HANA SPS07 App Function Library

What´s New? SAP HANA SPS 07
SAP HANA Application Function Library (AFL)
(Delta from SPS 06 to SPS 07)
SAP HANA Product Management

November, 2013

Contents


Overview



What’s New for SPS 07: Predictive Analysis Library



What’s New for SPS 07: Application Function Modeler

© 2013 SAP AG. All rights reserved.

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Application Function Library: General Overview – 1Application Function Library (AFL) – what is it?
Pre-delivered commonly utilized business, predictive and other types of algorithms for use in projects
or solutions that run on SAP HANA. The technology framework enabling the use of these algorithms
is called the Application Function Library .
What are it’s benefits?

These algorithms can be leveraged directly in development projects, speeding up projects by
avoiding writing custom complex algorithms from scratch. AFL operations also offer very fast
performance, as AFL functions run in the core of SAP HANA in-memory DB.
What are its primary libraries?
The Predictive Analysis Library (PAL) is a set of functions in the AFL. It contains pre-built, parameterdriven, commonly used algorithms primarily related to predictive analysis and data mining.
The Business Function Library (BFL) is a set of functions in the AFL. It contains pre-built, parameterdriven, commonly used algorithms primarily related to finance.

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The Application Function Library Framework in SAP HANA
AFL Technology includes

HANA Clients (App Server, Analytics Technology, etc)

Application Functions
Written in C++ and delivered as AFL content by SAP
Predictive Analysis and Business Function Library have been
released in SPS 05 as AFL content

AFM
SAP HANA
SQLScript
Parameter Table

AFL Framework
On demand library loading framework for registered and supported
libraries
AFL are consumed for use from SqlScript via so-called wrapperprocedures. Consumption can be controlled via permissions.
Beyond the initial script-based approach, the Application
Function Modeler is released with SPS 06 as a graphical editor
to facilitate the design-time process of creating the
wrapper-procedures and can easily be re-used as part of
development workflow.


AFL Framework

Application Functions (C++)

Business Function
Library

Predictive Analysis
Library

…

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Predictive Analysis Library – General Overview
Predictive Analysis Library (PAL)
• Compiled analytic function library for predictive analysis in HANA SPS 05
• Support multiple algorithms: K-Means, Association Analysis, C4.5 Decision Tree, Multiple
Linear Regression, Exponential Smoothing…
Help Customers To
Know Your Business

Decide with Confidence

Compute Quickly

Empower the business

Uncover deep insights & patterns about the business: association rules,
customer clustering, or sales prediction
Drive more advanced analyses. Decision is made with support from
analysis numbers
Query and analyze data in real-time with high-performance
computation in-memory
Bring decision support capabilities to the business users through
simplified experience and pre-built scenarios
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The Predictive Analysis Library in SAP HANA
Functional Overview as of SPS 06
The Predictive Analysis Library (PAL) is a built-in C++ library to perform in-database data mining
and statistical calculations, designed to provide excellent performance on large data sets.
Embedded in SAP HANA via the Application Function Library
Classification Analysis
C4.5 Decision Tree Analysis
CHAID Decision Tree Analysis
K Nearest Neighbour
Multiple Linear Regression
Polynomial Regression
Exponential Regression
Bi-Variate Geometric Regression
Bi-Variate Logarithmic Regression
Logistic Regression
Naïve Bayes

Data Preparation
Sampling
Binning
Scaling
Convert Categorical to Binary

Outlier Detection
Inter-Quartile Range Test (Tukey’s Test)
Variance Test
Anomaly Detection

Cluster Analysis
Association Analysis
Apriori
Apriori Lite


ABC Classification
DBSCAN
K-Means
Kohonen Self Organized Maps

Status: SAP HANA SPS 06.

Time Series Analysis
Single Exponential Smoothing
Double Exponential Smoothing
Triple Exponential Smoothing

Link Prediction
Different Measurements: Common
Neighbors; Jaccard’s Coefficient;
Adamic/Adar; Katzβ

Other
Weighted Scores Table
Y
X

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Application Function Modeler
Application Function Modeler
A graphical editor to facilitate a faster and easier design-time process of creating the wrapper-procedures
AFL Models are stored as repository objects and can easily be re-used as part of development workflow.

Model Editor
Drag‘n drop of functions
Template for table types
Data source selection and
automatic mappings to table
types
Sample SQL for
procedure
consumption


Library Selection

Function List and
Search

Parameters and
specifications
for table types

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What’s New: Predictive
Analysis Library

New Algorithms: Statistical Functions
Univariate
 Mean, median, variance, standard deviation
 Kurtosis
 Skewness

Multivariate
 Covariance matrix
 Pearson correlations coefficient matrix
 Chi-squared tests:
– test of quality of fit
– test of independence
 Variance equal test (F-test)


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New Algorithms: Substitute Missing Values; Partitioning
Substitute missing values
 Continuous variable: replace the ‘null’ value with ‘mean’ or ‘median’
 Categorical variable: replace the ‘null’ value with ‘mode’

Partitioning
 Splits an input table into 3 parts (training table, testing table and validation table).
Union could be a subset of the input table and the validation part could be empty
 Two partition methods are supported:
– Random Partition
– Stratified Partition (stratified sampling)


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New Algorithms: Support Vector Machine; Forecast Smoothing
Support Vector Machine
 A family of supervised machine learning algorithms that analyze data and
recognize patterns, typically used for classification and regression analysis
 Holds advantages over other supervised learning models: SVM models can be
either linear or non-linear

Forecast Smoothing
 Calculates a set of optimal parameters for PAL functions: Single Smoothing,

Double Smoothing and Triple Smoothing
 Outputs the forecasting result by using the best parameter
 Parameter optimization is based on global and local search algorithms


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New Clustering Algorithms: Agglomerate Hierarchical; Affinity Propagation

Agglomerate Hierarchical Clustering
 Widely used clustering method which can find natural groups within a set of data
 Groups the data into a hierarchy or a binary tree of the subgroups
 The PAL version follows the agglomerate approach: merges the clusters with a
bottom-up strategy.

Affinity Propagation Clustering
 A relatively new clustering algorithm based on the concept of "message passing"
between data points
 Holds the advantage that the number of clusters does not necessarily have to be
predetermined


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Enhancements to Regression Algorithms

Linear regression
 Support p-value for each coefficient
 Stepwise linear regression (forward and backward only)

Logistic regression
 Logistic regression with L1 regularization
 Categorical support for input variable
 p-value for coefficient


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Enhancements to Other Algorithms

Apriori
 Output rules in relational format
 Add lift as a filter for rules
 Provide an option which controls the number of items in right hand side (1 or many)

Decision Tree
 Missing value handling in decision tree (mean/mode)


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What’s New: Application
Function Modeler

Application Function Modeler SPS 07 Features Overview
Objectives
Drive AFM achieve a more stable and usable solution
 Better integrate with AFL Framework.
 UI Improvement: more friendly and usable

 Better support the E2E Development Scenarios: create, activation, calling, delivery etc.
 Others: Performance
AFL Framework improvement, Error Handling

UI Improvement






 Better support the table type editing
 Better naming rule and icon
 Better UI Text

Update table type constrains and default parameter
Support new function in PAL (11 new functions)
Use new AFL API to generate wrapper procedure
Refactor the data model

E2E Development Scenarios

Other







 Performance improvement: creation, model consistence
check
 Error Message: in studio side and server side
 Support open file offline but read-only
 …

View support
Better undo/redo
Calling parameter remove/add/restore/persistence
Schema mapping
…


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AFM: New Functions
 11 new functions will be supported
New Algorithm in AFM in SPS 07 Description
DBSCAN
DBSCAN
LINKPREDICTION
Link Prediction
Convert category Type to
CONV2BINARYVECTOR
Binary Vector
SLIGHTSILHOUETTE
Slight Silhouette
NBCTRAIN
NBCTRAIN
NBCPREDICT
NBCPREDICT
HCAGGLOMERATE
Hierarchical Clustering
Train with Support Vector
SVMTRAIN
Machines
Predict with Support Vector
SVMPREDICT
Machines
PARTITION
Data Partition
SUBSTITUTE_MISS
Substitute Missing Values
….


Category
Clustering
Social network analysis

AFM
SPS 07
SPS 07

PAL
SPS 06
SPS 06

Preprocessing

SPS 07

SPS 06

Clustering
Classification
Classification
Clustering

SPS 07
SPS 07
SPS 07
SPS 07

SPS 06
SPS 06
SPS 06
SPS 07

Classification

SPS 07

SPS 07

Classification

SPS 07

SPS 07

Preprocessing
Preprocessing

SPS 07
SPS 07

SPS 07
SPS 07

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AFM: Updated Generated Procedure Definition

 New AFL Generator API in Runtime Plugin



SPS 06: CREATE PROCEDURE ... LANGUAGE LLANG AS BEGIN ...
SPS 07: CREATE PROCEDURE ... LANGUAGE AFLLANG
SQL SECURITY INVOKER READS SQL DATA AS BEGIN ..


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AFM: Table Type Editor & Parameter Editing


Table Type Editor



Parameter Editing

 Support user add/remove parameter
 Provide restore defaults action

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AFM: New Error Messages


Provide better error messages and provide the possible next step

Case
No required permission
Table Type Configuration Error
Calling with activation
…
No schema sleeted
No Function Selected
Table Type has not been initialized
File corrupt

…


Error Message
(Client Side)
User does not have the AFL__SYS_AFL_AFLBFL_EXECUTE role. Assign the role first.
Table column setting does not match function specification
AFL Model (.aflpmml) file has not been activated. Activate the file before calling it.
…
(Server side Activation)
Repository: Error in repository runtime extension. No schema selected. Select a schema.
Repository: Error in repository runtime extension. No function in the model. Select a function.
Repository: Error in repository runtime extension. Add columns to the Dataset table.
Repository: Error in repository runtime extension. Invalid file. Check the file or create the file again.

…

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Disclaimer
This presentation outlines our general product direction and should not be relied on in making
a purchase decision. This presentation is not subject to your license agreement or any other
agreement with SAP.
SAP has no obligation to pursue any course of business outlined in this presentation or to
develop or release any functionality mentioned in this presentation. This presentation and
SAP’s strategy and possible future developments are subject to change and may be changed
by SAP at any time for any reason without notice.
This document is provided without a warranty of any kind, either express or implied, including
but not limited to, the implied warranties of merchantability, fitness for a particular purpose, or
non-infringement. SAP assumes no responsibility for errors or omissions in this document,
except if such damages were caused by SAP intentionally or grossly negligent.


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Thank you
Contact information
Ron Silberstein
SAP HANA Product Management
AskSAPHANA@sap.com
To get the best overview of what’s new in SAP HANA SPS 07, read this blog.

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