Significant performance improvement for SAP app
Utilizing new hardware i.e. multi core, built vector engine
Mastic parallel main memory processing
Changing the dromedaries between application server and data management layer
Simplification of application F gramming _ model
Usage of extended SQL (SQL script)
Rich functionless in the calculation engine
Fast apps delivery
New Approach in Building Apps
(a) Performance Improvement by factors
Applications need to push down the data intensive calculation into the SAP HANA database
Parallelization of algorithms
(b)Shorten Development Life guide
Rich business functions for different industries
Extend and powerful SQL scripts for use.
(c)Deliver – Customers’ real need application:
Quickly build business scenarios
Effective communication on requirements
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Predictive Analysis Algorithms in BFL
In SAP HANA SPUB, the following, will supported in the BFL, grouped into class syerred to as the predictive analysis library within the BFL
Association Analysis (approve)
Decision Trce (c4.5)
Weighted score tables
The predicative analysis process is as follows
Select the data for the predicative analysis
Data could be in csr file, database table, universe…..
Understand the data and preparing the data for analysis
Various data preparation methods : Sample,
Apply formula …….
Visualize the data
Perform the analysis using predicative algorithms & visualize the results
Define the mode:- clarification analysis, time series analysis Association analysis, segmentation analysis ……………..
Training the model, test the model, visualizes the model.
Example:- A decision tree viewer, cluster viewer
Store the result or created model
Store the results in csv, database, universal store the model in PMML.
Store the created “trained” model in the repository …
Use the created model for further analysis:
Use the model on the tire data to scours the model
Export the created model in Industry standard model which can be used by SAP business Applications.
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