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

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 Data Loading: Understand the business and the issue Load the data Data Visualization and Sharing Visualize the model for better understanding Store the model and results Step 4 : Data Visualization & Sharing Step 2 : Data Preparation Data Preparation Visualize and examine the data Sample, filter, merge, append, apply formulas Step 3 : Data Processing Data Processing Define the model via clustering, classification, association, time series, etc. Run the model

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" Right click on Data Model to 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. 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. Provide suitable Names for Model Provider, Data Provider, OData Model and OData Service or use Default values proposed by the system Final Step Select all Entity Types proposed by the system and press Finish. The service should be successfully generated. Select all Entity types Finish Generation 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.

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. Options 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.

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. 2 1 Select the saved visualization and drag it to the Compose pane.

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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