SAP Predictive Analytics
The Comprehensive Guide
(Sprache: Englisch)
Leave your crystal ball behind and peer into the future with SAPPredictive Analytics! Master predictive models-regression, time series forecasting, clustering, and more-and learn how to get SAP Predictive Analytics up and running. Discover the essential...
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Klappentext zu „SAP Predictive Analytics “
Leave your crystal ball behind and peer into the future with SAPPredictive Analytics! Master predictive models-regression, time series forecasting, clustering, and more-and learn how to get SAP Predictive Analytics up and running. Discover the essential tools, from Predictive Factory and the Automated Modeler to the Data Manager and Social Network Analysis. Get predictive analysis working for you!Highlights include:
Predictive models
Classification models
Regression models
Time-series forecasting models
Predictive Factory
Automated Analytics
Expert Analytics
Social Network Analysis (SNA)
Data Manager
Installation
Integration
Inhaltsverzeichnis zu „SAP Predictive Analytics “
... Preface ... 17... Objective ... 17
... Target Audience ... 18
... Structure of This Book ... 19
I ... Getting Started ... 21
1 ... An Introduction to Predictive Analytics ... 23
1.1 ... The Importance of Predictive Analysis ... 24
1.2 ... Predictive Analysis: Prescriptive and Exploratory ... 26
1.3 ... Preparing for a Successful Predictive Analysis Project ... 32
1.4 ... Industry Use Cases ... 37
1.5 ... Summary ... 39
2 ... What Is SAP Predictive Analytics? ... 41
2.1 ... Building Predictive Models with SAP Predictive Analytics ... 41
2.2 ... Automated Analytics and Expert Analytics ... 42
2.3 ... Mass Production of Predictive Models with the Predictive Factory ... 43
2.4 ... Data Preparation ... 45
2.5 ... Additional SAP Predictive Analytics Capabilities ... 47
2.6 ... Summary ... 48
3 ... Installing SAP Predictive Analytics ... 49
3.1 ... Recommended Deployments ... 50
3.2 ... Installing the SAP Predictive Analytics Server ... 51
3.3 ... Installing the SAP Predictive Analytics Client ... 57
3.4 ... Installing the Predictive Factory ... 61
3.5 ... Installing SAP Predictive Analytics Desktop ... 71
3.6 ... SAP HANA Installation Steps ... 74
3.7 ... Summary ... 75
4 ... Planning a Predictive Analytics Project ... 77
4.1 ... Introduction to the CRISP-DM Methodology ... 78
4.2 ... Running a Project ... 81
4.3 ... Summary ... 100
II ... The Predictive Factory ... 101
5 ... Predictive Factory ... 103
5.1 ... Predictive Factory: End-to-End Modeling ... 103
5.2 ... Creating a Project ... 105
5.3 ... External Executables ... 107
5.4 ... Variable Statistics ... 110
5.5 ... Summary ... 110
6 ... Automated Predictive Classification Models ... 111
6.1 ... Introducing Classification Models ... 112
6.2 ... Creating an Automated Classification Model ... 115
6.3 ... Understanding and Improving an
... mehr
Automated Classification Model ... 123
6.4 ... Applying an Automated Classification Model ... 139
6.5 ... The Data Science behind Automated Predictive Classification Models ... 149
6.6 ... Summary ... 172
7 ... Automated Predictive Regression Models ... 173
7.1 ... Introducing Regression Models ... 173
7.2 ... Creating an Automated Regression Model ... 174
7.3 ... Understanding and Improving an Automated Regression Model ... 179
7.4 ... Applying an Automated Regression Model ... 184
7.5 ... Summary ... 191
8 ... Automated Predictive Time Series Forecasting Models ... 193
8.1 ... Creating and Understanding Time Series Forecast Models ... 194
8.2 ... Mass Producing Time Series Forecasts ... 214
8.3 ... Productizing the Forecast Model ... 221
8.4 ... The Data Science behind Automated Time Series Forecasting Models ... 227
8.5 ... Summary ... 231
9 ... Massive Predictive Analytics ... 233
9.1 ... Deploying Predictive Models in Batch Mode ... 234
9.2 ... Model Quality and Deviation ... 241
9.3 ... Automatically Retraining Models ... 244
9.4 ... Scheduling and Combining Massive Tasks ... 250
9.5 ... Deploying Expert Analytics Models ... 253
9.6 ... Summary ... 256
III ... Automated Analytics ... 257
10 ... Automated Analytics User Interface ... 259
10.1 ... When to Use Automated Analytics ... 259
10.2 ... Navigating the User Interface ... 260
10.3 ... Exploring the Automated Analytics Modules ... 264
10.4 ... Summary ... 269
11 ... Automated Predictive Clustering Models ... 271
11.1 ... The Clustering Approach of Automated Analytics ... 272
11.2 ... Creating a Clustering Model ... 273
11.3 ... Supervised and Unsupervised Clustering ... 297
11.4 ... The Data Science behind Automated Clustering Models ... 299
11.5 ... Summary ... 300
12 ... Social Network Analysis ... 301
12.1 ... Terminology of Social Network Analysis ... 302
12.2 ... Automated Functionalities of Social Network Analysis ... 303
12.3 ... Creating a Social Network Analysis Model ... 306
12.4 ... Navigating and Understanding the Social Network Analysis Output ... 323
12.5 ... Colocation and Path Analysis Overview ... 340
12.6 ... Conclusion ... 346
13 ... Automated Predictive Recommendation Models ... 347
13.1 ... Introduction ... 348
13.2 ... Using the Social Network Analysis Module ... 353
13.3 ... Using the Recommendation Module ... 368
13.4 ... Using the Automated Predictive Library ... 374
13.5 ... Summary ... 375
14 ... Advanced Data Preparation Techniques with the Data Manager ... 377
14.1 ... Data Preparation for SAP Predictive Analytics ... 377
14.2 ... Building Datasets for SAP Predictive Analytics ... 379
14.3 ... Creating a Dataset using the Data Manager ... 381
14.4 ... Additional Functionalities ... 400
14.5 ... Using Data Manager Objects in the Modeling Phase ... 408
14.6 ... Managing Metadata ... 408
14.7 ... SQL Settings ... 411
14.8 ... Summary ... 412
IV ... Advanced Workflows ... 413
15 ... Expert Analytics ... 415
15.1 ... When to Use Expert Analytics ... 415
15.2 ... Navigating the Expert Analytics Interface ... 416
15.3 ... Understanding a Typical Project Workflow ... 417
15.4 ... Creating an Expert Analytics Predictive Model ... 427
15.5 ... Exploring the Available Algorithms ... 438
15.6 ... Extending Functionality with R ... 442
15.7 ... Summary ... 448
16 ... Integration into SAP and Third-Party Applications ... 451
16.1 ... Exporting Models as Third-Party Code ... 451
16.2 ... In-Database Integration ... 455
16.3 ... Scripting ... 456
16.4 ... Summary ... 465
17 ... Hints, Tips, and Best Practices ... 467
17.1 ... Improving Predictive Model Quality ... 467
17.2 ... Additional Resources ... 476
17.3 ... Summary ... 478
18 ... Conclusion ... 479
18.1 ... Lessons Learned ... 479
18.2 ... The Future of SAP Predictive Analytics ... 479
18.3 ... Next Steps ... 480
... Appendices ... 481
... The Authors ... 481
... Index ... 483
6.4 ... Applying an Automated Classification Model ... 139
6.5 ... The Data Science behind Automated Predictive Classification Models ... 149
6.6 ... Summary ... 172
7 ... Automated Predictive Regression Models ... 173
7.1 ... Introducing Regression Models ... 173
7.2 ... Creating an Automated Regression Model ... 174
7.3 ... Understanding and Improving an Automated Regression Model ... 179
7.4 ... Applying an Automated Regression Model ... 184
7.5 ... Summary ... 191
8 ... Automated Predictive Time Series Forecasting Models ... 193
8.1 ... Creating and Understanding Time Series Forecast Models ... 194
8.2 ... Mass Producing Time Series Forecasts ... 214
8.3 ... Productizing the Forecast Model ... 221
8.4 ... The Data Science behind Automated Time Series Forecasting Models ... 227
8.5 ... Summary ... 231
9 ... Massive Predictive Analytics ... 233
9.1 ... Deploying Predictive Models in Batch Mode ... 234
9.2 ... Model Quality and Deviation ... 241
9.3 ... Automatically Retraining Models ... 244
9.4 ... Scheduling and Combining Massive Tasks ... 250
9.5 ... Deploying Expert Analytics Models ... 253
9.6 ... Summary ... 256
III ... Automated Analytics ... 257
10 ... Automated Analytics User Interface ... 259
10.1 ... When to Use Automated Analytics ... 259
10.2 ... Navigating the User Interface ... 260
10.3 ... Exploring the Automated Analytics Modules ... 264
10.4 ... Summary ... 269
11 ... Automated Predictive Clustering Models ... 271
11.1 ... The Clustering Approach of Automated Analytics ... 272
11.2 ... Creating a Clustering Model ... 273
11.3 ... Supervised and Unsupervised Clustering ... 297
11.4 ... The Data Science behind Automated Clustering Models ... 299
11.5 ... Summary ... 300
12 ... Social Network Analysis ... 301
12.1 ... Terminology of Social Network Analysis ... 302
12.2 ... Automated Functionalities of Social Network Analysis ... 303
12.3 ... Creating a Social Network Analysis Model ... 306
12.4 ... Navigating and Understanding the Social Network Analysis Output ... 323
12.5 ... Colocation and Path Analysis Overview ... 340
12.6 ... Conclusion ... 346
13 ... Automated Predictive Recommendation Models ... 347
13.1 ... Introduction ... 348
13.2 ... Using the Social Network Analysis Module ... 353
13.3 ... Using the Recommendation Module ... 368
13.4 ... Using the Automated Predictive Library ... 374
13.5 ... Summary ... 375
14 ... Advanced Data Preparation Techniques with the Data Manager ... 377
14.1 ... Data Preparation for SAP Predictive Analytics ... 377
14.2 ... Building Datasets for SAP Predictive Analytics ... 379
14.3 ... Creating a Dataset using the Data Manager ... 381
14.4 ... Additional Functionalities ... 400
14.5 ... Using Data Manager Objects in the Modeling Phase ... 408
14.6 ... Managing Metadata ... 408
14.7 ... SQL Settings ... 411
14.8 ... Summary ... 412
IV ... Advanced Workflows ... 413
15 ... Expert Analytics ... 415
15.1 ... When to Use Expert Analytics ... 415
15.2 ... Navigating the Expert Analytics Interface ... 416
15.3 ... Understanding a Typical Project Workflow ... 417
15.4 ... Creating an Expert Analytics Predictive Model ... 427
15.5 ... Exploring the Available Algorithms ... 438
15.6 ... Extending Functionality with R ... 442
15.7 ... Summary ... 448
16 ... Integration into SAP and Third-Party Applications ... 451
16.1 ... Exporting Models as Third-Party Code ... 451
16.2 ... In-Database Integration ... 455
16.3 ... Scripting ... 456
16.4 ... Summary ... 465
17 ... Hints, Tips, and Best Practices ... 467
17.1 ... Improving Predictive Model Quality ... 467
17.2 ... Additional Resources ... 476
17.3 ... Summary ... 478
18 ... Conclusion ... 479
18.1 ... Lessons Learned ... 479
18.2 ... The Future of SAP Predictive Analytics ... 479
18.3 ... Next Steps ... 480
... Appendices ... 481
... The Authors ... 481
... Index ... 483
... weniger
Autoren-Porträt von Antoine Chabert, Andreas Forster, Laurent Tessier, Pierpaolo Vezzosi
Vezzosi, PierpaoloPierpaolo Vezzosi is director of product management in the SAP Analytics organization. He joined BusinessObjects in 2000 as a technology alliances manager to develop the company relationship with key technological partners and monitor innovating trends, including offshore activities in India. In 2006 he moved to product management in the Semantic Layer area to define the strategy for SAP BusinessObjects BI universes and the Information Design Tool. His current area of focus is predictive analytics and big data. Before joining BusinessObjects, Pierpaolo held several positions in the software industry as an analyst, developer, support engineer, and technical documentation editor. He holds a master's degree in aeronautics and space engineering.
Bibliographische Angaben
- Autoren: Antoine Chabert , Andreas Forster , Laurent Tessier , Pierpaolo Vezzosi
- 2017, 491 Seiten, Maße: 18,4 x 23,6 cm, Gebunden, Englisch
- Verlag: Rheinwerk Verlag
- ISBN-10: 1493215922
- ISBN-13: 9781493215928
- Erscheinungsdatum: 13.02.2018
Sprache:
Englisch
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