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SAP Predictive Analysis

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Revisiting the Technical Content in BW Administration Cockpit with SAP Predictive Analysis

The following blog post demonstrates how to use the technical content of SAP BW as a forecast data basis for a prognosis model in SAP Predictive Analysis. The aim is to show a smooth and straight-forward process avoiding additional modelling outside of BW as much as possible. In the described use case the Database Volume Statistics[1] have been chosen as an example.


The official SAP Help summarizes the Technical Content in BW Administration Cockpit as follows: “The technical BI Content contains objects for evaluating the runtime data and status data of BW objects and BW activities. This content is the basis for the BW Administration Cockpit, which supports BW administrators in monitoring statuses and optimizing performance.[2]


The Technical Content with its pre-delivered Web Reporting might look a bit old-fashioned nevertheless the variety, quality, and quantity of data which is “generated” at any time in the system is very useful and important for further analysis. The type of data has a strong focus on performance-related data (e.g. query runtimes, loading times) but also other system-related data like volume statistics are available.



BW on Hana and SAP Predictive Analysis[3] together are extending the possibilities how to see the data and what to do (potentially more) with it.[4]

Technically there are simply the following 3 steps to follow[5]:

  1. Expose cube information model to Hana (SAP BW)
  2. Adjust data types to PA-specific format (Hana Studio)
  3. Create forecast model (SAP PA Studio)


The Database Volume statistics in the technical content are designed with a simple data model consisting of just one cube with some characteristics (day, week, month, DB object, object type, DB table etc.) and key figures (DB size in MB, number of records etc.). Following the above steps with this set of data, choosing a certain type of algorithm, results in a bar chart shown below integrated with forecast figures for the past and some months into the future.


The blue bars represent the actual database size by month. The green line represents the calculated figures of the forecast model (in this case a Double Exponential Smooth regression) for the past 20 months and 10 months into the future.



Below are some technical details for each of the mentioned steps:


(1) Expose information model of Infocube 0TCT_C25 to Hana Studio[6]

  • Edit the Infocube in BW and set the flag for “External SAP HANA view”:



Immediately the information model is generated as an Analytic View and can be viewed in Hana Studio:

  • Content -> system-local -> bw -> bw2hana -> 0 -> Analytic Views -> TCT_C25



(2) Adjust data types to PA-specific format (Hana Studio)

  • The generated Analytic View of Infocube 0TCT_C25 looks like below:


SAP Predictive Analysis needs (currently) a specific time-ID column and the key figures must be of data type DOUBLE. The new Calculation View CV_TCT_C25_1 is created based on the generated Analytic View TCT_C25:

  • Column [Month] (PA_TIME_ID_MONTH) = <unique sequential number for each month>[7]
  • Column [Database Size] (PA_TCTDBSIZE) = DOUBLE(0TCTDBSIZE)



(3) Create forecast model (SAP PA Studio)


Creating a forecast model in SPA Predictive Analysis follows the standard tasks as for any other data source.


  • Select data source i.e. select prepared calculation view including (time) key id column and relevant key figures
  • Select and configure components for the model:
    • Use [Filter] component (if necessary restrict columns and rows like filtering the relevant database object types, time range etc.)
    • Choose adequate [Algorithm] component, in the following case a Double-smoothing algorithm (PAL) has been chosen for forecasting several months into the future



And finally the resulting trend diagram is shown (see above).




[1] Infocube 0TCT_C25

[2] SAP Help Portal -> Technology -> SAP NetWeaver Platform

[3] This post deals with SAP BW on Hana 7.40/SP6 and SAP Predictive Analysis 1.19

[4] The blog post is focusing on the technical aspects to get a forecast model successfully executed. The chosen algorithm might not be statistically appropriate.

[5] Assuming the technical content has been activated in SAP BW

[6] Unfortunately it’s not yet possible to expose the information model of a Multiprovider

[7] Data used is from April 2013 to November 2014. To get a unique ID the following calculation is used (in order to get a sequence starting from 1):

    (int("0CALYEAR") - 2013)*12 + int(rightstr("0CALMONTH",2)) - 3

SAP uses Advanced Analytics expertise to support the fight against Ebola


A team within SAP is developing an analytical application to help combat the spread of Ebola. The current outbreak poses a global health and safety threat and requires the help of everyone to be contained.


All hands on deck: The outbreak of Ebola in several West African countries, and the threat of it spreading to Europe and the United States have mobilized hundreds of volunteers around the world to combat its spread. Volunteers have ranged from individual healthcare workers to global companies like SAP, who have joined forces to develop a cutting edge advanced analytics solution to support the helpers in their challenging task of fighting the disease. Our goal is to provide large health organizations with this application to support their mission. This solution promises to be not only valuable in the field in Africa, but can also be used by state authorities to screen passengers of incoming flights from affected countries.


Our plan: We want to make an efficient and fast diagnosis of the disease possible, which is essential for medical personnel to make the right treatment decisions. The developed application will first enable doctors and helpers to gather data on infectious diseases. This information will be subsequently fed into a central database. Based on input from remote doctors and machine learning, the application identifies whether a patient may have been infected with Ebola.


Kevin Richards, Head of U.S. Government Relations at SAP interviewed the WHO and US State Department representatives to identify the key challenges that operators in the field are facing. It became clear that one of the biggest influencing factors is the ability to collect the patient data when in most cases there is no stable connection to the internet. Hence, the quality of collected data will be determined by the robust offline capabilities of the application, which then can be synchronised to an overall data hub as soon as an internet connection becomes available.


Data collection & Diagnosis: Whenever a doctor or a volunteer thinks someone may be showing signs of an infectious disease, they can open the application and navigate to the “Add Patients” Tab. The doctor can take a picture or make a video of the patient and report their symptoms. This data is then sent to a central database along with the doctor’s geo-location and submission time. The application is a cloud solution which can be accessed easily on any mobile devices. The collected data can be stored on the device and synchronised later, as soon as an internet connection becomes available. 


Once the data is uploaded, remote doctors are able to comment on each patient, help with the diagnosis and give treatment recommendations. Meanwhile SAPs Advanced Analytics solution InfiniteInsight clusters the described symptoms, patient data and the judgments of the doctors in the background of the application. This way it can be determined what reported symptoms are most highly correlated with an Ebola diagnosis. For example the symptoms of chills, blurred vision, nausea and vomiting, ulcer, severe headache, and unexplained hemorrhage are the symptoms that are the most important in determining if a particular patient may or may not be infected with Ebola. Upon further analysis into the contributing variables, it becomes clear that ulcers, chills, and blurred vision are the most commonly reported symptoms not associated with an Ebola diagnosis. Conversely, the contributing variables of nausea and vomiting, unexplained hemorrhage, and severe headache are associated with the disease. As the Algorithm determines which symptoms are significant indicators, the application is able to push a preliminary diagnosis to the helpers even in offline mode and an appropriate treatment can commence without any delay. Additionally, the application will allow the tracking of any mutations and subsequent symptom changes of the disease over time and geography.



Forecast: One of the biggest challenges is to understand how the disease will spread during the coming weeks. Hundreds of lives could be saved if we would be able to predict in which cities Ebola is going to break out next – With SAP’s Advanced Analytics we can provide a tool that will give the necessary insights into the future spread and development of the disease based on the data patterns of the collected incidents. Users will also be able to view an infographic in the app to see the current spread of Ebola and information about the appropriate safety measures.



In my previous blog SAP InfiniteInsight - Explorer , I demonstrated how you can create a data set for further analysis.



In this blog I will focus on the SAP InfiniteInsight Modeler to create a model on the data set from my previous blog.


In the previous blog we prepared data that comes from a garden retailer that has a coffee shop. We prepared the data so we could analyze in this blog what will influence someone visiting the garden shop to most likely have a Dessert or Cake at the garden retailers coffee shop.


So lets start...


From the welcome screen I will select "Create a Classification" under the Modeler section. As you can see different types of models can be created.


Figure 1




I have now selected the data from the explorer. I have selected Analytical Record Set 1.


Figure 2



By pressing next you will go to the next screen, will be blank until you press Analyze button. Then figure 3 will be displayed. At this step we can also view the data if we need to.


Figure 3



Now we will select the target variable which we want to analyze, the target variable is who bought cakes or desserts. We also exclude some variables. So here we are saying we want the model to determine what the other variables impact is on our target variable.


Figure 4



The next screen will then show the summary of the model.


Figure 5



The model generation will start, also known as "Training the model"


Figure 6



The results of the model will be shown as seen in figure 7. It is important to know the following values shown and the meaning of the values.

  • KI - a measure of how powerful the model is at predicting. This is a number that ranges between 0 and 1. The closer the KI is to 1, the more accurate the model is.
  • KR - a measure of robustness, or how well the model generalizes on an independent hold out sample. KR should be a number ideally above 0.95.


So based on the above, our KI measure is poor. But will serve our purposes for the blog 


Figure 7

We can now review the model results by selecting the appropriate options.


Figure 8

By selecting "Contribution by variable" we can see that the following aspects influence the scenario. Firstly pets, then children, then the segment, then the age, etc.


Figure 9

We can now take it further and analyze the age variables. Here we see that ages between 18-26 and 48-70 are likely to buy a cake or dessert. Individuals with the age 26-48 are less likely.


Figure 10.

So this now tells us the coffee shop will have better success with cakes and desserts that are appealing to people with pets, have children and are between the age gaps identified. This will help deliver a more precise advertising if needed.


Hope the above shows how a predictive model is created by just clicking away and how the results can be a valuable tool.

Developers are rarely shy about sharing their views on new tools and technology. I appreciate their passion and healthy scepticism, in fact I seem to have developed my own slightly cynical perspective. So when I heard we’d added SAP InfiniteInsight (formerly from KXEN) to the SAP OEM offering (it’s my job to build OEM marketing content), I quietly wondered how relevant the solution was going to be for our OEM partners.


As I gathered solution information my sceptical attitude soon began to shift to one of pleasant surprise at how ‘cool’ the functionality was. I knew that predictive analytics was about looking at data and forecasting the likelihood of future events, and yes that is cool, but that’s not what impressed me. My own experience of working with in-house data scientists (dudes with PHDs in statistics and analytics) had shown me that creating a predictive model for optimizing campaign lead follow-up takes weeks, if not months. The process required the identification of predictive variables and development of a consistent model for using those variables to score prospects based on how likely they were to buy, and then involved lots of iterative testing.



What I hadn’t expected was SAP InfiniteInsight’s ability to self-learn from historical data… and identify the predictive variables without a data scientist in the room. In fact, the software can continuously relearn and adapt its scoring based on current target audience actions.


Next I’m thinking, ok this would be great value-add for any partner building customer management solutions or operations software but it must be pretty tricky to integrate… and I was once again pleasantly surprised. SAP InfiniteInsight’s core functionality resides in 4 DLLs totalling just 1.5 MB with comprehensive APIs.

That means our OEM partners can relatively easily embed the technology, point the solution at an historical database and let it figure out the predictive characteristics and then use those variables to score a net new target individual or target dataset of many individuals. This can even be done in real-time so if someone is surfing my ecommerce site and has selected to purchase an item I can instantly offer up the next-best three items as suggestions – based on what others have typically bought with the 1st item.


This really gets the brain cells firing in terms of the all the potential scenarios where SAP InfiniteInsight might extend existing application value, and drive increased customer satisfaction and loyalty. Here are just a few of the scenarios that I thought were appealing.

For CRM related applications:

  • Optimize direct marketing campaigns to boost response rates
  • Analyze customers’ website touch points to improve their online experience
  • Target customers that have a high propensity to churn with new customized offers
  • Analyze customer purchasing histories to deliver targeted up-sell recommendations


For business operations:

  • Predict how market-price volatility will impact production
  • Foresee changes in demand and supply
  • Analyze streams machine data to build proactive maintenance schedules
  • Forecast customer demand and optimize inventory
    in real time


For finance solutions:

  • Analyze sales transactions to identify unsafe investments
  • Predict patterns of fraud within Big Data
  • Perform credit score analysis in real time


And I almost forgot, if you’re interested in turbocharging your predictive analytics performance you can also pair InfininieInsight with the in-memory computing power of SAP HANA for a real-time experience.

In the end, my mind set had completely reversed from one of skepticism to one of optimism but for those of you that have that skeptical bone in your body, I invite you to do your own investigation. I’ve included a couple of links to speed the process.


SAP InfiniteInsight home page

SAP InfiniteInsight Industry and LOB scenarios

SAP Predictive Analytics OEM eBook

SAP InfiniteInsight Introduction and Overview Blog


If you’re interested in learning more about…

  • building predictive models in minutes or hours, not weeks or months
  • integrating automated predictive modeling into your applications
  • increasing your application footprint at existing customers

then please reach out to our OEM team. Many SAP OEM partners are already using SAP InfiniteInsight to differentiate their offerings and open new revenue streams.


Get the latest updates on SAP OEM by following us @SAPOEM on Twitter

For more details on SAP OEM partnership and to know about SAP OEM platforms and solutions , visit us www.sap.com/partners/oem



I have not seen much posted regarding InfiniteInsight. I thought I would take some time to demonstrate parts of this product.


InfiniteInsight is predictive analysis tool SAP has acquired form the acquisition of the company KXEN.


This tool is designed to make the process of using a predictive tool easier and with less reliance on a data science. Also everything done is done by just CLICKING AWAY.


When you launch the product you will see Figure 1 as your entry point. In this blog I will focus purely on the explorer part. Explorer is used to get your datasets in a format that we can used to build predictive models on.

1. Explorer.png

Figure 1 - InfiniteInsight




So first step is to create explorer objects, will need to select the source of the data. In this scenario we are pulling from HANA.

2. Connect To Data.png

Figure 2 - Create or Explorer Objects


You can then create your datasets. In my example I have already created the datasets, all done by clicking and no code. I have created three types of data sets.

  1. Entity
  2. Time Stamped
  3. Analytical Record


Figure 3 - data sets


I wont be showing how I created each data set as there is a few screens that would need to be captured and will make the blog too long. Here is a example of the entity data. Data that shows entity that will be analyzed.


Figure 4 - entity data

Example of time stamp data, here we just create time entries.


Figure 5 - Time Stamp Data


The analytical record we have basically taken the time stamp data and joined the entity data, when creating this we can choose what fields to keep or exclude.


Figure 6 - Analytical Record


You can create different versions of the types of data, here I have a second analytical record set. It is the same as the first one except we have added some calculation columns being a sum, count and count distinct. Once again created just with clicks and no code.


Figure 7 - Analytical Record 2

I have also created a third analytical record where we have added extra columns that are pivoted so we can use to analyse even further.

As seen above, the explorer part allows you to get different sets of data and combine them, do counts, pivots and more. Once the data is arranged in desired format you can now move to the next section to predict data on it.


I will try cover that on another blog.

First some background about the issue:
      InfiniteInsight (II) is not letting you use your analytical views, calculated views and so on in the user interface

In the background, II will use the capabilities of the ODBC driver to get the list of "data space" to be presented to the user using a standard ODBC function.

Unfortunately, the HANA ODBC driver is not currently including the names of the analytical views, calculated views.


However this ODBC driver behavior can easily be bypassed in two ways:
- simply type in the full name of the calculated view (including the catalog name) like "PUBLIC"."foodmart.foodmart::EXPENSES"
- configure II to use your own custom SQL that will list the item you want to display.

This feature is used in II to restrict the list of tables for example when your datawarehouse has hundreds of schemas.


One file needs to be change depending on if you are using a workstation version (KJWizard.cfg) or a client/server version (KxCORBA.cfg) by adding the following content:


ODBCStoreSQLMapper.MyDSN.SQLOnCatalog1="  SELECT * FROM (   "


ODBCStoreSQLMapper.MyDSN.SQLOnCatalog3="  UNION ALL   "

ODBCStoreSQLMapper.MyDSN.SQLOnCatalog4="   SELECT '""' || SCHEMA_NAME || '""', '""' || VIEW_NAME || '""', VIEW_TYPE FROM SYS.VIEWS WHERE NOT EXISTS (  "


ODBCStoreSQLMapper.MyDSN.SQLOnCatalog6="         WHERE SCHEMA_NAME = a.CATALOG_NAME AND VIEW_NAME = a.CUBE_NAME AND ( MANDATORY = 1 OR MODEL_ELEMENT_TYPE IN ('Measure', 'Hierarchy', 'Script') )  "


ODBCStoreSQLMapper.MyDSN.SQLOnCatalog8="  ) order by 1,2   "


The KxCORBA.cfg file (used in a client/server installation) itself is located on the InfiniteInsight server installation directory named:

     C:\Program Files\SAP InfiniteInsight\InfiniteInsightVx.y.y\EXE\Servers\CORBA

where x.y.z is the version you have installed.


If you are using a standlaone (a.k.a. Workstation), then the file to modify is KJWizard.cfg which is located in:

     C:\Program Files\SAP InfiniteInsight\InfiniteInsightVx.y.y\EXE\Clients\KJWizardJNI

where x.y.z is the version you have installed.


In this example I only include tables, views, calc and join views with no mandatory variables or 'Measure', 'Hierarchy', 'Script' variables at all.


You may need to adjust this configuration SQL if you want to list Smart Data Access objects.


You can notice here that we are changing the behavior for one ODBC DSN (MyDSN), so this value might need to be adjusted in your environment.

You can also replace it with a star (*), then this configuration will be applied to all ODBC DSN, which may not work on other databases.


Some functionalities in II may not work yet properly despite this workaround.

For example:

  • data manipulations requires the configuration file change
  • view placeholhers and in general views attributes are not properly supported
  • some type of aggregates are not "selectable by name" which mean that if used in a select statement in HANA Studio it will not be returned (select * vs select cols).


Hope this will save you some time

Hello !

This is my first post to scn, so, please, be generous)


I'm working with HANA PAL for 4 monthes. My domain is time series predictions, so I'm using *ESM functions collection, espeially TESM.

When I build my forecast models, I always want to visualise the results - that gives me the first understanding of whether I'm doing right or not. You know that - two charts are much less "readable" than one:







When you look at the second one - you get very clearly that your forecast is not realy good, while looking at the first two you might think "Mmm?... "



So, what we want is to merge the input PAL table/view (let it be fact) and the output one - let it be prediction.



There would be no problem here if you had your data in the appropriate structure by default:



But usually I don't.

My raw data usually comes as PSEUDO_TIMESTAMP | DOUBLE table.

Where PSEUDO_TIMESTAMP may be of mm-yyyy, ww-yyyy, yyyy.mm, yyyy.ww and so on...


So, the question is - how to sort it in an appropriate way and then to numerate the rows?


  1. Sorting
    My solution is to transform any input pseudo_timestamp format to YYYY.[ MM | WW | DD ] with the help of DateTime and String functions. (1.7.2 and 1.7.5 in SAP HANA SQL and System Views Reference respectively).
    After you've done it, order by clause will work just fine.
  2. Numerating
    First I've tried to use undocumented HANA table's technical row "$row_id$" - but it works bad..
    The clear and fast solution is to perform the following code before PAL call:

    --assuming that fact table has two columns, timestamp and values. Timestamp is a primary key.

    alter table fact add ("id" bigint);
    drop sequence sequence1;
    create sequence sequence1 START with 1 increment by 1;

    upsert fact select  T1."timestamp", T1."values", sequence1.nextval from fact T1;

After that you can easily create table/view with {"id","value"} to feed to ESM, and then to left join with prediction results


on fact.ID = prediction.ID

Then you visualize the final table/view of your prediction in HANA Studio -> Data Preview -> Analysis

Hope that will help you


Precise forecasts to all of us

These are some brief notes with question and answer and polls from yesterday’s SAP webcast.  The usual disclaimer applies that things in the future are subject to change.


Also note I didn't stay for the whole session so I may have missed some points.



Figure 1: Source: SAP

The Speed of evolution has changed.  As Figure 1 shows, today we have the challenges and inefficiencies of current analytics landscape including complexity, speed, and cost (Source: @SAPAnalytics)


Figure 2: Source: SAP


SAP wants to democratize advanced analytics and make it easy, fast, and efficient as the slides shows.


They want to make it easy so you don’t need advanced degrees to do this work.


Figure 3: Source: SAP


Figure 3 shows you can embed Analytics so the user doesn't know it's underneath


Business Analysts are the lynch pin, want things easier to use said SAP’s Shekhar Iyer


Figure 4: Source: SAP


The above shows an overview of predictive analytics solutions from SAP


Figure 5: Source: SAP


Figure 5 shows bringing together lines of business and industries to make things “efficient and effective”

SAP says to consider the new analysis that is possible with predictive analytics & put our creativity to work



Q: What is biggest stumbling block?

A: Complexity, KXEN – Infinite Insight combines both


Figure 6: Source: SAP


Figure 6 shows the results of attendees poll responses.  Most of us aren’t using any predictive analytics solution.


Figure 7: Source: SAP

A customer example is eBay. They saved millions by finding an attribute that contributed to a lack of pipeline (Source: @SAPAnalytics)


Figure 8: Source: SAP


Analogy was made that InfiniteInsight is the espresso machine & Predictive Analysis is the barista


Learn more about InfiniteInsight at ASUG Annual Conference, where the data modeler for the 2012 Obama Presidential Campaign discusses Using Analytics to Help Win the US Presidency


Figure 9: Source: SAP


The above shows an overview of the HANA Predictive Analysis Library (PAL)


Learn how a customer is using PAL – see Predictive Analytics for Procurement Lead Time Forecasting at Lockheed Martin Space Systems Using SAP HANA, R, and the SAP Predictive Analysis Toolset at ASUG Annual Conference next month.


Figure 10: Source: SAP


The above shows an overview of SAP R Integration for predictive analytics



Q: Can you contrast solutions – R with HANA PAL

A: Algorithms with HANA PAL are a subset of R, optimized to run in R

SAP will continue to invest in #HANA PAL, R Integration

Continue to invest in PAL. Added 100 engineers in this area


Q: How see algorithms in KXEN?

A: Not algorithms in KXEN/InfiniteInsight – they are functions

What you see in InfiniteInsight are functions, sorted by category vs. the algorithms

Proprietary algorithms in KXEN -/ II but they do share details


Figure 11: Source: SAP


Attendees said the biggest barrier to adopting predictive analytics is skills shortage.  Second is cost.


Figure 12: Source: SAP


Figure 12 shows the smart vending example of “Smart operations”


Asset management is used keep things cold


It also helps personalize the experience


Figure 13: Source: SAP


The customer in Figure 13 went from 4 days to 3 hours breakdown time on the Smart Vending example.


Figure 14: Source: SAP

Figure 14 shows a Cox case study.


Question and Answer

Q: I’d like to understand predictive and stochastic capabilities and how it understands unstructured data

A: address any model data

Unstructured – when build predictive models, need to structure data in some ways

Use SAP HANA libraries, data services, InfiniteInsight to structure data


Q: How often do you switch models out?

A: It depends on business problem and data

Tool to manage models is Infinite Insight –Factory, which lets you reconstruct original data set on the fly. Model management is a big piece


Figure 15: Source: SAP


Figure 15 asks who is using predictive analytics to build models in your organization?  Looks like it is mostly the business analyst


Figure 16: Source: SAP


Figure 16 is an overview of future direction/roadmap of predictive analytics solutions from SAP. For more details attend ASUG Annual Conference Session Predictive Analysis Roadmap with SAP’s Charles Gadalla.



If you missed yesterday’s session and you can register for today’s 7:00 PM session http://bit.ly/RMHgEm


Other (source: @SAPAnalytics):


  • If you are interested in test driving SAP Predictive there is a free trial available at http://bit.ly/1sqaowj
  • SAP offers Rapid Deployment Solutions to speed up deployments
  • It can use HANA smart data access feature. You can use HANA as overlay to federate the data into Predictive Analysis.


ASUG Annual Conference

Preview of ASUG Annual Conference 2014: Focus on Analysis Office/OLAP/Predictive


Share your Story: Call for Sessions for ASUG at SAP d-code (former TechEd)


You are invited to submit a proposal to share your experience and expertise with your colleagues to speak at SAP d-code to be held October 20-24 in Las Vegas.  Others will benefit from your experience while you make a valuable contribution to the profession's field of knowledge.

Follow this link to create a speaker account where you can formally submit your proposal, review important deadlines, and other general information about SAP d-code.  The deadline to submit your abstract is May 25. If you have any questions, please e-mail sapdcodespeaker.info@sap.com

Upcoming ASUG Analytics Webcasts:


May 15: Lumira Self Service for Business User

May 21: SAP Lumira Question and Answer Session

June 23: Predictive Analysis Roadmap

September 15: Design Studio and Analysis Scenarios on HANA

This is part 2 of today’s ASUG webcast with SAP's Charles Gadalla.


Part 1 is Predictive Analysis - KXEN is not a Radio Station -  ASUG Webcast - Part 1


Figure 1: Source: SAP


Figure 1 shows the popularity of R, with a “hockey stick from 2011 and up”


Figure 2: Source: SAP


Figure 2 shows an example of editing a custom component with R inside Predictive Analysis.


Figure 3: Source: SAP


Figure 3 shows an example of "live editing" of the Custom R component inside Predictive Analysis.


Figure 4: Source: SAP


Figure 4 shows upcoming sharing options.


Figure 5: Source: SAP


Figure 5 shows building the deployment, solution set, extend it through the organization


Figure 6: Source: SAP


An example of embedding is shown in Figure 6 - no one knows this is Predictive Insight, it is part of the module


Figure 7: Source: SAP


Figure 7 shows RDS content and it is “free”


Figure 8: Source: SAP


Figure 8 shows Predictive Analysis and KXEN are converging over time (subject to change).


Question & Answer

Q: What Predictive Analysis capabilities are available in ECC without HANA?

A: SAP InfiniteInsight EXPLORER

A: APO, BW modules, if not use HANA you can use Predictive and KXEN - not dependent on HANA.


Q: Quite a few client tools. Is there a guide to know when to use which tool?

A: Yes, a few client tools.

A: Predictive & Infinite Insight sold as Infinite Insight Modeler. Data  scientists -  Lumira is a visualizaiton tool - 2 algorishm.


Q: Any plan on having SAP Lumira to be a thin client?

A: SAP Lumira is available in the Cloud cloud.saplumira.com


Q: Have you seen any successful models used in healthcare that predict patient outcomes (micro) or hospital admits (macro)?

A: Health care  - SEPSIS / influenza analysis

A: Yes SEPSIS, hospital management, research etc



Q: Are there any projects / RDS to use HANA to speed up pricing rebuilding

A: Price optimization - complicated module- sister line - retail product lines using Hybris / Customer Engagement Intelligence.


Q: Can the tool extract data from external sources such as websites/partner portals (maybe usig RSS or other feeds), and include in my data assessment/analysis?

A: Yes, typically have intermediary of Hadoop


Q: What are the client tools scheduled to be running in 64 bit soon and in in-memory?

A: Predictive and InfiniteInsight running in 64 bit


Q: With regard to Lumira Server, currently the artifacts look to be persisted on HANA, what are the plans to integrate these into Business Objects Enterprise or is the idea to position Lumira Server as a lightweight content repository?

A: Lumira Server is Lumira on HANA and will integrate with BI Platform > will standardize as one on BI framework


Q: Pricing question restated - I have pricing programs that must rebuild prices based on commodity market input and it has difficulty completing overnight.   Any projects to apply HANA to this problem?

A: If look at pricing on market input, projection, trend, can do this with HANA - PAL library algorithms, Monte Carlo that would help with simulations



Q: looks like the biggest use cases are currently in market forecast and customer analysis. Are there any for supply chain?

A: Yes - Demand Signal Management, APO, - 150+ use cases and growing.



Join Us at ASUG Annual Conference


Upcoming ASUG Webcast next month:

SAP's Charles Gadalla provided this webcast today.




Figure 1: Source: SAP

On the left of Figure 1, high skill sets are needed to be a data scientist, with a masters in statistics.


On the right side, you have business users


Consumers take output from data scientists and take an action.

In the middle: data analysts/business analysts – do more than basic reporting – segmentation, forecasting, in a more sophisticated manner


Figure 2: Source: SAP

Data scientists on the far right of Figure 2 are already well served.


SAP is interested in group in the middle, including embedding the analytics inside the workflow


Figure 3: Source: SAP


Figure 3 shows a paradox that there is a lot of “big data”.


We are using more data today and decisions are made in a much shorter time scale, with a huge increase in speed of algorithms


Every business is being asked to make decisions faster with more data

Why should I care?



Figure 4: Source: SAP


Figure 4 shows that back in December, SAP released a survey, showing competitive "ROI"

sap track record.png

Figure 5: Source: SAP


Figure 5 shows Mobilink going through 900TB call data records for communities – 6M communities from these calls


MONext – decision on fraud transactions in milliseconds

why acquire kxen.png

Figure 6: Source: SAP


It was on this slide that Charles said "KXEN doesn't stand for a radio station...it means knowledge extraction engine".  I did not know that.

advanced solution insight to action.png

Figure 7: Source: SAP


Insight from KXEN to view thousands of fields of data; Predictive Analysis was built inside SAP


Charles used as an example if you drink a diet cola on Tuesday at that means you had chips on a Sunday


Another example is to integrate and tell story as Predictive is built on Lumira


hana analytics portfolio.png

Figure 8: Source: SAP


Figure 8 shows data comes in from any of the channels


PAL is on HANA is the implementation on HANA R – maintained by universities and consortium – popular algorithms to use and reuse – execute in memory,


It is based on an open source language


Client tools on top left of Figure 8.


SAP combined Predictive Analysis with Insight in a tool called Insight Modeler


It also includes a line of business application – like Fraud Management, etc.


SAP has RDS solutions using Predictive


They partner with ESRI, SAS


Charles has special speaker from Obama campaign presenting on how the Obama campaign used KXEN to win the 2012 US Presidential election.

predictive analytics portfolio on HANA.png

Figure 9: Source: SAP


With SAP embedded on HANA, you are not getting the SAS algorithm


You can see the two ways to access HANA in Figure 9

predictive and kxen.png

Figure 10: Source: SAP


Three options:

1) Client side – PA/KXEN – Java based and R based predictive  - connect to relational database and CSV


2) Server – Infinite Insight Explorer – connect to database (say Oracle)

a. Factory – model management – how data looked 1-2-3 months

b. Factory scheduling to refresh

c. Infinite Insight Social – trying to detect similar/like-minded people

d. Recommendation engine – buy brown shoes, likelihood to buy belt


3) Third option is HANA – with PAL in memory, connected to R


More to come...



ASUG Annual Conference has the following SAP Predictive sessions:

Session IDTitleStart Date
202Predictive Analysis Roadmap6/3/2014
203Using Analytics to Help Win the US Presidency6/3/2014
204Predictive Analytics for Procurement Lead Time Forecasting at Lockheed Martin Space Systems Using SAP HANA, R, and the SAP Predictive Analysis Toolset



Charles is presenting the Predictive Analysis Roadmap and co-presenting "Using Analytics to Help Win the US Presidency".


Join us in May for ASUG Annual Conference   - Pre-Conference SAP BusinessObjects BI4.1 with SAP BW on HANA and ERP Hands-on – Everything You Need in One Day June 2nd


Register at: ASUG Preconference Seminars



Share your Story: Call for Sessions for ASUG at SAP d-code (former TechEd)

Share your knowledge with others and submit a proposal to speak at SAP d-code. Selected proposals will be part of the ASUG and SAP d-code: Partners in Education program, providing attendees with interactive learning experiences with fellow customers.

View the education tracks planned this year.  If selected, you will receive a complimentary registration for the conference and it will give you valuable professional exposure.

Follow this link to create a speaker account where you can formally submit your proposal, review important deadlines, and other general information about SAP d-code.

The deadline to submit your abstract is May 25. If you have any questions, please e-mailsapdcodespeaker.info@sap.com

How well will you do tomorrow? How can we be sure?


Algorithmic and biomedical advances are now producing sports coaches, mangers and team owners the tools to predict which players have picked and which ones have their full potential ahead of them.

I don’t use much of quantitative methods when it comes to sports. I think it takes away my excitement.




After the Super Bowl game finished – I saw on twitter that SAP had predicted that Denver will win over Seattle in a close match. As it turned out – Seattle won a rather one sided match with a very young side.


I didn’t work on the predictive Analytics solution that made the prediction for Super Bowl and I am not authorized by SAP to provide a response. But I wanted to share my personal views on this matter.

Then I saw Vijay Vijayasankar’s discussion about the perils of predictive analytics. He makes the crucial points:


Predictive Analytics in general cannot be used to make absolute predictions when there are so many variables involved . In fact – I think there is no place for absolute predictions at all . And when the results are explained to the non-statistical expert user – it should not be dumbed down to the extent that it appears to be an absolute prediction .

Predictive models make assumptions – and these should be explained to the user to provide the context . And when the model spits out a result – it also comes with some boundaries (the probability of the prediction coming true , margin of error , confidence etc). When those things are not explained – predictive Analytics start to look like reading palms or tarot cards . That is a disservice to Predictive Analytics .

If the chance of Denver winning is 49% and Seattle winning is 51% – it doesn’t exactly mean Seattle will win . And not all users will look at it that way unless someone tells them more details .

In business , there is hardly any absolute prediction ever . Analytics provide a framework for decision making for the business leaders . Analytics can say that if sales increases at the same historic trend , Latin America will outperform planned numbers next year compared to Asia. However , the global sales leader might know more about the nuances that the predictive model had no idea of, and hence can decide to prioritize Asia . The additional context provided by predictive Analytics enhances the manager’s insight and over time will trend to better decisions . The idea definitely is not to over rule the intuition and experience of the manager . Of course the manager should understand clearly what the model is saying and use that information as a factor in decision making .

When this balance in approach is lost – predictive Analytics gets an unnecessary bad rap.

I thought I would post this quick blog to try and help anybody out who may come across the same issue as I did.


When trying to do some data preparation via filtering on a date in the "Predict" tab of SAP Predictive Analysis, I received the following error when trying to run the analysis: "An error occurred while executing the query.Error details: SAP DBTech JDBC: [266]: inconsistent datatype: 3-3-2014 is not a DATE type const: line 1 col 779 (at pos 778)". The error with a different format is the same:




Unfortunately, this error is vague and does not point out what a valid date format is or where to find the valid date formats. I tried multiple formats until I stumbled upon one that works (ie, YYYY-MM-DD):



Hopefully this helps some people out.


If you know of documentation or other date formats that are supported, please share!

Continuing from previous post we now explore Sentiment Analysis. First of all let’s talk about Sentiment Analysis and Text Mining and what exactly it means when we speak about these terms. Wikipedia defines Sentiment Analysis as “Generally speaking, sentiment analysis aims to determine the attitude of a speaker or a writer with respect to some topic or the overall contextual polarity of a document”. Sometimes it is also called as Opinion Mining which is extracting information from people’s opinions. Opinions are usually in the form of text and hence to do Sentiment Analysis we need some knowledge of Text Mining also. Text Mining in the words of Hearst (1999) is “the use of large online text collections to discover new facts and trends about the world itself" Standard techniques are text classification, text clustering, ontology and taxonomy creation, document summary and latent corpus analysis.  We are going to use the combination of both Sentiment Analysis and Text Mining in our example scenario discussed below.

Before I start let me make it clear that this is only sample data which was analyzed only for the purpose learning. It’s not to target any brand or influence any brand. The outputs and analysis shown here are just based on opinion and should not be considered facts.

I downloaded some public opinion data regarding Car Manufacturer from the NCSI-UK website.

Scores By Industry

The data is from 2009-2013. My intention was to just see what is the public sentiment of people for these manufacturers on Social Networking Site twitter and build a probable score for 2014 based on twitter sample population. The intention is just to see if the scores are similar to those obtained in 2013.


The steps to do sentiment analysis using SAP PA and twitter are shown below. The code is shown at the end of this post.


1. Load the necessary packages. Also load the credential file that stores the credential information required to connect to twitter. This credential file was created using the steps shown in the below post. Also establish the handshake with twitter.

2. Retrieve the tweets for each of the brand in our data-set (total 9) and save the information in a data-frame for each car brand.

3. The next step is to analyse the tweets obtained for negative and positive words. For this we use something called as Lexicons. As per Wiki, the word "lexicon" means "of or for words". A Lexicon is basically similar to dictionary and collection of words. For our sentiment analysis we are going to use Lexicon of Hu and Liu available at Opinion Mining, Sentiment Analysis, Opinion Extraction. The Hu and Liu Lexicon is a list of positive and negative opinion words or sentiment words for English (around 6800 words).  We download the Lexicon and save it on our local desktop. We load this file to create an array of positive and negative words as shown in the code. We can also append our own list of positive and negative words as required.

4.Now that we have an array of positive and negative words we need to compare them with the tweets we obtained and assign a score of 1 to each positive word in the tweet and -1 to each negative word in the tweet. Each score of 1 is considered a positive sentiment and a score of -1 is considered a negative sentiment.

The sum of overall sentiment score gives us the net sentiment for that brand. For this we require a Sentiment Scoring function. I have used the function available at the below website.I have used the function As-Is from the below website and give full credit to the author who created that function. This function is not created by me.

How-To | Information Research and Analysis (IRA) Lab

5. After getting the sentiment score for each brand next step is to sum the score and assign it to an array. This array than we bind with our original data set. We use this final table to generate heat maps as shown below:

Final Output with Sentiment Score




Heat Maps





As we see from the above analysis that although the industry score for one brand (Audi) is quite high, the current pubic sentiment is with another brand (Vauxhall) that had an overall low industry score. This is just a basic analysis with 500 tweets. We can extend this analysis further and try to increase the tweets and create a more advanced score function that uses other parameters like region, time and historical data while calculating the final sentiment score.

This post serves as a starting point for anyone interested in doing Sentiment Analysis using twitter. There is certainly a lot of possibility to explore.



mymain<- function(mydata, mytweetnum)



## Load the necessary packages for twitter connecttion






##Packages required for sentiment analysis




##Loading the credential file saved

load('C:/Users/bimehta/Documents/twitter authentication.Rdata')


options(RCurlOptions = list(cainfo = system.file("CurlSSL", "cacert.pem", package = "RCurl")))


## Retrieving the tweets for the brands in our excel.

tweetList <- searchTwitter("#Audi", n=mytweetnum)

Audi.df = twListToDF(tweetList)


tweetList <- searchTwitter("#BMW", n= mytweetnum)

BMW.df = twListToDF(tweetList)


tweetList <- searchTwitter("#Nissan", n= mytweetnum)

Nissan.df = twListToDF(tweetList)


tweetList <- searchTwitter("#Toyota", n= mytweetnum)

Toyota.df = twListToDF(tweetList)


tweetList <- searchTwitter("#Volkswagen", n= mytweetnum)

Volkswagen.df = twListToDF(tweetList)


tweetList <- searchTwitter("#Peugeot", n= mytweetnum)

Peugeot.df = twListToDF(tweetList)


tweetList <- searchTwitter("#Vauxhall", n= mytweetnum)

Vauxhall.df = twListToDF(tweetList)


tweetList <- searchTwitter("#Ford", n= mytweetnum)

Ford.df = twListToDF(tweetList)


tweetList <- searchTwitter("#Renault", n= mytweetnum)

Renault.df = twListToDF(tweetList)


##Upload the Lexicon of Hu and Liu saved on your desktop

hu.liu.pos = scan('C:/Users/bimehta/Desktop/Predictive/Text Mining & SA/positive-words.txt', what='character', comment.char=';')

hu.liu.neg = scan('C:/Users/bimehta/Desktop/Predictive/Text Mining & SA/negative-words.txt', what='character', comment.char=';')


##Build an array of positive and negative words based on Lexicon and own set of words

pos.words = c(hu.liu.pos, 'upgrade')

neg.words = c(hu.liu.neg, 'wtf', 'wait','waiting','fail','mechanical','breakdown')


## Build the score sentiment function that will return the sentiment score

score.sentiment = function(sentences, pos.words, neg.words, .progress='none')



  # we want a simple array ("a") of scores back, so we use

  # "l" + "a" + "ply" = "laply":


  scores = laply(sentences, function(sentence, pos.words, neg.words) {


    # clean up sentences with R's regex-driven global substitute, gsub():


    sentence = gsub('[[:punct:]]', '', sentence)


    sentence = gsub('[[:cntrl:]]', '', sentence)


    sentence = gsub('\\d+', '', sentence)


    # and convert to lower case:


    sentence = tolower(sentence)


    # split into words. str_split is in the stringr package


    word.list = str_split(sentence, '\\s+')


    # sometimes a list() is one level of hierarchy too much


    words = unlist(word.list)


    # compare our words to the dictionaries of positive & negative terms


    pos.matches = match(words, pos.words)

    neg.matches = match(words, neg.words)


    # match() returns the position of the matched term or NA

    # we just want a TRUE/FALSE:


    pos.matches = !is.na(pos.matches)


    neg.matches = !is.na(neg.matches)


    # and conveniently enough, TRUE/FALSE will be treated as 1/0 by sum():


    score = sum(pos.matches) - sum(neg.matches)




  }, pos.words, neg.words, .progress=.progress )

  scores.df = data.frame(score=scores, text=sentences)




## Creating a Vector to store sentiment scores

a = rep(NA, 10)


## Calculate the sentiment score for each brand and store the score sum in array

Audi.scores = score.sentiment(Audi.df$text, pos.words,neg.words, .progress='text')

a[1] = sum(Audi.scores$score)


Nissan.scores = score.sentiment(Nissan.df$text, pos.words,neg.words, .progress='text')



BMW.scores = score.sentiment(BMW.df$text, pos.words,neg.words, .progress='text')

a[3] =sum(BMW.scores$score)


Toyota.scores = score.sentiment(Toyota.df$text, pos.words,neg.words, .progress='text')



##Sentiment Score for other brands is considered 0



Volkswagen.scores = score.sentiment(Volkswagen.df$text, pos.words,neg.words, .progress='text')



Peugeot.scores = score.sentiment(Peugeot.df$text, pos.words,neg.words, .progress='text')



Vauxhall.scores = score.sentiment(Vauxhall.df$text, pos.words,neg.words, .progress='text')



Ford.scores = score.sentiment(Ford.df$text, pos.words,neg.words, .progress='text')



Renault.scores = score.sentiment(Renault.df$text, pos.words,neg.words, .progress='text')



##Plot the histogram for a few brand.


hist(Audi.scores$score, main="Audi Sentiments")

hist(Nissan.scores$score, main="Nissan Sentiments")

hist(Vauxhall.scores$score, main="Vauxhall Sentiments")

hist(Ford.scores$score, main="Ford Sentiments")


## Return the results by combining sentiment score with original dataset

result <- as.data.frame(cbind(mydata, a))





Code Acknowledgements:

Opinion Mining, Sentiment Analysis, Opinion Extraction

How-To | Information Research and Analysis (IRA) Lab

R by example: mining Twitter for consumer attitudes towards airlines

While doing some research on Sentiment and Text Analysis for one of my projects, I came across a really nice blogspot.



Inspired by the above, I thought of doing some sentiment analysis in SAP PA using twitter tweets.Hence decided to go ahead and do some text mining and Sentiment Analysis using the twitteR package of R.

I have created a multi-series blog where we see the different things we can do using SAP PA, R and Twitter.


First blog here talks about how get the twitter data inside SAP PA and build a word-cloud by building a text corpus.



I downloaded some public opinion data regarding Car Manufacturer from the NCSI-UK website.


The data is from 2009-2013. My intention was to just see what is the public sentiment of people for these manufacturers on Social Networking Site twitter and build a probable score for 2014 based on twitter sample population. I loaded the data in SAP PA. First I build a word cloud for some of the hashtags of the cars and plot a graph on number of re-tweets. In the next blog postings I will be doing Sentiment Analysis of this data and Emotion Classification.


Before I start let me make it clear that this is only sample data which was analyzed only for the purpose learning. It’s not to target any brand or influence any brand. The outputs and analysis shown here are just based on opinion and should not be considered facts.

Step1: Setting up the Twitter account and API for handshake with R

Please refer this step by step document to setup the twitter API and the settings required to call the API and get tweet data inside R.

Setting up Twitter API to work with R


Step2: Getting the tweet data in SAP PA and building a word-cloud.

Now we need to create a custom R component to get the data into SAP PA and create a text corpus and display it as a word-cloud. I have used the tm_map function comes that comes with the tm package for setting up the text corpus data for word-cloud. The various commands are self-explanatory as shown in the comments. I have used wordcloud package to generate the word-cloud.


The code below lists down the steps you need to do to get the desired output. The configuration settings are shown in the screenshots below.


mymain<- function(mydata, mytweet, mytweetnum)




##Load the necessary packages










## Enable Internet access.



##Load the environment containing twitter credential data (saved in Step 1)

load('C:/Users/bimehta/Documents/twitter authentication.Rdata')


##Establish the handhsake with R


options(RCurlOptions = list(cainfo = system.file("CurlSSL", "cacert.pem", package = "RCurl")))


##Get the tweet list from twitter site (based on parameters entered by user)

tweetList <- searchTwitter(mytweet, n=mytweetnum)


##create text corpus

r_stats_text <- sapply(tweetList, function(x) x$getText())

r_stats_text_corpus <- Corpus(VectorSource(r_stats_text))


##clean up of twitter Text data by removing punctuation and English stop words like "the", "an"

r_stats_text_corpus <- tm_map(r_stats_text_corpus, tolower)

r_stats_text_corpus <- tm_map(r_stats_text_corpus, removePunctuation)

r_stats_text_corpus <- tm_map(r_stats_text_corpus, removeWords, stopwords("english"))

r_stats_text_corpus <- tm_map(r_stats_text_corpus, stemDocument)



##Build and print wordcloud

out2 <-wordcloud(r_stats_text_corpus, scale=c(10,1), random.order=FALSE, rot.per=0.35, use.r.layout=FALSE, colors="blue")




## Return the twitter data in a table

result <- as.data.frame(cbind(Audi.df$text, Audi.df$created, Audi.df$statusSource, Audi.df$retweetCount))




Configuration Setting:




Running the Algorithm and getting the output:



The output table (created on is char):










The general opinion of the public from wordcloud seems positive. However we will do a detailed sentiment analysis of the various brands in our source file and plot the heat map based on 2013 survey findings in my next blog. This will help us know whether current public sentiment is in line with survey findings.

To be continued in Sentiment Analysis.



Following on from Clarissa Dold's announcement about the KXEN acquisition end-2013, I wanted to take this opportunity to introduce to you the latest addition to SAP's predictive analytics portfolio: SAP Infinite Insight .


The majority of this information is already available through Clarissa's blog and external PA Roadmap presentation. I started chatting about this topic on this discussion here: Starting with KXEN - [Updated with more info] but it wasn't enough.


So the purpose of this blog is to offer an overview of the 'solution brief' including product positioning; a description of current software modules & deployment options; followed by some mention of future integration plans and tentative possibilities. Finally, a consolidation of useful resources (links etc) for your own on-boarding.


I've shown this type content during regional enablement workshops, so I'm hoping it'll be of use to you too!






  • Let's start with a positioning slide which describes some of the key benefits and features of this product. The key message here is that you don't need to be a data scientist to use the tool effectively!


1 intro.png


  • Taking this differentiation further, we can call-out the specific areas where Infinite Insight has clearly gained a competitive advantage over classic data-mining vendors:


2 intro why.png


  • Infinite Insight is revolutionizing the way companies use predictive analytics to make better decisions on petabytes of big data. Their unprecedented solution approach allows line of business users to solve common predictive problems without the need for highly skilled data scientists.


3 model lifecycle.png


  • Infinite Insight is a suite of tools providing predictive analytic applications for the automated creation of accurate and robust predictive models.  These solutions replace the classic model creation process, which is manual, repetitive and prone to human error.


3 overview modules.png


  • Explorer is an extremely powerful data-manipulation tool, which allows the designer to create derived columns and row-values, effectively “exploding out” existing data into new compound variables and ratios. Lots of semantic definitions and transformations can be authored here into the dataset.


5 a explorer.png


  • The Modeler is the main workspace/module for mining activities: Classification, regression, segmentation and clustering. It generates statistical models, and represents them using indicators and chart types.


5 b modeller.png


  • Factory is a secured java web-deployed interface, which includes Roles & Rights administration on  the server platform. From there, Projects are accessed by users, are assigned Models, and KPI evaluation/Model retraining can be scheduled as Tasks.


5 c factory.png


  • Scorer is a feature that exports regression & segmentation models in different programming languages. The generated code (or SQL) allows models be applied outside of InfiniteInsight. It reproduces the operations made during data encoding and either Classification/Regression or Clustering efforts.


5 c scorer.png


  • Social improves decision capacities by extracting the implicit structural relational information of the dataset. You can then navigate a social network, the structure of which is represented in the form of a graph, made of nodes and links. For example, it can help identify individuals of influence within a community.


5 c social.png



5 component model.png


  • In terms of licensing and selling 'software bundles', smaller departments would likely consider the desktop "thick-client" workstation Modeller installations, whereas larger enterprises would implement the full "suite" of client-server components:


5 software bunble options.png


  • You need to be prudent when obtaining your package from the SMP download marketplace  as there are a number of items available to cover the various license and audience options:


6 installation types.png


  • Infinite Insight's data mining methods are unique in the market, here are a few of the value propositions & differentiators which set it aside from the competition:


8 the benefits of SRM.png


  • There is a wealth of existing guides and training available, to help you further your knowledge of the product. The documentation are very detailed, as is the online course, and locally installed media (post-installation):


9 product docu.png


  • The documentation at help.sap.com perfectly complements the RKT learning maps, you'll be an expert in no time:


11 doc page.png


  • Just to reiterate again, the legacy named "KXEN" has been totally retired from the product portfolio, we are now dealing exclusively with SAP Infinite Insight (II):


22 product rename.png


  • This is the snapshot of the combined "PA" and "II" roadmap plans (subject to change). Whilst Infinite Insight's capabilities will strengthen for the next +1 release, incremental features will also be ported to the Predictive Analysis (and hence Lumira) client, and Server capabilities will be delegated down to the HANA in-memory processing platform:


555 future integration roadmap.png


  • Focusing specifically on Infinite Insight's next-steps, we will be seeing initially tighter, followed by complete/native integration of ex-KXEN assets into the SAP Predictive Analytics portfolio, in keeping with our commitment to strategic initiatives such as In-Memory, Big Data, Cloud, Mobile and agile Visualization:


666  II_roadmap.png


  • Here's a non-binding illustration of our go-to-market intentions for 2014. These estimated timelines are subject to change and purely communicated in the spirit of openness:


555 future integration roadmap FULL overview.png


  • One thing is for sure, PA will be the interface going forward (so that Infinite Insight can benefit from its flashy CVOM visualization gallery and HTML5 agility). Our first expectation is that the ex-KXEN proprietary algorithm will start to appear in the Predictive Analysis Designer:


33 kxen into PA.png


  • We're going to harness the processing power of HANA's in-memory platform to maximize the reach of KXEN's unique approach to data mining. Infinite Insight algorithms are going to be rewritten into HANA as 'function libraries' that can be called by the Application Foundation Modeler or other SAP apps:


99 lab preview KFL into hana.png


  • As mentioned already, we have a vision of a unified client. A single desktop experience that will cover the full spectrum of use-cases, from the casual end-user Lumira 'visualize' workflows, through to business-users wanting to 'predict', through to analysts/scientists wanting to 'analyze' deeper.
  • Here's a mock-up of what that could look like, as the user is guided into the application:


99 lab preview unified client.png


  • Other innovations we might see could include an intuitive "drag to forecast" - how pleasant an experience that would be on a tablet device!


99 visual drag to forecast.png


  • One thing is for sure, Infinite insight's advanced statistical charts will massively benefit from the refresh they are about to receive from its inclusion into the Lumira suite (CVOM charting and HTML5). We can envisage drill-able charts to find influencers, similar to the BO Set Analysis of old:


999 drillage influencers chart.png


  • This all ties-in very significantly into the wider plans for SAP Lumira integration, and our roll-out plans for the SERVER version. About which, more info can be found at the GA Announcement page:


99999 Lum_srv_plans.png


** addendum **  (June 2014)


Our good friend and colleagues have been busy!





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