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by Sebastian Muszytowski, Cedrine Madera, Steven LaFalce, Ravi Kumar, Willie Favero
Enabling Real-time Analytics on IBM z Systems Platform
Front cover
Notices
Trademarks
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Preface
Authors
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Chapter 1. Executive overview
1.1 Introduction
1.2 Real-time analytics
1.2.1 Business advantages
1.2.2 IT advantages
1.3 In-database analytics
1.3.1 Accelerated in-database transformation
1.3.2 Accelerated in-database predictive modeling
1.4 Enabling applications with machine learning capability
1.5 Value propositions
1.6 Related products
1.7 Use cases
1.7.1 Countering payment fraud and financial crimes
1.7.2 Insurance claims in-process payment analytics
1.7.3 Predictive customer intelligence
Chapter 2. Analytics implementation on z Systems platform
2.1 Adding analytics to a mainframe data sharing environment
2.1.1 SPSS Modeler
2.1.2 DB2 Analytics Accelerator wrapper stored procedure
2.1.3 Using accelerator-only tables
2.2 Installation and customization
2.2.1 DB2 and DB2 Analytics Accelerator setup and installation
2.2.2 Required DB2 privileges for SPSS users
2.2.3 SPSS Modeler client
2.2.4 SPSS Modeler server
2.2.5 Data sources in SPSS
2.2.6 AQT_ANALYTICS_DATABASE variable
2.2.7 User management in the SPSS Modeler server
2.2.8 Installing SPSS Modeler scoring adapter for DB2 z/OS
2.3 Real-time analytics lifecycle
2.3.1 Swim lane diagram of in-database analytics lifecycle
2.3.2 Interaction between a DB2 DBA and a data scientist
2.3.3 Key strengths of various components
Chapter 3. Data integration using IBM DB2 Analytics Accelerator Loader for z/OS
3.1 Functional overview
3.1.1 Loader v2.1 enhancements
3.1.2 Loader methods to move data
3.1.3 Components and interfaces
3.2 Getting started
3.2.1 Installation
3.2.2 Customization
3.2.3 Workload Management (WLM) performance goals
3.2.4 IBM z Systems Integrated Information Processor (zIIP)
3.2.5 z Systems advantage
3.2.6 Parallelism
3.3 Scenarios
3.3.1 ACCEL_LOAD_TASKS
3.3.2 Sequential Input IDAA_ONLY and IDAA_DUAL
3.3.3 Load RESUME
3.3.4 IBM DB2 Analytics Accelerator Loader image copy input
3.3.5 VSAM
3.4 System Management Facility (SMF)
Chapter 4. Data transformation
4.1 Introduction
4.1.1 Accelerator-only table (AOT)
4.1.2 Enabling in-database processing on SPSS Modeler client
4.2 SQL pushback in SPSS Modeler
4.2.1 How SQL generation works
4.2.2 Where improvements can occur with IDT using Accelerator
4.3 Nodes supporting SQL generation for DB2 Accelerator
4.3.1 Source palette tab
4.3.2 Record Ops palette tab
4.3.3 Field Ops palette tab
4.3.4 Graphs palette tab
4.3.5 Database Modeling (Nuggets) palette tab
4.3.6 Output palette tab
4.3.7 Export palette tab
4.4 In-database analytics Processing effort by components
4.4.1 SPSS Modeler client (running on Windows)
4.4.2 SPSS Modeler server
4.4.3 DB2 for z/OS with Analytics Accelerator
4.4.4 Netezza based DB2 Analytics Accelerator for z/OS
4.5 SPSS data transformation stream
4.5.1 SQL Preview option to verify in-database processing
4.5.2 Why are my nodes purple
4.5.3 Enable cache at node level
4.6 Stream messages
4.7 Data transformation using DataStage
Chapter 5. Data modeling using SPSS and DB2 Analytics Accelerator
5.1 Introduction
5.2 SPSS data modeling stream design
5.2.1 SPSS data modeling stream
5.2.2 Example of how to optimize pruning a decision tree
5.3 Adding record IDs to input tables
5.4 Combining in-database transformation on Accelerator withtraditional model creation
Chapter 6. Model scoring
6.1 In-transactional scoring (single-record scoring)
6.2 In-database scoring and scoring adapter
6.2.1 Real-time scoring and near real-time scoring
6.2.2 Export PMML from SPSS model apply node
6.3 Batch scoring
6.3.1 Complete batch scoring example with accelerated in-databasepredictive modeling
6.3.2 Batch scoring using Scoring Adapter into accelerator-only tables (less efficient method)
6.3.3 Batch scoring using Scoring Adapter into z/OS DB2 base tables (more efficient method)
6.4 DMG and PMML
6.4.1 Zementis and in-application scoring
6.5 Scoring and smart decisions
6.5.1 Solution architecture
6.5.2 Operational Decision Manager overview
6.5.3 SPSS Collaboration and Deployment Services
6.5.4 SPSS Scoring Adaptor for DB2 z/OS
6.6 Examples of integration for scoring solutions using ODM on z/OS
6.6.1 Example: Calling the SPSS Scoring Adapter for IBM DB2 for z/OS
6.6.2 Example: Calling SPSS Collaboration and Deployment Services
Chapter 7. Acceleration for descriptive analytics: IBM DB2 Analytics Accelerator value with IBM Cognos BI
7.1 Descriptive analytics
7.2 IBM Cognos Business Intelligence
7.3 IBM Cognos BI 10 and DB2 Analytics Accelerator and z13
Chapter 8. Using R with the IBM DB2 Analytics Accelerator
8.1 Introduction to the R programming language
8.2 Prerequisites
8.3 Using RJDBC with the IBM DB2 Analytics Accelerator
8.3.1 RJDBC functions
8.4 Using RODBC with the IBM DB2 Analytics Accelerator
8.4.1 RODBC functions
8.5 Using accelerator-only tables (AOTs) with R
8.6 Using Netezza Analytics stored procedures
8.6.1 Basic concept
8.6.2 Helper stored procedures
8.6.3 Decision trees
8.6.4 Regression trees
8.6.5 K-means clustering
8.6.6 TwoStep clustering
8.6.7 Naive Bayes
8.7 Performance considerations
Appendix A. ODBC connectivity between SPSS Modeler and DB2 for z/OS
ODBC related terms and concepts
ODBC Driver Manager
ODBC gateway mode
ODBC driver used by SPSS Modeler server
Installation and configuration of IBM DB2 ODBC driver
SPSS Modeler configuration for use of DB2 ODBC driver
Installation and configuration of SPSS SDAP driver
Appendix B. Job to install wrapper stored procedures
JCL job sample
IBM Netezza Analytics stored procedures
Related publications
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Enabling Real-time Analytics on IBM z Systems Platform
Back cover
Acrobat bookmark
ISBN 0738441864
SG24-8272-00
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