For validation Document version 0.1 7/7/2014 · Document version 0.1 – 7/7/2014 . ... To consume BEx Queries on SAP Predict Analysis, select “New Dataset -> Query with SQL”

Consumption of OData Services of OIA Dashboard using SAP Predictive Analysis (Version 1.17.0)

Consumption of OData Services of Open Items Analytics Dashboard using SAP Predictive Analysis

(Version 1.17)

For validation

Document version 0.1 – 7/7/2014


Contents What is SAP Predictive Analytics? ...................................................................................................... 3

Predictive Steps ............................................................................................................................. 3

Capabilities of SAP Predictive Analysis ........................................................................................... 4

What is SAP Lumira? .......................................................................................................................... 4

Step by Step Implementation ............................................................................................................ 4

Prerequisites ................................................................................................................................. 4

Generation of SAP BW Queries and OData ................................................................................. 4

Activation Steps of SAP BW Queries and OData ......................................................................... 6

Consumption of OData Services using SAP Predictive Analysis ....................................................... 7

Acquisition ................................................................................................................................. 7

Transformation ........................................................................................................................ 10

Prediction ................................................................................................................................ 11

Visualization ............................................................................................................................ 17

Composing ............................................................................................................................... 21

Sharing .................................................................................................................................... 26


What is SAP Predictive Analytics? “Predictive analysis helps connect data to effective action by drawing reliable conclusions about current conditions and

future events.” ~ Gareth Herschel, Research Director, Gartner Group

SAP Predictive Analytics is a tool of extracting information from existing data sets in order to determine patterns and

predict future outcomes and trends. It is based on a predictive model in which an equation or algorithm or set of rules are

used to predict an outcome based on input data.

SAP Predictive Analysis is ‘Data driven approach to problem solving‘. It focuses on business objectives and leverages

organizational data. It uses input data to predict the outcome.

Predictive Steps 1. Data Loading

2. Data Preparation

3. Data Processing

4. Data Visualization and Sharing

The steps can be visualised as:

Step 1 : Data Loading

Step 2 : Data Preparation

Step 3 : Data Processing

Step 4 : Data Visualization &

Sharing

Data Loading:

Understand the business and the issue

Load the data

Data Preparation

Visualize and examine the data

Sample, filter, merge,

append, apply formulas

Data Processing

Define the model via clustering, classification, association, time series, etc.

Run the model

Data Visualization and Sharing Visualize the model for better

understanding

Store the model and results


Capabilities of SAP Predictive Analysis Simplified User Interface for Predictive Analytics

In Memory predictive analytics in SAP HANA

Support for Open Source R algorithms o Dialogue driver interface for R algorithm

Reads data from a variety of sources o SAP BusinessObjects Universe o Personal data files – CSV or Excel o Relational database tables/views

Intuitive visualizations o Exploratory Data Analysis o Predictive Analytics results visualization o Predictive Model visualization

What is SAP Lumira? SAP Lumira is an exciting new tool that allows you to quickly and easily create engaging visualizations that combine data from multiple sources into a single view. An intuitive UI featuring drag and drop functionality allows everyone to tell a great data story. SAP Predictive Analysis created predictive results can be integrated into SAP Lumira; hence SAP Lumira is a visualization tool in which it is a part of SAP Predictive Analysis. It involves the following steps:

Step by Step Implementation

Prerequisites Transformation of a SAP BEx query into OData services which can be accessed using MDX.

Generation of SAP BW Queries and OData

Activation of SAP BW Queries and OData

OData Analytics service can be generated using SAP NetWeaver Gateway Service Builder (Transaction - SEGW).

Generation of SAP BW Queries and OData 1. Run transaction “SEGW”

2. Create a New Project in Service Builder

Create a Project


3. Right click on “Data Model” and select from the context menu "Redefine->BW Query Service"

4. A three step wizard is displayed

First Step

1. Choose the Access Type as "Controller for Easy Queries (SAP BW)"

2. Press F4 on field Cube to select the Cube of type query that you wish to convert to an OData Service.

Right click on Data Model to redefine BW

Query Service

Choose Access Type –

Controller For Easy Queries

Choose query from BW system


Second Step You could choose to leave the screen with default values. Just fill in a meaningful description for the OData Model and OData service. In this step you specify the OData Model Provider and Data Provider class names and OData Model and OData Service names.

Final Step Select all Entity Types proposed by the system and press “Finish”. The service should be successfully generated.

Activation Steps of SAP BW Queries and OData The next step is to activate the generated OData Analytic Service. Logon to SAP NetWeaver Gateway Hub and execute the transaction” /IWFND/MAINT_SERVICE “to activate the service. The OData service created for the query has one main Entity type by which the result of the query is performed.

Provide suitable Names for

Model Provider, Data

Provider, OData Model and

OData Service or use Default

values proposed by the

system

Select all Entity types

Finish Generation


The metadata document for one of the services created can be accessed using $metadata OData Command as shown below:

The metadata document for the service can be accessed using $metadata OData Command. The image below shows how

the Dimensions, Dimension Attributes and Measures are represented as properties of an Entity Type.

Consumption of OData Services using SAP Predictive Analysis

Acquisition

To consume BEx Queries on SAP Predict Analysis, select “New Dataset -> Query with SQL” Generic OData 2.0 appears on the right panel as seen above, click “Next”.


Select a Database “Generic OData 2.0 – OData Connector”. Click “Next”.

Enter the values for the fields

Service Root URI (OData service)

User Name

Password Click “Connect”.


Expand the node “Entities” and the click “Preview”.

Select which columns to be displayed and click “Create”.


Once acquisition is done, click “Finish”.

Transformation Go to “Prepare” Tab, the following tasks can be performed.

The data set can be enriched. Measures, Geographic and Time elements are automatically detected.

Data are manipulated and cleansed with no scripts.

All transformations are replayed when new data is loaded.

Time elements are automatically detected

here (Year, Quarter, and Month)


Prediction Two algorithms are used.

1. R-NNet Neural Network 2. R-K-Means

Go to “Predict” tab, the dataset is already there on the workspace.

1. R-NNet Neural Network

Select the Algorithm “R-NNET NEURAL NETWORK” and drag it to the dataset.


Click ‘gear’ icon on R-NNet Neural Network icon and select “Configure Settings”

Use this algorithm for forecasting, classification, and statistical pattern recognition using R library functions. Go to “Properties”: Select the mode in which you want to display the output data. Possible values:

Trend: Predicts the values for the dependent column and adds an extra column in the output containing the predicted values. Output Mode – Trend. Independent Columns - Select input source columns. Dependent Column - Select the target column. Hidden Layer Neurons -Enter the number of nodes/neurons in the hidden layer. Enter a name for the newly created column that contains the predicted values. Click “Done”. After successful setting, click “Run”.


Click ‘OK’.

Result:

The result is in the form of ‘Data Grid’, there are different data insights for this algorithm.

Statistical Summary Chart

Parallel Coordinate Chart

Scatter Matrix Chart

R K-Means Algorithm

Use this algorithm to cluster observations into groups of related observations without any prior knowledge of those relationships. The algorithm clusters observations into k groups, where k is provided as an input parameter. The algorithm then assigns each observation to clusters based on the proximity of the observation to the mean of the cluster. The process continues until the clusters converge.


Note: • You might obtain a different cluster number for each cluster each time you execute the R-K-Means algorithm. However, the observations in each cluster remain the same. • Creating models using the R-K-Means algorithm is not supported.

Select the Algorithm “R- K-Means” and drag it to the dataset.

Click ‘gear’ icon on R K-Means icon and select “Configure Settings”


Go to “Properties” tab and enter as follows:

Output Mode- Select the mode in which you want to display the output data. Independent- Columns Select the input source columns. Number of Clusters - Enter the number of groups for clustering. Enter a name for the newly created column that contains the cluster name. Go to “Advanced” tab and enter as follows:

Maximum Iterations- Enter the number of iterations allowed for finding clusters. Number of Initial Sets - Enter the number of random initial sets for clustering (n start). Algorithm - Select the type of algorithm to be used for performing K-Means clustering.


The data grid for the dataset is shown as:

The result can be displayed using different representation as such as:

Cluster distribution

Cluster Density and Distance

Feature Distribution

Cluster Center Representation

Parallel Co-ordinate Chart

Scatter Matrix Chart Here it is Cluster Distribution representation of the result.


Save as Model

In order to save the result, click on the algorithm icon and select “Save as Model”.

Visualization

Easily build interactive visualization.

Chart manipulation with trends, forecast, rank, sort and calculated measures.

Ascending or descending sorts on measures. Go to “Visualize” tab .

There are measures and dimensions on the left pane which correspond to the fields taken from Characteristic Catalog

and Key Figures Catalog in BEx Query.

For Y Axis, only “Measures” can be added, in case if some fields in the “Dimensions” are required to be added to the Y

Axis, convert them to Measures.


Click on ‘gear’ icon (Options) and select ‘Create a measure’.

It will create a copy of the field in the “Measures”, thus in turn can be added to the list in the Y Axis.

For X Axis, the columns are taken from the Dimensions, in case if the fields from the measures are needed to be taken

into the X Axis, as mentioned earlier, the field in the Measures, needs to be converted to “Dimensions”, which in turn

can be added to X Axis.

Options


For Legend Color, we can pick only from Dimensions.

Time Hierarchy

To break the date into its components as such as Year, Quarter, Month, Week, Days, click on Options button next to

the field in the Dimensions section and select “Create a time hierarchy” in the context menu.

The components of the date are displayed below Dimensions.


To have the time hierarchy components appearing on the Dimensions and which can be added to X Axis, they need to

be available as the separate fields under the Dimensions.

Click on Options button next to ‘Dimensions’ and click “Show Hidden Dimensions”.

The components of Time are added as seen:

These in turn can be added to the X Axis.


In the latest version of SAP Predictive Analysis(version 1.18), a new feature is added where Measures can be used as

Dimensions.

Another new feature is added for which the time hierarchy components can be used in the X Axis.

Composing Go to “Compose” tab. Create a new storyboards. The saved visualizations are seen on the left pane of the workspace.

Click “Add Page” button at the bottom left.

“Page Type” window appears.


Select the type of page and click “Create” button.

Select the saved visualization and drag it to the Compose pane.

1

2


To add the filters, go to “Input Controls” tab on the left and select the field and drag it to the preferred position on the

Compose pane.

To add another page in the same storyboard, click “Add Page” button at bottom left of the Compose pane.


To add background image, click “+ Add “link on the “PAGE SETTINGS” section and select the image.

To adjust the transparency of the background image, drag the peg along the number line.


To change the board title, place the cursor next to the default title ‘BOARD TITLE’ and change the text according to your

liking.

To save the document, go to “File->Save”.


Sharing Save the document in SAP Lumira Cloud so it can be shared.

There are many ways of sharing such as:

Export as File

Publish to SAP HANA

Publish to Explorer

Publish to SAP Lumira Cloud

Publish to SAP Lumira Server Publish to Stream Work

In this way a Story Board which shows all the scenarios of Open Items Analytics dashboard, is available in the SAP Lumira Cloud and can be shared easily.

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Documents

For validation Document version 0.1 7/7/2014 · Document version 0.1 – 7/7/2014 . ... To consume BEx Queries on SAP Predict Analysis, select “New Dataset -> Query with SQL”