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SAP Predictive Analytics offers an expert mode to desktop users to let you use any combination of algorithms from a variety of libraries. Libraries include those in SAP HANA such as the PAL and APL, as well as an external R server. This enables you to create predictive models on the desktop and then publish them to a server environment. There, you can execute the predictive workload without requiring any further intervention or involvement (see Figure 11). Figure 11: Calling of R Scripts from SAP HANA Application SAP HANA® database SQL interface Calculation engine Trigger Server R operator R client Data R Results Run time SAP HANA Tables © 2016 SAP SE or an SAP affiliate <strong>company</strong>. All rights reserved. 20 / 33
SAP HANA supports this type of sidecar deployment to offer data scientists the ultimate flexibility to use any algorithm they want or even create their own in R. They call R scripts through SQLScript in SAP HANA and then pass the scripts to an external R server, along with all of the required input data. The results are sent back to SAP HANA and combined with any local data required to complete the query (see Figure 12). However, integrating the results from arbitrary scripting code using an external server results in significant overhead required to extract and transfer data to the R server for processing. Performing all calculations with a local in-memory predictive engine, such as the PAL for SAP HANA, avoids this overhead. PREDICTIVE ANALYSIS LIBRARY: PAL The predictive analysis library, or PAL, is designed to take advantage of the ability of SAP HANA to host execution engines and perform local calculations in memory. Unlike the previous option that uses an external predictive server for processing, this SAP HANA–native library enables users to perform in-database data mining and statistical calculations with excellent performance on large data sets. Figure 12: Sample Code in SQLScript /*Create a function using input and output table types in HANA*/ CREATE FUNCTION LR( IN input1 SUCC_PREC_TYPE, OUT output0 R_COEF_TYPE) LANGUAGE RLANG AS’’’ CHANGE_FREQ
Page 1 and 2: SAP Predictive Analytics with SAP H
Page 3 and 4: Big Data, Big Challenges, and Big O
Page 5 and 6: The result of predictive analytics
Page 7 and 8: Examining SAP Predictive Analytics
Page 9 and 10: If you are already using SAP HANA,
Page 11 and 12: USING SAP PREDICTIVE ANALYTICS AND
Page 13 and 14: The following sections discuss many
Page 15 and 16: Predictive models executed in SAP H
Page 17 and 18: REAL-TIME AUTOMATED PREDICTIVE ANAL
Page 19: “Algorithmic” Predictive Capabi
Page 23 and 24: Figure 14 shows a nonexhaustive lis
Page 25 and 26: “Automated” Predictive Capabili
Page 27 and 28: Operationalizing Automated Predicti
Page 29 and 30: Beyond the basic script-based appro
Page 31 and 32: Comparing R, PAL, and APL Capabilit
Page 33 and 34: Why SAP Predictive Analytics with S

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