{"product_id":"business-intelligence-isbn-9780470511381","title":"Business Intelligence","description":"Business intelligence is a broad category of applications and technologies for gathering, providing access to, and analyzing data for the purpose of helping enterprise users make better business decisions. The term implies having a comprehensive knowledge of all factors that affect a business, such as customers, competitors, business partners, economic environment, and internal operations, therefore enabling optimal decisions to be made.  \u003cp\u003e\u003ci\u003eBusiness Intelligence\u003c\/i\u003e provides readers with an introduction and practical guide to the mathematical models and analysis methodologies vital to business intelligence.\u003c\/p\u003e \u003cp\u003eThis book:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eCombines detailed coverage with a practical guide to the mathematical models and analysis methodologies of business intelligence.\u003c\/li\u003e \u003cli\u003eCovers all the hot topics such as data warehousing, data mining and its applications, machine learning, classification, supply optimization models, decision support systems, and analytical methods for performance evaluation.\u003c\/li\u003e \u003cli\u003eIs made accessible to readers through the careful definition and introduction of each concept, followed by the extensive use of examples and numerous real-life case studies.\u003c\/li\u003e \u003cli\u003eExplains how to utilise mathematical models and analysis models to make effective and good quality business decisions.\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThis book is aimed at postgraduate students following data analysis and data mining courses.\u003c\/p\u003e \u003cp\u003eResearchers looking for a systematic and broad coverage of topics in operations research and mathematical models for decision-making will find this an invaluable guide.\u003c\/p\u003eData Mining und Optimierung zur Erleichterung von Entscheidungen: Der Autor dieses Bandes hat Informationen zu diesem Thema zusammengefasst und aufbereitet, die Sie sonst mühsam in der weit verstreuten Fachliteratur suchen müssten. Mathematische Modelle und Analysenverfahren werden gut verständlich eingeführt und anhand von Beispielen und Fallstudien aus der Praxis erläutert. \u003cp\u003ePreface xiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003eI Components of the decision-making process 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Business intelligence 3\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Effective and timely decisions 3\u003c\/p\u003e \u003cp\u003e1.2 Data, information and knowledge 6\u003c\/p\u003e \u003cp\u003e1.3 The role of mathematical models 8\u003c\/p\u003e \u003cp\u003e1.4 Business intelligence architectures 9\u003c\/p\u003e \u003cp\u003e1.4.1 Cycle of a business intelligence analysis 11\u003c\/p\u003e \u003cp\u003e1.4.2 Enabling factors in business intelligence projects 13\u003c\/p\u003e \u003cp\u003e1.4.3 Development of a business intelligence system 14\u003c\/p\u003e \u003cp\u003e1.5 Ethics and business intelligence 17\u003c\/p\u003e \u003cp\u003e1.6 Notes and readings 18\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Decision support systems 21\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Definition of system 21\u003c\/p\u003e \u003cp\u003e2.2 Representation of the decision-making process 23\u003c\/p\u003e \u003cp\u003e2.2.1 Rationality and problem solving 24\u003c\/p\u003e \u003cp\u003e2.2.2 The decision-making process 25\u003c\/p\u003e \u003cp\u003e2.2.3 Types of decisions 29\u003c\/p\u003e \u003cp\u003e2.2.4 Approaches to the decision-making process 33\u003c\/p\u003e \u003cp\u003e2.3 Evolution of information systems 35\u003c\/p\u003e \u003cp\u003e2.4 Definition of decision support system 36\u003c\/p\u003e \u003cp\u003e2.5 Development of a decision support system 40\u003c\/p\u003e \u003cp\u003e2.6 Notes and readings 43\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Data warehousing 45\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Definition of data warehouse 45\u003c\/p\u003e \u003cp\u003e3.1.1 Data marts 49\u003c\/p\u003e \u003cp\u003e3.1.2 Data quality 50\u003c\/p\u003e \u003cp\u003e3.2 Data warehouse architecture 51\u003c\/p\u003e \u003cp\u003e3.2.1 ETL tools 53\u003c\/p\u003e \u003cp\u003e3.2.2 Metadata 54\u003c\/p\u003e \u003cp\u003e3.3 Cubes and multidimensional analysis 55\u003c\/p\u003e \u003cp\u003e3.3.1 Hierarchies of concepts and OLAP operations 60\u003c\/p\u003e \u003cp\u003e3.3.2 Materialization of cubes of data 61\u003c\/p\u003e \u003cp\u003e3.4 Notes and readings 62\u003c\/p\u003e \u003cp\u003e\u003cb\u003eII Mathematical Models and Methods 63\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Mathematical models for decision making 65\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Structure of mathematical models 65\u003c\/p\u003e \u003cp\u003e4.2 Development of a model 67\u003c\/p\u003e \u003cp\u003e4.3 Classes of models 70\u003c\/p\u003e \u003cp\u003e4.4 Notes and readings 75\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Data mining 77\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Definition of data mining 77\u003c\/p\u003e \u003cp\u003e5.1.1 Models and methods for data mining 79\u003c\/p\u003e \u003cp\u003e5.1.2 Data mining, classical statistics and OLAP 80\u003c\/p\u003e \u003cp\u003e5.1.3 Applications of data mining 81\u003c\/p\u003e \u003cp\u003e5.2 Representation of input data 82\u003c\/p\u003e \u003cp\u003e5.3 Data mining process 84\u003c\/p\u003e \u003cp\u003e5.4 Analysis methodologies 90\u003c\/p\u003e \u003cp\u003e5.5 Notes and readings 94\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Data preparation 95\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Data validation 95\u003c\/p\u003e \u003cp\u003e6.1.1 Incomplete data 96\u003c\/p\u003e \u003cp\u003e6.1.2 Data affected by noise 97\u003c\/p\u003e \u003cp\u003e6.2 Data transformation 99\u003c\/p\u003e \u003cp\u003e6.2.1 Standardization 99\u003c\/p\u003e \u003cp\u003e6.2.2 Feature extraction 100\u003c\/p\u003e \u003cp\u003e6.3 Data reduction 100\u003c\/p\u003e \u003cp\u003e6.3.1 Sampling 101\u003c\/p\u003e \u003cp\u003e6.3.2 Feature selection 102\u003c\/p\u003e \u003cp\u003e6.3.3 Principal component analysis 104\u003c\/p\u003e \u003cp\u003e6.3.4 Data discretization 109\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Data exploration 113\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Univariate analysis 113\u003c\/p\u003e \u003cp\u003e7.1.1 Graphical analysis of categorical attributes 114\u003c\/p\u003e \u003cp\u003e7.1.2 Graphical analysis of numerical attributes 116\u003c\/p\u003e \u003cp\u003e7.1.3 Measures of central tendency for numerical attributes 118\u003c\/p\u003e \u003cp\u003e7.1.4 Measures of dispersion for numerical attributes 121\u003c\/p\u003e \u003cp\u003e7.1.5 Measures of relative location for numerical attributes 126\u003c\/p\u003e \u003cp\u003e7.1.6 Identification of outliers for numerical attributes 127\u003c\/p\u003e \u003cp\u003e7.1.7 Measures of heterogeneity for categorical attributes 129\u003c\/p\u003e \u003cp\u003e7.1.8 Analysis of the empirical density 130\u003c\/p\u003e \u003cp\u003e7.1.9 Summary statistics 135\u003c\/p\u003e \u003cp\u003e7.2 Bivariate analysis 136\u003c\/p\u003e \u003cp\u003e7.2.1 Graphical analysis 136\u003c\/p\u003e \u003cp\u003e7.2.2 Measures of correlation for numerical attributes 142\u003c\/p\u003e \u003cp\u003e7.2.3 Contingency tables for categorical attributes 145\u003c\/p\u003e \u003cp\u003e7.3 Multivariate analysis 147\u003c\/p\u003e \u003cp\u003e7.3.1 Graphical analysis 147\u003c\/p\u003e \u003cp\u003e7.3.2 Measures of correlation for numerical attributes 149\u003c\/p\u003e \u003cp\u003e7.4 Notes and readings 152\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Regression 153\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Structure of regression models 153\u003c\/p\u003e \u003cp\u003e8.2 Simple linear regression 156\u003c\/p\u003e \u003cp\u003e8.2.1 Calculating the regression line 158\u003c\/p\u003e \u003cp\u003e8.3 Multiple linear regression 161\u003c\/p\u003e \u003cp\u003e8.3.1 Calculating the regression coefficients 162\u003c\/p\u003e \u003cp\u003e8.3.2 Assumptions on the residuals 163\u003c\/p\u003e \u003cp\u003e8.3.3 Treatment of categorical predictive attributes 166\u003c\/p\u003e \u003cp\u003e8.3.4 Ridge regression 167\u003c\/p\u003e \u003cp\u003e8.3.5 Generalized linear regression 168\u003c\/p\u003e \u003cp\u003e8.4 Validation of regression models 168\u003c\/p\u003e \u003cp\u003e8.4.1 Normality and independence of the residuals 169\u003c\/p\u003e \u003cp\u003e8.4.2 Significance of the coefficients 172\u003c\/p\u003e \u003cp\u003e8.4.3 Analysis of variance 174\u003c\/p\u003e \u003cp\u003e8.4.4 Coefficient of determination 175\u003c\/p\u003e \u003cp\u003e8.4.5 Coefficient of linear correlation 176\u003c\/p\u003e \u003cp\u003e8.4.6 Multicollinearity of the independent variables 177\u003c\/p\u003e \u003cp\u003e8.4.7 Confidence and prediction limits 178\u003c\/p\u003e \u003cp\u003e8.5 Selection of predictive variables 179\u003c\/p\u003e \u003cp\u003e8.5.1 Example of development of a regression model 180\u003c\/p\u003e \u003cp\u003e8.6 Notes and readings 185\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Time series 187\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Definition of time series 187\u003c\/p\u003e \u003cp\u003e9.1.1 Index numbers 190\u003c\/p\u003e \u003cp\u003e9.2 Evaluating time series models 192\u003c\/p\u003e \u003cp\u003e9.2.1 Distortion measures 192\u003c\/p\u003e \u003cp\u003e9.2.2 Dispersion measures 193\u003c\/p\u003e \u003cp\u003e9.2.3 Tracking signal 194\u003c\/p\u003e \u003cp\u003e9.3 Analysis of the components of time series 195\u003c\/p\u003e \u003cp\u003e9.3.1 Moving average 196\u003c\/p\u003e \u003cp\u003e9.3.2 Decomposition of a time series 198\u003c\/p\u003e \u003cp\u003e9.4 Exponential smoothing models 203\u003c\/p\u003e \u003cp\u003e9.4.1 Simple exponential smoothing 203\u003c\/p\u003e \u003cp\u003e9.4.2 Exponential smoothing with trend adjustment 204\u003c\/p\u003e \u003cp\u003e9.4.3 Exponential smoothing with trend and seasonality 206\u003c\/p\u003e \u003cp\u003e9.4.4 Simple adaptive exponential smoothing 207\u003c\/p\u003e \u003cp\u003e9.4.5 Exponential smoothing with damped trend 208\u003c\/p\u003e \u003cp\u003e9.4.6 Initial values for exponential smoothing models 209\u003c\/p\u003e \u003cp\u003e9.4.7 Removal of trend and seasonality 209\u003c\/p\u003e \u003cp\u003e9.5 Autoregressive models 210\u003c\/p\u003e \u003cp\u003e9.5.1 Moving average models 212\u003c\/p\u003e \u003cp\u003e9.5.2 Autoregressive moving average models 212\u003c\/p\u003e \u003cp\u003e9.5.3 Autoregressive integrated moving average models 212\u003c\/p\u003e \u003cp\u003e9.5.4 Identification of autoregressive models 213\u003c\/p\u003e \u003cp\u003e9.6 Combination of predictive models 216\u003c\/p\u003e \u003cp\u003e9.7 The forecasting process 217\u003c\/p\u003e \u003cp\u003e9.7.1 Characteristics of the forecasting process 217\u003c\/p\u003e \u003cp\u003e9.7.2 Selection of a forecasting method 219\u003c\/p\u003e \u003cp\u003e9.8 Notes and readings 219\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Classification 221\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Classification problems 221\u003c\/p\u003e \u003cp\u003e10.1.1 Taxonomy of classification models 224\u003c\/p\u003e \u003cp\u003e10.2 Evaluation of classification models 226\u003c\/p\u003e \u003cp\u003e10.2.1 Holdout method 228\u003c\/p\u003e \u003cp\u003e10.2.2 Repeated random sampling 228\u003c\/p\u003e \u003cp\u003e10.2.3 Cross-validation 229\u003c\/p\u003e \u003cp\u003e10.2.4 Confusion matrices 230\u003c\/p\u003e \u003cp\u003e10.2.5 ROC curve charts 233\u003c\/p\u003e \u003cp\u003e10.2.6 Cumulative gain and lift charts 234\u003c\/p\u003e \u003cp\u003e10.3 Classification trees 236\u003c\/p\u003e \u003cp\u003e10.3.1 Splitting rules 240\u003c\/p\u003e \u003cp\u003e10.3.2 Univariate splitting criteria 243\u003c\/p\u003e \u003cp\u003e10.3.3 Example of development of a classification tree 246\u003c\/p\u003e \u003cp\u003e10.3.4 Stopping criteria and pruning rules 250\u003c\/p\u003e \u003cp\u003e10.4 Bayesian methods 251\u003c\/p\u003e \u003cp\u003e10.4.1 Naive Bayesian classifiers 252\u003c\/p\u003e \u003cp\u003e10.4.2 Example of naive Bayes classifier 253\u003c\/p\u003e \u003cp\u003e10.4.3 Bayesian networks 256\u003c\/p\u003e \u003cp\u003e10.5 Logistic regression 257\u003c\/p\u003e \u003cp\u003e10.6 Neural networks 259\u003c\/p\u003e \u003cp\u003e10.6.1 The Rosenblatt perceptron 259\u003c\/p\u003e \u003cp\u003e10.6.2 Multi-level feed-forward networks 260\u003c\/p\u003e \u003cp\u003e10.7 Support vector machines 262\u003c\/p\u003e \u003cp\u003e10.7.1 Structural risk minimization 262\u003c\/p\u003e \u003cp\u003e10.7.2 Maximal margin hyperplane for linear separation 266\u003c\/p\u003e \u003cp\u003e10.7.3 Nonlinear separation 270\u003c\/p\u003e \u003cp\u003e10.8 Notes and readings 275\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Association rules 277\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Motivation and structure of association rules 277\u003c\/p\u003e \u003cp\u003e11.2 Single-dimension association rules 281\u003c\/p\u003e \u003cp\u003e11.3 Apriori algorithm 284\u003c\/p\u003e \u003cp\u003e11.3.1 Generation of frequent itemsets 284\u003c\/p\u003e \u003cp\u003e11.3.2 Generation of strong rules 285\u003c\/p\u003e \u003cp\u003e11.4 General association rules 288\u003c\/p\u003e \u003cp\u003e11.5 Notes and readings 290\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Clustering 293\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Clustering methods 293\u003c\/p\u003e \u003cp\u003e12.1.1 Taxonomy of clustering methods 294\u003c\/p\u003e \u003cp\u003e12.1.2 Affinity measures 296\u003c\/p\u003e \u003cp\u003e12.2 Partition methods 302\u003c\/p\u003e \u003cp\u003e12.2.1 \u003ci\u003eK\u003c\/i\u003e-means algorithm 302\u003c\/p\u003e \u003cp\u003e12.2.2 \u003ci\u003eK\u003c\/i\u003e-medoids algorithm 305\u003c\/p\u003e \u003cp\u003e12.3 Hierarchical methods 307\u003c\/p\u003e \u003cp\u003e12.3.1 Agglomerative hierarchical methods 308\u003c\/p\u003e \u003cp\u003e12.3.2 Divisive hierarchical methods 310\u003c\/p\u003e \u003cp\u003e12.4 Evaluation of clustering models 312\u003c\/p\u003e \u003cp\u003e12.5 Notes and readings 315\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIII Business Intelligence Applications 317\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Marketing models 319\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Relational marketing 320\u003c\/p\u003e \u003cp\u003e13.1.1 Motivations and objectives 320\u003c\/p\u003e \u003cp\u003e13.1.2 An environment for relational marketing analysis 327\u003c\/p\u003e \u003cp\u003e13.1.3 Lifetime value 329\u003c\/p\u003e \u003cp\u003e13.1.4 The effect of latency in predictive models 332\u003c\/p\u003e \u003cp\u003e13.1.5 Acquisition 333\u003c\/p\u003e \u003cp\u003e13.1.6 Retention 334\u003c\/p\u003e \u003cp\u003e13.1.7 Cross-selling and up-selling 335\u003c\/p\u003e \u003cp\u003e13.1.8 Market basket analysis 335\u003c\/p\u003e \u003cp\u003e13.1.9 Web mining 336\u003c\/p\u003e \u003cp\u003e13.2 Salesforce management 338\u003c\/p\u003e \u003cp\u003e13.2.1 Decision processes in salesforce management 339\u003c\/p\u003e \u003cp\u003e13.2.2 Models for salesforce management 342\u003c\/p\u003e \u003cp\u003e13.2.3 Response functions 343\u003c\/p\u003e \u003cp\u003e13.2.4 Sales territory design 346\u003c\/p\u003e \u003cp\u003e13.2.5 Calls and product presentations planning 347\u003c\/p\u003e \u003cp\u003e13.3 Business case studies 352\u003c\/p\u003e \u003cp\u003e13.3.1 Retention in telecommunications 352\u003c\/p\u003e \u003cp\u003e13.3.2 Acquisition in the automotive industry 354\u003c\/p\u003e \u003cp\u003e13.3.3 Cross-selling in the retail industry 358\u003c\/p\u003e \u003cp\u003e13.4 Notes and readings 360\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Logistic and production models 361\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Supply chain optimization 362\u003c\/p\u003e \u003cp\u003e14.2 Optimization models for logistics planning 364\u003c\/p\u003e \u003cp\u003e14.2.1 Tactical planning 364\u003c\/p\u003e \u003cp\u003e14.2.2 Extra capacity 365\u003c\/p\u003e \u003cp\u003e14.2.3 Multiple resources 366\u003c\/p\u003e \u003cp\u003e14.2.4 Backlogging 366\u003c\/p\u003e \u003cp\u003e14.2.5 Minimum lots and fixed costs 369\u003c\/p\u003e \u003cp\u003e14.2.6 Bill of materials 370\u003c\/p\u003e \u003cp\u003e14.2.7 Multiple plants 371\u003c\/p\u003e \u003cp\u003e14.3 Revenue management systems 372\u003c\/p\u003e \u003cp\u003e14.3.1 Decision processes in revenue management 373\u003c\/p\u003e \u003cp\u003e14.4 Business case studies 376\u003c\/p\u003e \u003cp\u003e14.4.1 Logistics planning in the food industry 376\u003c\/p\u003e \u003cp\u003e14.4.2 Logistics planning in the packaging industry 383\u003c\/p\u003e \u003cp\u003e14.5 Notes and readings 384\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Data envelopment analysis 385\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Efficiency measures 386\u003c\/p\u003e \u003cp\u003e15.2 Efficient frontier 386\u003c\/p\u003e \u003cp\u003e15.3 The CCR model 390\u003c\/p\u003e \u003cp\u003e15.3.1 Definition of target objectives 392\u003c\/p\u003e \u003cp\u003e15.3.2 Peer groups 393\u003c\/p\u003e \u003cp\u003e15.4 Identification of good operating practices 394\u003c\/p\u003e \u003cp\u003e15.4.1 Cross-efficiency analysis 394\u003c\/p\u003e \u003cp\u003e15.4.2 Virtual inputs and virtual outputs 395\u003c\/p\u003e \u003cp\u003e15.4.3 Weight restrictions 396\u003c\/p\u003e \u003cp\u003e15.5 Other models 396\u003c\/p\u003e \u003cp\u003e15.6 Notes and readings 397\u003c\/p\u003e \u003cp\u003eAppendix A Software tools 399\u003c\/p\u003e \u003cp\u003eAppendix B Dataset repositories 401\u003c\/p\u003e \u003cp\u003eReferences 403\u003c\/p\u003e \u003cp\u003eIndex 413\u003c\/p\u003e  \u003cb\u003eCarlo Vercellis - School of Management, Politecnico di Milano, Italy\u003c\/b\u003e  \u003cp\u003eAs well as teaching courses in Operations Research and Business Intelligence, Professor Vercellis is director of the research group MOLD (Mathematical Modeling, Optimization, Learning from Data). He has written four book in Italian, contributed to numerous other books, and has had many papers published in a variety of international journals.\u003c\/p\u003e  Business intelligence is a broad category of applications and technologies for gathering, providing access to, and analyzing data for the purpose of helping enterprise users make better business decisions. The term implies having a comprehensive knowledge of all factors that affect a business, such as customers, competitors, business partners, economic environment, and internal operations, therefore enabling optimal decisions to be made.  \u003cp\u003e\u003ci\u003eBusiness Intelligence\u003c\/i\u003e provides readers with an introduction and practical guide to the mathematical models and analysis methodologies vital to business intelligence.\u003c\/p\u003e \u003cp\u003eThis book:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eCombines detailed coverage with a practical guide to the mathematical models and analysis methodologies of business intelligence.\u003c\/li\u003e \u003cli\u003eCovers all the hot topics such as data warehousing, data mining and its applications, machine learning, classification, supply optimization models, decision support systems, and analytical methods for performance evaluation.\u003c\/li\u003e \u003cli\u003eIs made accessible to readers through the careful definition and introduction of each concept, followed by the extensive use of examples and numerous real-life case studies.\u003c\/li\u003e \u003cli\u003eExplains how to utilise mathematical models and analysis models to make effective and good quality business decisions.\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e This book is aimed at postgraduate students following data analysis and data mining courses. Researchers looking for a systematic and broad coverage of topics in operations research and mathematical models for decision-making will find this an invaluable guide.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988871987429,"sku":"NP9780470511381","price":207.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780470511381.jpg?v=1761781859","url":"https:\/\/k12savings.com\/products\/business-intelligence-isbn-9780470511381","provider":"K12savings","version":"1.0","type":"link"}