsmesser: A System for Assessing the Readability of German Text.- FACADE: Fake Articles Classification And Decision Explanation.- PsyProf: A Platform for Assisted Screening of Depression in Social Media.- SOPalign: A Tool for Automatic Estimation of Compliance with Medical Guidelines.-\u003cb\u003e \u003c\/b\u003e\u003cb\u003eTutorials\u003c\/b\u003e\u003cb\u003e.- \u003c\/b\u003eUnderstanding and Mitigating Gender Bias in Information Retrieval Systems.- Neuro-Symbolic Representations for Information Retrieval.- Legal IR and NLP: the History, Challenges, and State-of-the-Art.- Deep Learning Methods for Query Auto Completion.- Trends and Overview: The Potential of Conversational Agents in Digital Health.- Crowdsourcing for Information Retrieval.- Uncertainty Quantification for Text Classification.- \u003cb\u003eWorkshops\u003c\/b\u003e\u003cb\u003e.-\u003c\/b\u003e Fourth International Workshop on Algorithmic Bias in Search and Recommendation (Bias 2023).- The 6th International Workshop on Narrative Extraction from Texts (Text2Story’23).- 2nd Workshop on Augmented Intelligence in Technology-Assisted Review Systems (ALTARS): Evaluation Metrics and Protocols for eDiscovery and Systematic Review Systems.- Workshop QPP++ 2023: Query Performance Prediction and Ist Evaluation in New Tasks.- Bibliometric-enhanced Information Retrieval: 13th International BIR Workshop (BIR˜2023).- Geographic information extraction from texts (GeoExT).- ROMCIR 2023: Overview of the 3rd Workshop on Reducing Online Misinformation through Credible Information Retrieval.- ECIR 2023 workshop proposal: Legal Information Retrieval.- \u003cb\u003eDoctoral Consoritum\u003c\/b\u003e\u003cb\u003e.- \u003c\/b\u003eBuilding Safe and Reliable AI systems for Safety Critical Tasks with Vision-Language Processing.- Text Information Retrieval in Tetun.- Identifying and Representing Knowledge Delta in Scientific Literature.- Investigation of Bias in Web Search Queries.- Monitoring online discussions and responses to support the identification of misinformation.- User Privacy in Recommender Systems.- Conversational Search for Multimedia Archives.- Disinformation Detection: Knowledge Infusion with Transfer Learning and Visualizations.- A Comprehensive Overview of Consumer Conflicts on Social Media.- Designing useful conversational interfaces for information retrieval in career decision-making support.- CLEF Lab Descriptions\u003c\/p\u003e  \u003cp\u003eiDPP@CLEF 2023: The Intelligent Disease Progression Prediction Challenge.- LongEval: Longitudinal Evaluation of Model Performance at CLEF 2023.- The CLEF-2023 CheckThat! Lab: Checkworthiness, Subjectivity, Political Bias, Factuality, and Authority.- Overview of PAN 2023: Authorship Verification, Multi-Author Writing Style Analysis, Profiling Cryptocurrency Influencers, and Trigger Detection.- Overview of Touché 2023: Argument and Causal Retrieval.- CLEF 2023 SimpleText Track: What Happens if General Users Search Scientific Texts?.- Science for Fun: The CLEF 2023 JOKER Track on Automatic Wordplay Analysis.- ImageCLEF 2023 Highlight: Multimedia Retrieval in Medical, Social Media and Content Recommendation Applications.- LifeCLEF 2023 teaser: Species Identification and Prediction Challenges.- BioASQ at CLEF2023: The eleventh edition of the Large-scale biomedical semantic indexing and question answering challenge.- eRisk 2023: Depression, Pathological Gambling, and Eating Disorder Challenges.- Overview of EXIST 2023: sEXism Identification in Social neTworks.- DocILE 2023 Teaser: Document Information Localization and Extraction.\u003c\/p\u003e","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":49415705002327,"sku":"9783031282409","price":75.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783031282409.jpg?v=1730527848"},{"product_id":"keywords-in-and-out-of-context-9783031325298","title":"Keywords In and Out of Context","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book explores the rich history of the keyword from its earliest manifestations (long before it appeared anywhere in Google Trends or library cataloging textbooks) in order to illustrate its implicit and explicit mediation of human cognition and communication processes. The author covers the concept of the keyword from its deictic origins in primate and proto-speech communities, through its development within oral traditions, to its initial appearances in numerous graphical forms and its workings over time within a variety of indexing traditions and technologies. The book follows the history  all the way to its role in search engine optimization and social media strategies and its potential as an element in the slowly emerging semantic web, as well as in multiple voice search applications. The author synthesizes different perspectives on the significance of this often-invisible intermediary, both in and out of the library and information science context, helping readers to understand how it has come to be so embedded in our daily life.\u003cbr\u003eThis book: \u003cp\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eProvides a thorough history of the keyword, from primate and proto-speech communities to current times\u003c\/li\u003e\n\u003cli\u003eExplains how the concept of the keyword relates to human cognition and communication processes\u003c\/li\u003e\n\u003cli\u003eHighlights the applications of the keyword, both in and out of the library and information science context\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e\u003c\/p\u003e  \u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eChapter 1 -  Representation, Reference, Relevance, and Retention.- Chapter 2 - Signals, Semiotics.- Chapter 3 - Proto-Words, Proto-Signs.- Chapter 4 - Philologies, Philosophies, Pragmatics.- Chapter 5 - Rites, Religions.- Chapter 6 - Writing, Indexing.- Chapter 7 - Progress, Public.- Chapter 8 - Discovery, Retrieval.- Chapter 9 - Databases, Search Engines.","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":49415708901719,"sku":"9783031325298","price":23.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783031325298.jpg?v=1730527861"},{"product_id":"knowledge-discovery-knowledge-engineering-and-knowledge-management-14th-international-joint-conference-ic3k-2022-valletta-malta-october-24-26-2022-revised-selected-papers-9783031434709","title":"Knowledge Discovery, Knowledge Engineering and","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book constitutes the refereed proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2022, held in Valletta, Malta, during October 24–26, 2022.\u003cbr\u003eThe 14 full papers included in this book were carefully reviewed and selected from 127 submissions. They were organized in topical sections as follows: Knowledge Discovery and Information Retrieval; Knowledge Engineering and Ontology Development; and Knowledge Management and Information Systems\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cb\u003e​\u003c\/b\u003e\u003cb\u003eKnowledge Discovery and Information Retrieval\u003c\/b\u003e.- Electrocardiogram Two-Dimensional Motifs: A Study Directed at Cardio Vascular Disease Classification.- Degree Centrality Definition, and Its Computation for Homogeneous Multilayer Networks Using Heuristics-Based Algorithms.- A Dual-Stage Noise Training Scheme for Breast Ultrasound Image  Classification.- A General-Purpose Multi-Stage Multi-Group Machine Learning Framework for Knowledge Discovery and Decision Support.- Comparative Assessment of Deep end-to-end, Deep Hybrid and Deep Ensemble Learning Architectures for Breast Cancer Histological Classification.- \u003cb\u003eKnowledge Engineering and Ontology Development\u003c\/b\u003e.- CIE: A Cloud-Based Information Extraction System for Named Entity Recognition in AWS, Azure, and Medical Domain.- From Natural Language Texts to RDF Triples: A Novel Approach to Generating e-Commerce Knowledge Graphs.- Situational Question Answering over Commonsense Knowledge Using Memory Nets.- Archives Metadata Text Information Extraction into CIDOC-CRM.- Evolution of Computational Ontologies: Assessing Development Processes Using Metrics.- System to Correct Toxic Expression with BERT and to Determine the Effect of the Attention Value.- \u003cb\u003eKnowledge Management and Information Systems\u003c\/b\u003e.- Machine Learning Decision Support for Production Planning and Control Based on Simulation-Generated Data..- FAIRification of CRIS: A Review.- Measuring Augmented Reality and Virtual Reality Trajectory in the Training Environment.- DroNit Project: Improving Drone Usage for Civil Defense Applications.- Innovation Processes and Information Technologies: A Study of Boutique Hotels in Valletta, Malta. \u003cp\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e  ","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":49415722139991,"sku":"9783031434709","price":61.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783031434709.jpg?v=1730527898"},{"product_id":"data-centers-edges-of-a-wired-nation-9783037786451","title":"Data Centers: Edges of a Wired Nation","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eQuestions of privacy, borders, and nationhood are increasingly shaping the way we think about all things digital. Data Centers brings together essays and photographic documentation that analyze recent and ongoing developments. Taking Switzerland as an example, the book takes a look at the country's data centers, law firms, corporations, and government institutions that are involved in the creation, maintenance, and regulation of digital infrastructures. Beneath the official storyline— Switzerland’s moderate climate, political stability, and relatively clean energy mix—the book uncovers a much more varied and sometimes contradictory set of narratives.","brand":"Lars Muller Publishers","offers":[{"title":"Default Title","offer_id":49415824179543,"sku":"9783037786451","price":24.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783037786451.jpg?v=1730528201"},{"product_id":"new-horizons-for-a-data-driven-economy-a-roadmap-for-usage-and-exploitation-of-big-data-in-europe-9783319215686","title":"New Horizons for a Data-Driven Economy: A Roadmap","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eIn this book readers will find technological discussions on the existing and emerging technologies across the different stages of the big data value chain. They will learn about legal aspects of big data, the social impact, and about education needs and requirements. And they will discover the business perspective and how big data technology can be exploited to deliver value within different sectors of the economy. \u003c\/p\u003e\u003cp\u003eThe book is structured in four parts: Part I “The Big Data Opportunity” explores the value potential of big data with a particular focus on the European context. It also describes the legal, business and social dimensions that need to be addressed, and briefly introduces the European Commission’s BIG project. Part II “The Big Data Value Chain” details the complete big data lifecycle from a technical point of view, ranging from data acquisition, analysis, curation and storage, to data usage and exploitation. Next, Part III “Usage and Exploitation of Big Data” illustrates the value creation possibilities of big data applications in various sectors, including industry, healthcare, finance, energy, media and public services. Finally, Part IV “A Roadmap for Big Data Research” identifies and prioritizes the cross-sectorial requirements for big data research, and outlines the most urgent and challenging technological, economic, political and societal issues for big data in Europe.\u003c\/p\u003e\u003cp\u003eThis compendium summarizes more than two years of work performed by a leading group of major European research centers and industries in the context of the BIG project. It brings together research findings, forecasts and estimates related to this challenging technological context that is becoming the major axis of the new digitally transformed business environment.                                                           \u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e“The book provides rich information on the different processes involved in big data value chain and explains each process with case studies in diverse industrial sectors. … the book can help academic researchers, undergraduate students, and graduate students because it contains information about big data and its recent development and generates some research ideas. This book can also facilitate government officials and executives of different organizations to consider future roadmap by taking advantage of big data.” (Sunny Sun and Rob Law, Information Technology \u0026amp; Tourism, Vol. 17, 2017)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e                                           ","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":49417087713623,"sku":"9783319215686","price":33.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783319215686.jpg?v=1730531593"},{"product_id":"metadata-shaping-knowledge-from-antiquity-to-the-semantic-web-9783319408910","title":"Metadata: Shaping Knowledge from Antiquity to the","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book offers a comprehensive guide to the world of metadata, from its origins in the ancient cities of the Middle East, to the Semantic Web of today.  \u003cp\u003eThe author takes us on a journey through the centuries-old history of metadata up to the modern world of crowdsourcing and Google, showing how metadata works and what it is made of. The author explores how it has been used ideologically and how it can never be objective. He argues how central it is to human cultures and the way they develop.\u003c\/p\u003e    \u003cp\u003e\u003ci\u003eMetadata: Shaping Knowledge from Antiquity to the Semantic Web\u003c\/i\u003e is for all readers with an interest in how we humans organize our knowledge and why this is important. It is suitable for those new to the subject as well as those know its basics. It also makes an excellent introduction for students of information science and librarianship.\u003c\/p\u003e  \u003cp\u003e \u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e“In Metadata: Shaping Knowledge from Antiquity to the Semantic Web, Gartner, the digital librarian at the Warburg Institute at the University of London, thoroughly covers not only the history of metadata, but how it affects and forms knowledge and culture. … The author concludes the work with recent advances in metadata creation—metadata produced via Web 2.0, crowdsourcing, and folksonomies. This is a meticulous overview of metadata and its history and application. Summing Up: Recommended. Graduate students, faculty, and professionals.” (A. Hollister, Choice, Vol. 54 (9), May, 2017)\u003cp\u003e“The book presents an enjoyable bird’s-eye view of metadata and related concepts, with outstanding examples accessible to non-experts. … I highly recommend the book.” (H. I. Kilov, Computing Reviews, May, 2017)\u003c\/p\u003e\u003cp\u003e“The book covers continuous evolution of metadata from the history of cataloguing to the modern forms … . This book will attract readers interested in metadata, the semantic web, metadata ontologies, digital libraries, and semantic retrieval. So, it is highly recommended to information professionals, digital librarians and students. The book is well structured and motivating. The results of Gartner’s effort are very much worth reading due to his librarianship perspective on metadata.” (Elaheh Hossseini, Information Research, informationr.net, Vol. 22 (1), March, 2017)\u003c\/p\u003e\u003cp\u003e“All of the usual elements of a book on metadata are present and correct … I would urge you to read this book if you are new to cataloguing or if you are old in cataloguing and world-weary about our professional mission. It’s a book to provoke your own thoughts, and quite possibly to give to your manager if you suspect they are unsure why metadata, why cataloguing and, therefore, why you and your team matter.” (Anne Welsh, Catalogue and Index, cilip.org.uk, Issue 186, March, 2017) \u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e“The book provides an overview of existing metadata approaches and standards such as MARC, Dublin Core, MIX and EAD. This book also offers a succinct history of metadata and discusses emerging metadata approaches. … This book can be read by both technical and non-technical people as it uses a rather accessible language. Metadata makes information finding easier. This is an excellent read and I highly recommend it to my colleagues and friends.” (Getaneh Alemu, Linkedin.com, January, 2017)\u003c\/p\u003e\u003cp\u003e“This slim volume aims to provide the reader with an overview of the history and development of metadata from the earliest times to the present day, and it offers a straightforward and readable account of metadata for the novice or the non-professional. … There is plenty here to intrigue and entertain for those wanting a lightweight introduction to the subject, at a very attractive price … .” (Vanda Broughton, Library \u0026amp; Information History, Vol. 33 (2), 2017)\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eWhat Metadata is and why it Matters.- Clay, Goats and Trees: Metadata before the Byte.- Metadata Becomes Digital.- Metadata as Ideology.- The Ontology of Metadata.- The Taxonomic Urge.- From Hierarchies to Networks.- Breaking the Silos.- Democratizing Metadata.- Knowledge and Uncertainty.","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":49417090466135,"sku":"9783319408910","price":49.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783319408910.jpg?v=1730531599"},{"product_id":"data-warehouse-systeme-fur-dummies-9783527714476","title":"Data-Warehouse-Systeme für Dummies","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eJede Business-Intelligence-Anwendung beruht letzten Endes auf einem Data Warehouse. Data Warehousing ist deshalb ein sehr wichtiges Gebiet der Angewandten Informatik, insbesondere im Zeitalter von Big Data. Das vorliegende Buch beleuchtet das Data Warehouse aus zwei Perspektiven: der des Entwicklers und der des Anwenders. Der zukünftige Entwickler lernt, ein Data Warehouse mit geeigneten Methoden selbst zu entwickeln. Für den zukünftigen Anwender geht der Autor auf die Themen Reporting, Online Analytical Processing und Data Mining ein. Das Lehrbuch ist auch zum Selbststudium geeignet. Kenntnisse über Datenbanksysteme sollten allerdings vorhanden sein.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"Das didaktisch gut aufgebaute Buch auf dem aktuellen Stand der Technik endet mit 10 Übungsaufgaben (mit Lösungen) und ist auch zum Selbststudium gut geeignet.\"\u003cbr\u003e (EKZ im Dezember 2018)\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eEinleitung 19\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eÜber dieses Buch 19\u003c\/p\u003e \u003cp\u003eKonventionen in diesem Buch 20\u003c\/p\u003e \u003cp\u003eWas Sie nicht lesen müssen 20\u003c\/p\u003e \u003cp\u003eTörichte Annahmen über den Leser 21\u003c\/p\u003e \u003cp\u003eWie dieses Buch aufgebaut ist 21\u003c\/p\u003e \u003cp\u003eTeil I: Was ist ein Data Warehouse? 21\u003c\/p\u003e \u003cp\u003eTeil II: Architektur eines Data-Warehouse-Systems 21\u003c\/p\u003e \u003cp\u003eTeil III: Anwendungsbereiche für ein Data Warehouse 22\u003c\/p\u003e \u003cp\u003eTeil IV: Modellierung eines Data-Warehouse-Systems 22\u003c\/p\u003e \u003cp\u003eTeil V: Zugriff auf ein Data Warehouse 22\u003c\/p\u003e \u003cp\u003eTeil VI: Speicherung und Optimierung auf Datenbankebene 22\u003c\/p\u003e \u003cp\u003eTeil VII: Der Top-10-Teil 22\u003c\/p\u003e \u003cp\u003eSymbole, die in diesem Buch verwendet werden23\u003c\/p\u003e \u003cp\u003eWie es weitergeht 23\u003c\/p\u003e \u003cp\u003e\u003cb\u003eTEIL I WAS IST EIN DATA WAREHOUSE? 25\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eKapitel 1 Ein Beispiel zur Einführung 27\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDaten und ihre Verarbeitung 27\u003c\/p\u003e \u003cp\u003eDaten und Datenbanken 27\u003c\/p\u003e \u003cp\u003eDie Verarbeitung von Daten 28\u003c\/p\u003e \u003cp\u003eAnalyse von Absatzmengen und Planzahlen als Beispiel 29\u003c\/p\u003e \u003cp\u003eBesonderheiten analytischer Aufgabenstellungen 31\u003c\/p\u003e \u003cp\u003eWenn personenbezogene Daten ins Spiel kommen 34\u003c\/p\u003e \u003cp\u003e\u003cb\u003eKapitel 2 Das Data Warehouse im Umfeld der betrieblichen Informationssysteme 35\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eHierarchie betrieblicher Informationssysteme 35\u003c\/p\u003e \u003cp\u003eZusammenfassung: Analytische Informationssysteme 38\u003c\/p\u003e \u003cp\u003eBeispiele für analytische Informationssysteme 39\u003c\/p\u003e \u003cp\u003eBeispiel 1: Analytische Informationssysteme im CRM 39\u003c\/p\u003e \u003cp\u003eBeispiel 2: Kennzahlen-Analysesysteme im Rechnungswesen 41\u003c\/p\u003e \u003cp\u003eBeispiel 3: Website-Analysesysteme 43\u003c\/p\u003e \u003cp\u003eFazit: Data Warehouse und analytische Informationssysteme 45\u003c\/p\u003e \u003cp\u003e\u003cb\u003eKapitel 3 Definition und Abgrenzung des Begriffs »Data Warehouse« 47\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDie 3-Schichten-Architektur analytischer Informationssysteme 47\u003c\/p\u003e \u003cp\u003eDefinitionen des Begriffs Data Warehouse 50\u003c\/p\u003e \u003cp\u003eDefinition von Inmon 50\u003c\/p\u003e \u003cp\u003eDefinition von Kimball 52\u003c\/p\u003e \u003cp\u003eVergleich der beiden Definitionen 53\u003c\/p\u003e \u003cp\u003eAnwendungsfall: Das Data Warehouse und Business Intelligence 54\u003c\/p\u003e \u003cp\u003e\u003cb\u003eTEIL II ARCHITEKTUR EINES DATA-WAREHOUSE-SYSTEMS 57\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eKapitel 4 Überblick über die Architektur eines Data-Warehouse-Systems 59\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDie Phasen des Data Warehousing 59\u003c\/p\u003e \u003cp\u003eEin allgemeines Data-Warehouse-Architekturmodell 61\u003c\/p\u003e \u003cp\u003eVorgehensweisen bei der Erstellung eines Data Warehouse 64\u003c\/p\u003e \u003cp\u003eProjektdefinition und Machbarkeitsstudie 65\u003c\/p\u003e \u003cp\u003eAnalyse, Entwurf und Einführung für einen Anwendungsbereich 66\u003c\/p\u003e \u003cp\u003e\u003cb\u003eKapitel 5 Der ETL-Prozess 69\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eÜberblick 69\u003c\/p\u003e \u003cp\u003eEin einführendes Beispiel 70\u003c\/p\u003e \u003cp\u003eExtraktion 71\u003c\/p\u003e \u003cp\u003eDas Pull-Prinzip 71\u003c\/p\u003e \u003cp\u003eDas Push-Prinzip 72\u003c\/p\u003e \u003cp\u003eBeispiele 72\u003c\/p\u003e \u003cp\u003eTransformation 77\u003c\/p\u003e \u003cp\u003eDatenbestandsanalyse 77\u003c\/p\u003e \u003cp\u003eDatenbereinigung 78\u003c\/p\u003e \u003cp\u003eDatenintegration 80\u003c\/p\u003e \u003cp\u003eLaden 82\u003c\/p\u003e \u003cp\u003e\u003cb\u003eKapitel 6 Die Basisdatenbank 85\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eMerkmale der Basisdatenbank 85\u003c\/p\u003e \u003cp\u003eUnterschied zwischen operativen Datenbanken und der Basisdatenbank 87\u003c\/p\u003e \u003cp\u003eDie operativen Quellsysteme des Beispiels 88\u003c\/p\u003e \u003cp\u003eDie Basisdatenbank des Beispiels 89\u003c\/p\u003e \u003cp\u003e\u003cb\u003eKapitel 7 Das Analyse-Subsystem 93\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDimensionen und Fakten 93\u003c\/p\u003e \u003cp\u003eDimension oder Metrik? 95\u003c\/p\u003e \u003cp\u003eMetriken als Dimension 96\u003c\/p\u003e \u003cp\u003eDimensionen als Metrik 97\u003c\/p\u003e \u003cp\u003eKlassifizierung von Dimensionen 98\u003c\/p\u003e \u003cp\u003eFachliche Dimensionen 98\u003c\/p\u003e \u003cp\u003eKategorische Dimensionen 98\u003c\/p\u003e \u003cp\u003eStrukturelle Dimensionen 99\u003c\/p\u003e \u003cp\u003eHierarchien von Dimensionswerten 99\u003c\/p\u003e \u003cp\u003eParallele Hierarchien 100\u003c\/p\u003e \u003cp\u003eUnausgeglichene Hierarchiebäume 101\u003c\/p\u003e \u003cp\u003eStrukturänderungen in Hierarchien 102\u003c\/p\u003e \u003cp\u003eSlowly Changing Dimensions 102\u003c\/p\u003e \u003cp\u003eTyp 1: Überschreiben 103\u003c\/p\u003e \u003cp\u003eTyp 2: Neue Zeile 104\u003c\/p\u003e \u003cp\u003eTyp 3: Spalten mit altem und neuem Wert 105\u003c\/p\u003e \u003cp\u003eTyp 4: Mini-Dimension 105\u003c\/p\u003e \u003cp\u003eZusammenfassung 106\u003c\/p\u003e \u003cp\u003eVerknüpfung von Dimensionen über Metriken 106\u003c\/p\u003e \u003cp\u003eAggregationstypen von Fakten 107\u003c\/p\u003e \u003cp\u003eDie Themen Datenqualität und Datenschutz 108\u003c\/p\u003e \u003cp\u003eDatenqualität 108\u003c\/p\u003e \u003cp\u003eDatenschutz 109\u003c\/p\u003e \u003cp\u003eArchitekturvarianten für ein Analyse-Subsystem 109\u003c\/p\u003e \u003cp\u003eMöglichkeiten für die Architektur 110\u003c\/p\u003e \u003cp\u003eDie Hub-and-Spoke-Architektur 111\u003c\/p\u003e \u003cp\u003eAuswertungen und Analysen 112\u003c\/p\u003e \u003cp\u003e\u003cb\u003eKapitel 8 Metadaten 113\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWas sind Metadaten?113\u003c\/p\u003e \u003cp\u003eMetadaten im Data-Warehouse-Kontext 114\u003c\/p\u003e \u003cp\u003eDas Metadaten-Management in einem Data-Warehouse-System 114\u003c\/p\u003e \u003cp\u003eStandards für Data-Warehouse-Metadaten 118\u003c\/p\u003e \u003cp\u003eEin kleines Beispiel 119\u003c\/p\u003e \u003cp\u003e\u003cb\u003eTEIL III ANWENDUNGSBEREICHE FÜR EIN DATA WAREHOUSE 121\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eKapitel 9 Reporting 123\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDas Berichtswesen eines Unternehmens 123\u003c\/p\u003e \u003cp\u003eÜberblick und Definition 123\u003c\/p\u003e \u003cp\u003eErzeugung und Verteilung von Reports 125\u003c\/p\u003e \u003cp\u003eArten von Berichtssystemen 125\u003c\/p\u003e \u003cp\u003eWas sich Anwender vom Reporting wünschen und wie die Wirklichkeit oft aussieht 126\u003c\/p\u003e \u003cp\u003eEinige Tipps für die Report-Gestaltung 127\u003c\/p\u003e \u003cp\u003eGraphische Darstellungen im Report 128\u003c\/p\u003e \u003cp\u003eDie Hichert-Success-Regeln 131\u003c\/p\u003e \u003cp\u003eGrundformen für Reports 132\u003c\/p\u003e \u003cp\u003eIst-Ist-Vergleiche 132\u003c\/p\u003e \u003cp\u003ePlan-Ist-Vergleiche 133\u003c\/p\u003e \u003cp\u003ePlan-Wird-Vergleiche 134\u003c\/p\u003e \u003cp\u003eBerücksichtigung dynamischer Dimensionsstrukturen 135\u003c\/p\u003e \u003cp\u003eReport as-is 136\u003c\/p\u003e \u003cp\u003eReport as-of 136\u003c\/p\u003e \u003cp\u003eReport as-posted 137\u003c\/p\u003e \u003cp\u003eEin praktisches Beispiel 137\u003c\/p\u003e \u003cp\u003e\u003cb\u003eKapitel 10 Online Analytical Processing 139\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eMotivation und Definition 139\u003c\/p\u003e \u003cp\u003eCharakteristika von OLAP 141\u003c\/p\u003e \u003cp\u003eAbgrenzung OLAP und OLTP 141\u003c\/p\u003e \u003cp\u003eDie Coddschen Regeln 142\u003c\/p\u003e \u003cp\u003eFASMI 143\u003c\/p\u003e \u003cp\u003eSpezielle OLAP-Operatoren 144\u003c\/p\u003e \u003cp\u003ePivotierung bzwRotation 144\u003c\/p\u003e \u003cp\u003eRoll-up und Drill-down 145\u003c\/p\u003e \u003cp\u003eSlice und Dice 146\u003c\/p\u003e \u003cp\u003eBeispiel 148\u003c\/p\u003e \u003cp\u003e\u003cb\u003eKapitel 11 Data Mining151\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eEinführung 151\u003c\/p\u003e \u003cp\u003eCRISP-DM 153\u003c\/p\u003e \u003cp\u003eMethoden und Verfahren beim Data Mining 154\u003c\/p\u003e \u003cp\u003eAssoziationsanalyse 155\u003c\/p\u003e \u003cp\u003eClusteranalyse 160\u003c\/p\u003e \u003cp\u003eKlassifikation mit der Diskriminanzanalyse 164\u003c\/p\u003e \u003cp\u003eEntscheidungsbaumverfahren 166\u003c\/p\u003e \u003cp\u003eSpezielle Data-Mining-Fragestellungen im Kontext von Data-Warehouse-Daten 171\u003c\/p\u003e \u003cp\u003eWelche Artikel werden gemeinsam gekauft? 172\u003c\/p\u003e \u003cp\u003eUnterscheiden sich gute, normale und schlechte Kunden? 172\u003c\/p\u003e \u003cp\u003eWelche Kunden besitzen eine bestimmte Produktaffinität? 173\u003c\/p\u003e \u003cp\u003ePraxisbeispiel »Predictive Analytics« 174\u003c\/p\u003e \u003cp\u003eKollaboratives Filtern 175\u003c\/p\u003e \u003cp\u003e\u003cb\u003eTEIL IV MODELLIERUNG EINES DATA-WAREHOUSE-SYSTEMS 177\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eKapitel 12 Data Vault 179\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eEinführung 179\u003c\/p\u003e \u003cp\u003eHubs, Satelliten und Links 180\u003c\/p\u003e \u003cp\u003eHubs 180\u003c\/p\u003e \u003cp\u003eLinks 182\u003c\/p\u003e \u003cp\u003eSatelliten183\u003c\/p\u003e \u003cp\u003eBeispiel 185\u003c\/p\u003e \u003cp\u003e\u003cb\u003eKapitel 13 Semantischer Entwurf eines Data Warehouse 191\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eZur Wiederholung: das Entity-Relationship-Modell 191\u003c\/p\u003e \u003cp\u003eDrei Schritte bei der Modellierung einer Datenbank 192\u003c\/p\u003e \u003cp\u003eDas ER-Modell: Entitätstypen, Attribute und Beziehungen 192\u003c\/p\u003e \u003cp\u003eDas multidimensionale ER-Modell 194\u003c\/p\u003e \u003cp\u003eADAPT 196\u003c\/p\u003e \u003cp\u003e\u003cb\u003eKapitel 14 Relationale Modellierung der Datenwürfel 199\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eEinführung 199\u003c\/p\u003e \u003cp\u003eDas Star-Schema 200\u003c\/p\u003e \u003cp\u003eBeispiel 201\u003c\/p\u003e \u003cp\u003eBesondere Merkmale des Star-Schemas 204\u003c\/p\u003e \u003cp\u003eDas Snowflake-Schema 207\u003c\/p\u003e \u003cp\u003eVergleich von Star- und Snowflake-Schema 209\u003c\/p\u003e \u003cp\u003eDas Galaxy-Schema 211\u003c\/p\u003e \u003cp\u003e\u003cb\u003eTEIL V ZUGRIFF AUF EIN DATA WAREHOUSE 213\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eKapitel 15 Multidimensionale Abfragen mit SQL 215\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eZugriff auf ein Data Warehouse mit SQL 215\u003c\/p\u003e \u003cp\u003eErzeugen der Tabellen 216\u003c\/p\u003e \u003cp\u003eTypische analytische Fragestellungen 218\u003c\/p\u003e \u003cp\u003eOLAP-Erweiterungen von SQL 220\u003c\/p\u003e \u003cp\u003eDie WINDOW-Klausel 220\u003c\/p\u003e \u003cp\u003eErweiterungen der GROUP-BY-Option 225\u003c\/p\u003e \u003cp\u003eStatistische Funktionen 228\u003c\/p\u003e \u003cp\u003e\u003cb\u003eKapitel 16 Die Abfragesprache MDX 229\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eEinführung 229\u003c\/p\u003e \u003cp\u003eSpezielle OLAP-Operatoren und Funktionen 233\u003c\/p\u003e \u003cp\u003eTupel und Sets 233\u003c\/p\u003e \u003cp\u003eMember und Children 234\u003c\/p\u003e \u003cp\u003eKreuzprodukt mittels Crossjoin 234\u003c\/p\u003e \u003cp\u003eDer WITH-Operator 235\u003c\/p\u003e \u003cp\u003eHäufige Fragestellungen 236\u003c\/p\u003e \u003cp\u003e\u003cb\u003eKapitel 17 Zusammenspiel von MDX und SQL 239\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eOLAP-Server 239\u003c\/p\u003e \u003cp\u003eDer OLAP-Server Mondrian 241\u003c\/p\u003e \u003cp\u003eMDX-Schema von Mondrian 241\u003c\/p\u003e \u003cp\u003eMondrian-Frontend-Tools 245\u003c\/p\u003e \u003cp\u003e\u003cb\u003eTEIL VI SPEICHERUNG UND OPTIMIERUNG AUF DATENBANKEBENE 247\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eKapitel 18 ROLAP, MOLAP und anderes 249\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eROLAP und MOLAP 249\u003c\/p\u003e \u003cp\u003eSpaltenorientierte und In-Memory-Speicherung 252\u003c\/p\u003e \u003cp\u003eNoSQL-Datenbanksysteme 255\u003c\/p\u003e \u003cp\u003eTypen von NoSQL-Systemen 255\u003c\/p\u003e \u003cp\u003eNoSQL-Datenbanken bei einem Data Warehouse 258\u003c\/p\u003e \u003cp\u003eBeurteilung 263\u003c\/p\u003e \u003cp\u003e\u003cb\u003eKapitel 19 Optimierungsmöglichkeiten bei relationalen Datenbanken 265\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eEinführung 265\u003c\/p\u003e \u003cp\u003ePartitionierung 266\u003c\/p\u003e \u003cp\u003ePartition by List 267\u003c\/p\u003e \u003cp\u003ePartition by Range 268\u003c\/p\u003e \u003cp\u003ePartition by Hash 268\u003c\/p\u003e \u003cp\u003ePartition by Reference 269\u003c\/p\u003e \u003cp\u003eMaterialized Views 270\u003c\/p\u003e \u003cp\u003eKlassische Views vsMaterialized Views 270\u003c\/p\u003e \u003cp\u003eMaterialized Views bei einem Data Warehouse 273\u003c\/p\u003e \u003cp\u003eIndizierung 274\u003c\/p\u003e \u003cp\u003eKlassischer Index 274\u003c\/p\u003e \u003cp\u003eBitmap-Index 275\u003c\/p\u003e \u003cp\u003eMehrdimensionale Indizes 276\u003c\/p\u003e \u003cp\u003e\u003cb\u003eTEIL VII DER TOP-10-TEIL 279\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eKapitel 20 10 Schritte auf dem Weg zu Ihrem ersten Dashboard 281\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUnd so wird es gemacht 282\u003c\/p\u003e \u003cp\u003eFestlegung der Datenquellen 282\u003c\/p\u003e \u003cp\u003eVorbereitung der Daten 283\u003c\/p\u003e \u003cp\u003eErstellung eines Dashboards 285\u003c\/p\u003e \u003cp\u003eDaten aus mehreren Quellen 287\u003c\/p\u003e \u003cp\u003eIntegration von Landkarten 288\u003c\/p\u003e \u003cp\u003e\u003cb\u003eKapitel 21 10 Schritte, die helfen, die richtige Data-Warehouse-Software zu finden 291\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eMarktanalyse für BI-Software 291\u003c\/p\u003e \u003cp\u003eDefinition der eigenen Anforderungen 292\u003c\/p\u003e \u003cp\u003eEinbindung des Managements, Projektplan 293\u003c\/p\u003e \u003cp\u003eMarktanalyse der infrage kommenden BI-Anbieter 293\u003c\/p\u003e \u003cp\u003eEinholung von Angeboten 293\u003c\/p\u003e \u003cp\u003eDurchführung von Testinstallationen 294\u003c\/p\u003e \u003cp\u003eBewertung der Systeme 294\u003c\/p\u003e \u003cp\u003eErmittlung der Kosten 295\u003c\/p\u003e \u003cp\u003eEinholung von Referenzen, Anbieterqualifikation 296\u003c\/p\u003e \u003cp\u003eÜberprüfung der Lizenzvereinbarung 296\u003c\/p\u003e \u003cp\u003e\u003cb\u003eKapitel 22 10 Übungsaufgaben zur Wiederholung 297\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAufgaben 297\u003c\/p\u003e \u003cp\u003eAufgabe 1: Assoziationsanalyse 297\u003c\/p\u003e \u003cp\u003eAufgabe 2: Diskriminanzanalyse 297\u003c\/p\u003e \u003cp\u003eAufgabe 3: Data Vault 298\u003c\/p\u003e \u003cp\u003eAufgabe 4: ADAPT 298\u003c\/p\u003e \u003cp\u003eAufgabe 5: MDX299\u003c\/p\u003e \u003cp\u003eAufgabe 6: Star-Schema 299\u003c\/p\u003e \u003cp\u003eAufgabe 7: OLAP mit SQL 299\u003c\/p\u003e \u003cp\u003eAufgabe 8: Snowflake-Schema 300\u003c\/p\u003e \u003cp\u003eAufgabe 9: Optimierung 300\u003c\/p\u003e \u003cp\u003eAufgabe 10: Multidimensionale Datenbank301\u003c\/p\u003e \u003cp\u003eLösungen 301\u003c\/p\u003e \u003cp\u003eLösung von Aufgabe 1 301\u003c\/p\u003e \u003cp\u003eLösung von Aufgabe 2 303\u003c\/p\u003e \u003cp\u003eLösung von Aufgabe 3 303\u003c\/p\u003e \u003cp\u003eLösung von Aufgabe 4 304\u003c\/p\u003e \u003cp\u003eLösung von Aufgabe 5 304\u003c\/p\u003e \u003cp\u003eLösung von Aufgabe 6 306\u003c\/p\u003e \u003cp\u003eLösung von Aufgabe 7 307\u003c\/p\u003e \u003cp\u003eLösung von Aufgabe 8 309\u003c\/p\u003e \u003cp\u003eLösung von Aufgabe 9 309\u003c\/p\u003e \u003cp\u003eLösung von Aufgabe 10 311\u003c\/p\u003e \u003cp\u003eLiteraturverzeichnis 313\u003c\/p\u003e \u003cp\u003eStichwortverzeichnis 317\u003c\/p\u003e","brand":"Wiley-VCH Verlag GmbH","offers":[{"title":"Default Title","offer_id":49419469226327,"sku":"9783527714476","price":999.99,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783527714476.jpg?v=1730538412"},{"product_id":"begriffliche-wissensverarbeitung-methoden-und-anwendungen-9783540663911","title":"Begriffliche Wissensverarbeitung: Methoden und","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eDieses Buch stellt Methoden der Begrifflichen Wissensverarbeitung vor und präsentiert Anwendungen aus unterschiedlichen Praxisfeldern. Im Methodenteil wird in moderne Techniken der Begrifflichen Datenanalyse und Wissensverarbeitung eingeführt. Hierbei werden die mathematischen Grundlagen abgehandelt und durch zahlreiche Beispiele anschaulich gemacht. Der zweite Teil des Buches richtet sich verstärkt an potentielle Anwender. An ausgewählten Anwendungen wird die Vorgehensweise bei der Datenanalyse und dem Information Retrieval mit den Methoden der Begrifflichen Wissensverarbeitung vorgestellt und ihr Potential aufgezeigt.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eI: Methoden der Begrifflichen Wissensverarbeitung.- Begriffe und Implikationen.- ConImp - Ein Programm zur Formalen Begriffsanalyse.- Ähnlichkeit als Distanz in Begriffsverbänden.- Datenanalyse mit Fuzzy-Begriffen.- Terminologische Merkmalslogik in der Formalen Begriffsanalyse.- II: Anwendungen der Begrifflichen Wissensverarbeitung.- Formale Begriffsanalyse im Software Engineering.- Zugriffskontrolle bei Programmsystemen und im Datenschutz mittels Formaler Begriffsanalyse.- Inhaltliche Erschließung des Bereichs 'Sozialorientierte Gestaltung von Informationstechnik' - Ein begriffsanalytischer Ansatz.- Wissensdarstellungen in Informationssystemen, Fragetypen und Anforderungen an Retrievalkomponenten.- Ein TOSCANA-Erkundungssystem zur Literatursuche.- Ein Erkundungssystem zum Baurecht: Methoden der Entwicklung eines TOSCANA-Systems.- Begriffliche Erkundung semantischer Strukturen von Sprechaktverben.- Grundwerte, Ziele und Maßnahmen in einem regionalen Krankenhaus - Eine Anwendung des Verfahrens GABEK.- Normen- und regelgeleitete internationale Kooperationen - Formale Begriffsanalyse in der Politikwissenschaft.- Entwicklung eines kontextuellen Methodenkonzeptes mit Hilfe der Formalen Begriffsanalyse an Beispielen zum Risikoverständnis.- Über Möglichkeiten der Formalen Begriffsanalyse in der Mathematischen Archäochemie.","brand":"Springer-Verlag Berlin and Heidelberg GmbH \u0026 Co. KG","offers":[{"title":"Default Title","offer_id":49419504353623,"sku":"9783540663911","price":59.99,"currency_code":"GBP","in_stock":true}]},{"product_id":"advances-in-intelligent-data-analysis-third-international-symposium-ida-99-amsterdam-the-netherlands-august-9-11-1999-proceedings-9783540663324","title":"Advances in Intelligent Data Analysis: Third","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eFormanyyearstheintersectionofcomputing anddataanalysiscontainedme- based statistics packages and not much else. Recently, statisticians have - braced computing, computer scientists have started using statistical theories and methods, and researchers in all corners have invented algorithms to nd structure in vast online datasets. Data analysts now have access to tools for exploratory data analysis, decision tree induction, causal induction, function - timation,constructingcustomizedreferencedistributions,andvisualization,and thereareintelligentassistantsto adviseonmatters ofdesignandanalysis.There aretoolsfortraditional,relativelysmallsamples,andalsoforenormousdatasets. In all, the scope for probing data in new and penetrating ways has never been so exciting. The IDA-99 conference brings together a wide variety of researchers c- cerned with extracting knowledge from data, including people from statistics, machine learning, neural networks, computer science, pattern recognition, da- base management, and other areas.The strategiesadopted by people from these areas are often di erent, and a synergy results if this is recognized. The IDA series of conferences is intended to stimulate interaction between these di erent areas,sothatmorepowerfultoolsemergeforextractingknowledgefromdataand a better understanding is developed of the process of intelligent data analysis. The result is a conference that has a clear focus (one application area:intelligent data analysis) and a broad scope (many di erent methods and techniques).\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eLearning.- From Theoretical Learnability to Statistical Measures of the Learnable.- ALM: A Methodology for Designing Accurate Linguistic Models for Intelligent Data Analysis.- A “Top-Down and Prune” Induction Scheme for Constrained Decision Committees.- Mining Clusters with Association Rules.- Evolutionary Computation to Search for Strongly Correlated Variables in High-Dimensional Time-Series.- The Biases of Decision Tree Pruning Strategies.- Feature Selection as Retrospective Pruning in Hierarchical Clustering.- Discriminative Power of Input Features in a Fuzzy Model.- Learning Elements of Representations for Redescribing Robot Experiences.- “Seeing“ Objects in Spatial Datasets.- Intelligent Monitoring Method Using Time Varying Binomial Distribution Models for Pseudo-Periodic Communication Traffic.- Visualization.- Monitoring Human Information Processing via Intelligent Data Analysis of EEG Recordings.- Knowledge-Based Visualization to Support Spatial Data Mining.- Probabilistic Topic Maps: Navigating through Large Text Collections.- 3D Grand Tour for Multidimensional Data and Clusters.- Classification and Clustering.- A Decision Tree Algorithm for Ordinal Classification.- Discovering Dynamics Using Bayesian Clustering.- Integrating Declarative Knowledge in Hierarchical Clustering Tasks.- Nonparametric Linear Discriminant Analysis by Recursive Optimization with Random Initialization.- Supervised Classification Problems: How to Be Both Judge and Jury.- Temporal Pattern Generation Using Hidden Markov Model Based Unsupervised Classification.- Exploiting Similarity for Supporting Data Analysis and Problem Solving.- Multiple Prototype Model for Fuzzy Clustering.- A Comparison of Genetic Programming Variants for Data Classification.- Fuzzy Clustering Based on Modified Distance Measures.- Building Classes in Object-Based Languages by Automatic Clustering.- Integration.- Adjusted Estimation for the Combination of Classifiers.- Data-Driven Theory Refinement Using KBDistAl.- Reasoning about Input-Output Modeling of Dynamical Systems.- Undoing Statistical Advice.- A Method for Temporal Knowledge Conversion.- Applications.- Intrusion Detection through Behavioral Data.- Bayesian Neural Network Learning for Prediction in the Australian Dairy Industry.- Exploiting Sample-Data Distributions to Reduce the Cost of Nearest-Neighbor Searches with Kd-Trees.- Pump Failure Detection Using Support Vector Data Descriptions.- Data Mining for the Detection of Turning Points in Financial Time Series.- Computer-Assisted Classification of Legal Abstracts.- Sequential Control Logic Inferring Method from Observed Plant I\/O Data.- Evaluating an Eye Screening Test.- Application of Rough Sets Algorithms to Prediction of Aircraft Component Failure.- Media Mining.- Exploiting Structural Information for Text Classification on the WWW.- Multi-agent Web Information Retrieval: Neural Network Based Approach.- Adaptive Information Filtering Algorithms.- A Conceptual Graph Approach for Video Data Representation and Retrieval.","brand":"Springer-Verlag Berlin and Heidelberg GmbH \u0026 Co. KG","offers":[{"title":"Default Title","offer_id":49419504386391,"sku":"9783540663324","price":94.99,"currency_code":"GBP","in_stock":true}]},{"product_id":"cyber-intelligence-and-information-retrieval-proceedings-of-ciir-2021-9789811642838","title":"Cyber Intelligence and Information Retrieval:","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis book gathers a collection of high-quality peer-reviewed research papers presented at International Conference on Cyber Intelligence and Information Retrieval (CIIR 2021), held at Institute of Engineering \u0026amp; Management, Kolkata, India during 20–21 May 2021. The book covers research papers in the field of privacy and security in the cloud, data loss prevention and recovery, high-performance networks, network security and cryptography, image and signal processing, artificial immune systems, information and network security, data science techniques and applications, data warehousing and data mining, data mining in dynamic environment, higher-order neural computing, rough set and fuzzy set theory, and nature-inspired computing techniques. \u003c\/p\u003e\u003cp\u003e \u003c\/p\u003e\u003cp\u003e \u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eDTNMA: Identifying Routing Attacks in Delay-Tolerant Network.- Classification Framework for Fraud Detection Using Hidden Markov Model.- Analysis of the Beaufort Cipher Expansion Technique and Its Usage in Providing Data Security in Cloud.- Virtual Keyboard Using Image Processing \u0026amp; Computer Vision.- Next Step to the Future of Restaurants Through Artificial Intelligence and Facial Recognition.- A Comparative Study into Stock Market Prediction Through Various Sentiment Analysis Algorithms.- Bangla Document Categorization Using Deep RNN Model with Attention Mechanism.- Bangla Handwritten Digit Recognition.- A Comparative Study on Sentiment Analysis Influencing Word Embedding Using SVM and KNN.- COVID-19 Pandemic Diagnosis and Analysis Using Clinical Decision Support Systems.- Predictive Analysis of the Recovery Rate from Corona Virus(Covid-19).- Deep Learning Approaches for Spatio-Temporal Clues Modelling.- The Practical Enactment of Robotics and Artificial Intelligence Technologies in E-COMMERCE.- Comparative Analysis of Brain Tumor Segmentation with Fuzzy C-Means Using Multi-Core CPU and CUDA on GPU.- Multiregional Segmentation of High-Grade Glioma Using Modified Deep UNET Model with Edge-detected Multimodal MRI Images.\u003c\/p\u003e\u003cbr\u003e","brand":"Springer Verlag, Singapore","offers":[{"title":"Default Title","offer_id":49427828834647,"sku":"9789811642838","price":161.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9789811642838.jpg?v=1730565817"},{"product_id":"health-information-processing-evaluation-track-papers-9789819717163","title":"Health Information Processing. Evaluation Track","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e","brand":"Springer Nature Singapore","offers":[{"title":"Default Title","offer_id":49427870941527,"sku":"9789819717163","price":56.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9789819717163.jpg?v=1730565960"},{"product_id":"the-chief-data-officer-handbook-for-data-governance-9781583474174","title":"The Chief Data Officer Handbook for Data","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eA practical guide for today’s chief data officers to define and manage data governance programs\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003eThe relatively new role of chief data officer (CDO) has been created to address the issue of managing a company’s data as a strategic asset, but the problem is that there is no universally accepted “playbook” for this role. Magnifying the challenge is the rapidly increasing volume and complexity of data, as well as regulatory compliance as it relates to data. In this book, Sunil Soares provides a practical guide for today’s chief data officers to manage data as an asset while delivering the trusted data required to power business initiatives, from the tactical to the transformative. The guide describes the relationship between the CDO and the data governance team, whose task is the formulation of policy to optimize, secure, and leverage information as an enterprise asset by aligning the objectives of multiple functions. Soares provides unique insight into the role of the CDO and presents a blueprint for implementing data governance successfully within the context of the position. With practical advice CDOs need, this book helps establish new data governance practices or mature existing practices.","brand":"MC Press, LLC","offers":[{"title":"Default Title","offer_id":49532646195543,"sku":"9781583474174","price":13.46,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781583474174.jpg?v=1731887918"},{"product_id":"data-resource-guide-managing-the-data-resource-data-9781634621007","title":"Data Resource Guide: Managing the Data Resource","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eAre you struggling to find the data that you need to support your business activities?  Are you concerned that people may be using the wrong data for their business activities?  Are you having difficulty understanding the data that you do find in your data resource?  Are you frustrated over documenting that understanding in a manner that is readily accessible to anyone in the organisation?  If the answer to any of these questions is Yes, then you need to read \"Data Resource Guide\" to help identify, understand, access, and use the appropriate data. Most public and private sector organisations today have no formal, single location for the complete documentation of their data resource that is readily available to everyone in the organisation.  Many organisations do not even have a concept of how to design, develop, or manage a single repository containing an understanding all the data available to the organisation.  Yet they are staking their business on those data. \"Data Resource Data\" provided the complete data resource model for an organisation''s Data Resource Data.  Data Resource Understanding provided a detailed description of how to thoroughly understand an organisation''s data resource through those Data Resource Data.  Now, \"Data Resource Guide\" provides the detailed specifications for developing a simple, inexpensive, and effective way to document the data resource understanding and make that understanding readily available to anyone in the organisation. Michael Brackett draws on over half a century of data management experience to complete two trilogies for formally managing an organisation''s data as a critical resource.  The Data Architecture Trilogy describes the development of a single organisation wide data architecture for an organization.  The Data Understanding Trilogy describes the acquisition and documentation of understanding about all the data at an organisation''s disposal.","brand":"Technics Publications LLC","offers":[{"title":"Default Title","offer_id":50099348898135,"sku":"9781634621007","price":36.89,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781634621007.jpg?v=1740996395"},{"product_id":"growing-business-intelligence-an-agile-approach-to-leveraging-data-analytics-for-maximum-business-value-9781634621472","title":"Growing Business Intelligence: An Agile Approach","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eHow do we enable our organisations to enjoy the often significant benefits of BI and analytics, while at the same time minimising the cost and risk of failure? In this book, I am not going to try to be prescriptive; I wont tell you exactly how to build your BI environment. Instead, I am going to focus on a few core principles that will enable you to navigate the rocky shoals of BI architecture and arrive at a destination best suited for your particular organisation. Some of these core principles include: Have an overarching strategy, plan, and roadmap. Recognise and leverage your existing technology investments. Support both data discovery and data reuse. Keep data in motion, not at rest. Separate information delivery from data storage. Emphasise data transparency over data quality. Take an agile approach to BI development. This book will show you how to successfully navigate both the jungle of BI technology and the minefield of human nature. It will show you how to create a BI architecture and strategy that addresses the needs of all organisational stakeholders. It will show you how to maximise the value of your BI investments. It will show you how to manage the risk of disruptive technology. And it will show you how to use agile methodologies to deliver on the promise of BI and analytics quickly, succinctly, and iteratively. This book is about many things. But principally, its about success. The goal of any enterprise initiative is to succeed and to derive benefit -- benefit that all stakeholders can share in. I want you to be successful. I want your organisation to be successful. This book will show you how. This book is for anyone who is currently or will someday be working on a BI, analytics, or Big Data project, and for organisations that want to get the maximum amount of value from both their data and their BI technology investment. This includes all stakeholders in the BI effort -- not just the data people or the IT people, but also the business stakeholders who have the responsibility for the definition and use of data. There are six sections to this book: In Section I, What Kind of Garden Do You Want?, we will examine the benefits and risks of Business Intelligence, making the central point that BI is a business (not IT) process designed to manage data assets in pursuit of enterprise goals. We will show how data, when properly managed and used, can be a key enabler of several types of core business processes. The purpose of this section is to help you define the particular benefit(s) you want from BI. In Section II, Building the Bones, we will talk about how to design and build out the hardscape (infrastructure) of your BI environment. This stage of the process involves leveraging existing technology investments and iteratively moving toward your desired target state BI architecture.  In Section III, From the Ground Up, we explore the more detailed aspects of implementing your BI operational environment. In Section IV, Weeds, Pests and Critters, we talk about the myriad of things that can go wrong on a BI project, and discuss ways of mitigating these risks. In Section V, The Sustainable Garden, we talk about how to create a BI infrastructure that is easy and inexpensive to maintain. Finally, Section VI presents a case study illustrating the principles of this book, as applied to a fictional manufacturing company (the Blue Moon Guitar Company).","brand":"Technics Publications LLC","offers":[{"title":"Default Title","offer_id":50107812118871,"sku":"9781634621472","price":39.09,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781634621472.jpg?v=1741085017"},{"product_id":"data-architecture-a-primer-for-the-data-scientist-9780128169162","title":"Data Architecture A Primer for the Data Scientist","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. An Introduction to Data Architecture2. The End-State Architecture - The \"World Map\"3. Transformations in the End-State Architecture4. A Brief History of Big Data5. The Siloed Application Environment6. Introduction to Data Vault 2.07. The Operational Environment: A Short History8. A Brief History of Data Architecture9. Repetitive Analytics: Some Basics10. Nonrepetitive Data11. Operational Analytics: Response Time12. Operational Analytics13. Personal Analytics14. Data Models Across the End-State Architecture15. The System of Record16. Business Value and the End-State Architecture17. Managing Text18. An Introduction to Data Visualizations","brand":"Elsevier Science","offers":[{"title":"Default Title","offer_id":51017547874647,"sku":"9780128169162","price":999.99,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780128169162.jpg?v=1750773904"},{"product_id":"principles-of-data-mining-9781447174929","title":"Principles of Data Mining","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eIntroduction to Data Mining.- Data for Data Mining.- Introduction to Classification: Naïve Bayes and Nearest Neighbour.- Using Decision Trees for Classification.- Decision Tree Induction: Using Entropy for Attribute Selection.- Decision Tree Induction: Using Frequency Tables for Attribute Selection.- Estimating the Predictive Accuracy of a Classifier.- Continuous Attributes.- Avoiding Overfitting of Decision Trees.- More About Entropy.- Inducing Modular Rules for Classification.- Measuring the Performance of a Classifier.- Dealing with Large Volumes of Data.- Ensemble Classification.- Comparing Classifiers.- Associate Rule Mining I.- Associate Rule Mining II.- Associate Rule Mining III.- Clustering.- Mining.- Classifying Streaming Data.- Classifying Streaming Data II: Time-dependent Data.- An Introduction to Neural Networks.- Appendix A  Essential Mathematics.- Appendix B  Datasets.- Appendix C  Sources of Further Information.- Appendix D  Glossary and Notation.- Appendix E  Solutio\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eIntroduction to Data Mining.- Data for Data Mining.- Introduction to Classification: Naïve Bayes and Nearest Neighbour.- Using Decision Trees for Classification.- Decision Tree Induction: Using Entropy for Attribute Selection.- Decision Tree Induction: Using Frequency Tables for Attribute Selection.- Estimating the Predictive Accuracy of a Classifier.- Continuous Attributes.- Avoiding Overfitting of Decision Trees.- More About Entropy.- Inducing Modular Rules for Classification.- Measuring the Performance of a Classifier.- Dealing with Large Volumes of Data.- Ensemble Classification.- Comparing Classifiers.- Associate Rule Mining I.- Associate Rule Mining II.- Associate Rule Mining III.- Clustering.- Mining.- Classifying Streaming Data.- Classifying Streaming Data II: Time-dependent Data.- An Introduction to Neural Networks.- Appendix A – Essential Mathematics.- Appendix B – Datasets.- Appendix C – Sources of Further Information.- Appendix D – Glossary and Notation.- Appendix E – Solutions to Self-assessment Exercises.- Index.\u003c\/p\u003e","brand":"Springer","offers":[{"title":"Default Title","offer_id":51019858444631,"sku":"9781447174929","price":37.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781447174929.jpg?v=1750781499"},{"product_id":"sql-made-easy-tips-and-tricks-to-mastering-sql-programming-9798858294986","title":"SQL Made Easy: Tips and Tricks to Mastering SQL","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e","brand":"Independently Published","offers":[{"title":"Default Title","offer_id":51021561856343,"sku":"9798858294986","price":13.17,"currency_code":"GBP","in_stock":true}]}],"url":"https:\/\/bookcurl.com\/collections\/data-warehousing.oembed?page=3","provider":"Book Curl","version":"1.0","type":"link"}