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Predictive Analysis with SAP: The Comprehensive Guide

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en Limba Engleză Hardback – 28 Nov 2013

Today's businesses are driven by data. Unlock the potential of your structured and unstructured data, anticipate market changes, and drive decision making with this comprehensive guide to SAP Predictive Analysis tools-SAP Predictive Analysis module, the PAL Library, R Integration, and SAP HANA. Filled with simple examples, customer case studies, and explanations of the business benefits, this book helps you navigate the complex predictive analysis process. From cluster analysis to text analysis, transform your raw data into improved business process.

1. Predictive Analysis OverviewLearn what predictive analysis is, the practical business value that it provides, and the tools in SAP that support it.

2. Algorithm SelectionChoose the right algorithm for your needs and understand the strengths and weaknesses of each algorithm and method of predictive analysis.

3. Predictive Analysis AppliedSimplify the complex predictive analysis process and learn how to apply predictive analysis with practical examples, case studies, and business explanations.

4. Data VisualizedLearn how to investigate large amounts of data via useful and comprehensive visualizations, and share the analysis with ease!

5. Jump-start Your AnalysisFull code listings are provided to help facilitate your using the SAP HANA Predictive AnalysisLibrary (PAL), including data sets and parameter settings required for the analysis.


Highlights include:

  • Predictive Analysis Library (PAL) in SAP HANA
  • The R Integration for SAP HANA
  • SAP Predictive Analysis (PA)
  • Data and text mining
  • Outlier analysis
  • Association analysis
  • Cluster analysis
  • Classification analysis
  • Regression analysis
  • Decision tree analysis
  • Time-series analysis
  • Text analysis and text mining

  • ]]>
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Specificații

ISBN-13: 9781592299157
ISBN-10: 1592299156
Pagini: 525
Dimensiuni: 182 x 236 x 38 mm
Greutate: 1.29 kg
Editura: SAP PR

Cuprins


Introduction ... 17
Acknowledgments ... 21

PART I: Predictive Analysis Overview ... 23

1. An Introduction to Predictive Analysis ... 25


1.1 ... Definitions of Predictive Analysis ... 25
1.2 ... The Value of Predictive Analysis ... 28
1.3 ... User Personas ... 31
1.4 ... Applications of Predictive Analysis ... 33
1.5 ... Classes of Applications ... 37
1.6 ... Algorithms for Predictive Analysis ... 39
1.7 ... The Predictive Analysis Process ... 41
1.8 ... Hot Topics and Trends ... 44
1.9 ... Challenges and Criteria for Success ... 45
1.10 ... Summary ... 47

2. An Overview of the Predictive Analysis Products in SAP ... 49


2.1 ... The Predictive Analysis Library in SAP HANA ... 53
2.2 ... The R Integration for SAP HANA ... 59
2.3 ... SAP Predictive Analysis ... 63
2.4 ... SAP Business Solutions with Predictive Analysis ... 73
2.5 ... Summary ... 77

PART II: Predictive Analysis Applied ... 79

3. Initial Data Exploration ... 81


3.1 ... Data Types ... 83
3.2 ... Data Visualization for Data Exploration ... 86
3.3 ... Sampling ... 92
3.4 ... Scaling ... 97
3.5 ... Binning ... 101
3.6 ... Outliers ... 104
3.7 ... Summary ... 105

4. Which Algorithm When? ... 107


4.1 ... The Main Factors When Selecting an Algorithm ... 107
4.2 ... Classes of Applications and Algorithms ... 109
4.3 ... Matrix of Application Tasks, Variable Types and Output ... 113
4.4 ... Which Algorithm Is the Best? ... 115
4.5 ... A Set of Rules for Which Algorithm When ... 116
4.6 ... Summary ... 118

5. When Mining, Beware of Mines ... 119


5.1 ... Data Mining Heaven and Hell ... 119
5.2 ... Five Myths ... 121
5.3 ... Five Pitfalls ... 124
5.4 ... Further Challenges and Resolution ... 126
5.5 ... Key Factors for Success ... 137
5.6 ... Summary ... 138

6. Applications in SAP ... 139


6.1 ... SAP Smart Meter Analytics ... 139
6.2 ... SAP Customer Engagement Intelligence ... 142
6.3 ... SAP Enterprise Inventory & Service-Level Optimization ... 149
6.4 ... SAP Precision Gaming ... 158
6.5 ... SAP Affinity Insight ... 161
6.6 ... SAP Demand Signal Management ... 166
6.7 ... SAP On-Shelf Availability ... 172
6.8 ... SAP Product Recommendation Intelligence ... 177
6.9 ... SAP Credit Insight ... 182
6.10 ... SAP Convergent Pricing Simulation ... 184
6.11 ... Summary ... 187

7. SAP Predictive Analysis ... 189


7.1 ... Getting Started in PA ... 189
7.2 ... Accessing and Viewing the Data Source ... 195
7.3 ... Preparing Data for Analysis ... 199
7.4 ... Applying Algorithms to Analyze the Data ... 202
7.5 ... Running the Model and Viewing the Results ... 209
7.6 ... Deploying the Model in a Business Application ... 213
7.7 ... Summary ... 219

PART III: Predictive Analysis Categories ... 221

8. Outlier Analysis ... 223


8.1 ... Introduction to Outlier Analysis ... 223
8.2 ... Applications of Outlier Analysis ... 225
8.3 ... The Inter-Quartile Range Test ... 227
8.4 ... The Variance Test ... 232
8.5 ... K Nearest Neighbor Outlier ... 235
8.6 ... Anomaly Detection using Cluster Analysis ... 238
8.7 ... The Business Case for Outlier Analysis ... 243
8.8 ... Strengths and Weaknesses of Outlier Analysis ... 244
8.9 ... Summary ... 245

9. Association Analysis ... 247


9.1 ... Applications of Association Analysis ... 248
9.2 ... Apriori Association Analysis ... 250
9.3 ... Apriori Association Analysis in the PAL ... 255
9.4 ... An Example of Apriori Association Analysis in the PAL ... 257
9.5 ... An Example of Apriori in SAP Predictive Analysis ... 260
9.6 ... Apriori Lite Association Analysis ... 262
9.7 ... Strengths and Weaknesses of Association Analysis ... 266
9.8 ... Business Case for Association Analysis ... 266
9.9 ... Summary ... 267

10. Cluster Analysis ... 269


10.1 ... Introduction to Cluster Analysis ... 269
10.2 ... Applications of Cluster Analysis ... 270
10.3 ... ABC Analysis ... 271
10.4 ... K-Means Cluster Analysis ... 275
10.5 ... Silhouette ... 290
10.6 ... An Example of the Silhouette in the PAL ... 291
10.7 ... An Example of Validate K-Means in the PAL ... 292
10.8 ... Choosing the Initial Cluster Centers ... 294
10.9 ... Categorical Data and Numeric Cluster Analysis ... 296
10.10 ... Self-Organizing Maps ... 298
10.11 ... The Business Case for Cluster Analysis ... 309
10.12 ... Strengths and Weaknesses of Cluster Analysis ... 310
10.13 ... Summary ... 311

11. Classification Analysis ... 313


11.1 ... Introduction to Classification Analysis ... 313
11.2 ... Applications of Classification Analysis ... 314
11.3 ... An Introduction to Regression Analysis ... 315
11.4 ... An Introduction to Decision Trees ... 317
11.5 ... An Introduction to Nearest Neighbors ... 321
11.6 ... Summary ... 324

PART IV: Classification Analysis ... 325

12. Classification Analysis--Regression ... 327


12.1 ... Bi-Variate Linear Regression ... 327
12.2 ... Bi-Variate Geometric, Exponential, and Logarithmic Regression ... 345
12.3 ... Multiple Linear Regression ... 357
12.4 ... Multiple Exponential Regression ... 363
12.5 ... Polynomial Regression ... 368
12.6 ... Logistic Regression ... 373
12.7 ... The Business Case for Regression Analysis ... 384
12.8 ... Strengths and Weaknesses of Regression Analysis ... 384
12.9 ... Summary ... 385

13. Classification Analysis--Decision Trees ... 387


13.1 ... Introduction to the Decision Trees Algorithm ... 387
13.2 ... CHAID Analysis ... 390
13.3 ... The C4.5 Algorithm ... 406
13.4 ... CNR Tree--Classification and Regression Trees ... 415
13.5 ... Decision Trees and Business Rules ... 424
13.6 ... Strengths and Weaknesses of Decision Trees ... 426
13.7 ... Summary ... 426

14. Classification Analysis--K Nearest Neighbor ... 427


14.1 ... Introduction ... 427
14.2 ... Worked Example ... 428
14.3 ... Strengths and Weaknesses of the KNN Algorithm ... 437
14.4 ... Summary ... 438

PART V: Advanced Predictive Analysis ... 439

15. Time Series Analysis ... 441


15.1 ... Introduction to Time Series Analysis ... 441
15.2 ... Time Series Patterns ... 443
15.3 ... Naïve Methods ... 445
15.4 ... Single Exponential Smoothing ... 446
15.5 ... Double Exponential Smoothing ... 453
15.6 ... Triple Exponential Smoothing ... 460
15.7 ... Bi-Variate Linear Regression ... 467
15.8 ... The Business Case for Time Series Analysis ... 470
15.9 ... Strengths and Weaknesses of Time Series Analysis ... 470
15.10 ... Summary ... 471

16. Text Analysis and Text Mining ... 473


16.1 ... Introduction ... 473
16.2 ... Applications ... 474
16.3 ... Full Text Search ... 475
16.4 ... Fuzzy Search ... 481
16.5 ... Text Mining and Text Analysis ... 484
16.6 ... The Business Case for Text Analysis and Text Mining ... 496
16.7 ... Summary ... 496

17. Customer Applications ... 497


17.1 ... eBay ... 497
17.2 ... MKI Japan ... 499
17.3 ... CISCO ... 499
17.4 ... CIR Foods ... 500
17.5 ... Home Shopping Europe 24 ... 501
17.6 ... Bigpoint ... 502
17.7 ... Other Customer Use Cases ... 503
17.8 ... Summary ... 513

Appendices ... 515

A ... References and Resources ... 515
B ... The Author ... 519

Index ... 521
 


Notă biografică

John MacGregor has over 30 years of practical business and teaching experience in the world of predictive analysis. He has worked at Unilever, lectured at the University of London, and served as the Master's program chair. He developed the Statistical Analysis module for Pilot EIS product and Data Mining for Pilot Software, OLAP Server; was a founding member of the Data Mining team in Gentia Software, UK; and was a product and partner manager for KXEN and then for SPSS/IBM in BOBJ/SAP. He has run several software companies both in the UK and Australia and was the key driver in the creation of the SAP Predictive Analysis product. Jon is currently the vice president and head of the Centre of Predictive Analysis for SAP HANA. John regularly presents at SAPPHIRE, TechEd, ASUG and Predictive Analysis World, and writes a predictive analysis newsletter