1. SAP Toshihisa.hanaki@sap.com 6/11/2015 DBTech Showcase 2015 DB Big Data - SAP HANAHP Integriry Superdoom X High Performance Data Analysis(HPDA) 2. 2015 SAP SE or an SAP affiliate company. All rights reserved. 2 SAPSAP SAP SAP SAP SAP 3. 2015 SAP SE or an SAP affiliate company. All rights reserved. 3 4. High Performance Data AnalysisHPDA 5. 2015 SAP SE or an SAP affiliate company. All rights reserved. 5 6. 2015 SAP SE or an SAP affiliate company. All rights reserved. 6 / 7. 2015 SAP SE or an SAP affiliate company. All rights reserved. 7 High Performance Data AnalysisHPDA etc 8. 2015 SAP SE or an SAP affiliate company. All rights reserved. 8 High Performance Data Analysis SAP HANA on HP Superdoom X, HP Superdoom XSAP HANA SAP HANA DB DB SQL Scripts R XS (Web Server) ODataJava Script 9. 2015 SAP SE or an SAP affiliate company. All rights reserved. 9 High Performance Data AnalysisHPDA Suspicious Activity Nave Bayes 10. SAP HANA 11. 2015 SAP SE or an SAP affiliate company. All rights reserved. 11 SAP HANA SQL SQLScript // XS AFL JavaScript Odata SDA SDISDQ Smart Streaming SAP HANA DB DB SQL Scripts R XS (Web Server) ODataJava Script 12. 2015 SAP SE or an SAP affiliate company. All rights reserved. 12 SAP HANA SAP HANA ETL UI AP DB SAP HANA HANA 13. PALPredictive Analysis Library 14. 2015 SAP SE or an SAP affiliate company. All rights reserved. 14 AFL(Application Function Library) SAP HANAC++ Application Function:SAPC++SDK AFLAFunctionIMDB SQLScript Application Function(C++) AFL HANA Client AFL HANA Studio SAP HANA 15. 2015 SAP SE or an SAP affiliate company. All rights reserved. 15 Pridictive Analysis Library(PAL) HANA60 o 8000 o >> Predictive Analysis Library SAP HANA 55SPS09 Predictive Analysis Library 60 PMML HANA In DB Analytics 3 16. 2015 SAP SE or an SAP affiliate company. All rights reserved. 16 PALSPS09 Clustering (9) Affinity Propagation Agglomerate Hierarchical Clustering Anomaly Detection DBSCAN K-Means K-Medians K-Medoids Self-Organizing Maps Sight Silhouette Classification (9) Back Propagation Neural Network C4.5 Decision Tree CART Decision Tree CHAID Decision Tree KNN Multi-Class Logic Regression Navie Bays Predict with Tree Model Support Vector Machine Regression (5) Bi_Variable Geometric Regression Bi_Variable Natural Logarithmatic Regression Exponential Regression Multi Linear Regression Polynominal Regression Association (3) Apriori FP-Growth K-Optimal Rule Discovery Time Series (8) ARIMA Brown Exponential Smoothing Crostons Method Forecast Accuracy Measures Linear Regression with Damped Trend and Seasonal Adjust Single Exponential Smoothing Double Exponential Smoothing TripleExponential Smoothing 17. 2015 SAP SE or an SAP affiliate company. All rights reserved. 17 PALSPS09 Preprocessing (10) Binning Convert Category Type to Binary Vector Inter_Quartile Range Test Partition Principal Commponent Analysis(PCA) Random Distribution Sampling Sampling Scailing Range Substitute Missing Values Variance Test Statistics (8) Chi-Squarted Test for Fitness Chi-Squarted Test for Independent Cumulative Distribution Function Distribution Fitting Multivariate Statistics Quantile Function Univariate Statistics Variance Equal Test Social Network Analysis(1) Link Predication Miscellaneous (2) ABC Analysis Weighted Score Table 18. 2015 SAP SE or an SAP affiliate company. All rights reserved. 18 K-MeansK K-Means nk CRM , 2 http://tech.nitoyon.com/ja/blog/2013/11/07/k-means/ 19. 2015 SAP SE or an SAP affiliate company. All rights reserved. 19 PAL K-Means 20. 2015 SAP SE or an SAP affiliate company. All rights reserved. 20 SQL SET SCHEMA PAL; -- PAL setupCREATE TYPE PAL_T_KM_DATA AS TABLE (ID INTEGER, LIFESPEND DOUBLE, NEWSPEND DOUBLE, INCOME DOUBLE, LOYALTY DOUBLE); CREATE TYPE PAL_T_KM_PARAMS AS TABLE (NAME VARCHAR(60), INTARGS INTEGER, DOUBLEARGS DOUBLE, STRINGARGS VARCHAR (100)); CREATE TYPE PAL_T_KM_RESULTS AS TABLE (ID INTEGER, CENTER_ID INTEGER, DISTANCE DOUBLE); CREATE TYPE PAL_T_KM_CENTERS AS TABLE (CENTER_ID INTEGER, LIFESPEND DOUBLE, NEWSPEND DOUBLE, INCOME DOUBLE, LOYALTY DOUBLE); CREATE COLUMN TABLE PAL_KM_SIGNATURE (ID INTEGER, TYPENAME VARCHAR(100), DIRECTION VARCHAR(100)); INSERT INTO PAL_KM_SIGNATURE VALUES (1, 'PAL.PAL_T_KM_DATA', 'in'); INSERT INTO PAL_KM_SIGNATURE VALUES (2, 'PAL.PAL_T_KM_PARAMS', 'in'); INSERT INTO PAL_KM_SIGNATURE VALUES (3, 'PAL.PAL_T_KM_RESULTS', 'out'); INSERT INTO PAL_KM_SIGNATURE VALUES (4, 'PAL.PAL_T_KM_CENTERS', 'out'); CALL SYSTEM.AFL_WRAPPER_GENERATOR ('PAL_KM', 'AFLPAL', 'KMEANS', PAL_KM_SIGNATURE); -- app setup CREATE COLUMN TABLE KM_PARAMS LIKE PAL_T_KM_PARAMS; CREATE COLUMN TABLE KM_RESULTS LIKE PAL_T_KM_RESULTS; CREATE COLUMN TABLE KM_CENTERS LIKE PAL_T_KM_CENTERS; INSERT INTO KM_PARAMS VALUES ('THREAD_NUMBER', 2, null, null); INSERT INTO KM_PARAMS VALUES ('GROUP_NUMBER', 3, null, null); INSERT INTO KM_PARAMS VALUES ('INIT_TYPE', 1, null, null); INSERT INTO KM_PARAMS VALUES ('DISTANCE_LEVEL', 2, null, null); INSERT INTO KM_PARAMS VALUES ('MAX_ITERATION', 100, null, null); INSERT INTO KM_PARAMS VALUES ('NORMALIZATION', 0, null, null); INSERT INTO KM_PARAMS VALUES ('EXIT_THRESHOLD', null, 0.0001, null); -- app runtime TRUNCATE TABLE KM_RESULTS; TRUNCATE TABLE KM_CENTERS; CALL _SYS_AFL.PAL_KM (V_KM_DATA, KM_PARAMS, KM_RESULTS, KM_CENTERS) WITH OVERVIEW; 21. 2015 SAP SE or an SAP affiliate company. All rights reserved. 21 High Performance Data Analysis 22. 23. 2015 SAP SE or an SAP affiliate company. All rights reserved. 23 & HP ConvergedSystem 900 for SAP HANA Xeon E7-2890 v2 (2.8GHz, 15core) x 16CPU(240core) 12TB SAP HANA SPS08 Enterprise Edition () 12TB () 5TB * SQLScriptPAL 24. 2015 SAP SE or an SAP affiliate company. All rights reserved. 24 24 HANA 11SQL 22SQL 3(K-means)PAL: K-means / 4(KNN)PAL: K-means , KNN 5SQL 6K-means K-means SQL, PAL:K-means 7KNN K-means SQL, PAL: K-means, KNN 8ARIMA, ARIMAXSQL, PAL: ARIMA 9 SQL 25. 2015 SAP SE or an SAP affiliate company. All rights reserved. 25 25 5 52510 25 510 1212 1 and 2500121 10 (TRAN_CRRNT)(TRAN_HIST) DBTR_ID) (CDTR_ID) DBTR_ID) (CDTR_ID) (AMOUNT) 0..1 n (ACCOUNT) (ID) 0 (RED_FLAG) n 1 1 n 26. 2015 SAP SE or an SAP affiliate company. All rights reserved. 26 Suspicious Activity x b ac y 200 43.5 46.5 47.3 44.2 18.5 ATM 26 1 2 1 HPDA() Overall timeServer time 18.92611.420 27. 2015 SAP SE or an SAP affiliate company. All rights reserved. 27 2 27 2015/4/1 RED_FLAG (9) RED_FLAG (9) (BLACK_LIST) RED_FLAG (10) (ACCOUNT) RED_FLAG (10) RED_FLAG (10) RED_FLAG10 RED_FLAG 10 RED_FLAG 99 (TRAN_CRRNT) DBTR_ID CDTR_ID (ACCOUNT) ACC_ID RED_FLAG Suspicious Account 28. 2015 SAP SE or an SAP affiliate company. All rights reserved. 28 2 x b a c d e h f g Xred flag 28 2015/4/1 HPDA() Overall timeServer time 2.9270.423 29. 2015 SAP SE or an SAP affiliate company. All rights reserved. 29 483570 20 112 ,16 1 1 1 20 3122 3Desert Single Family 12 220 20 20 30. 2015 SAP SE or an SAP affiliate company. All rights reserved. 30 HANA:1 DBMS:1 WCHAN SQL C++Intel Xeon 31. 2015 SAP SE or an SAP affiliate company. All rights reserved. 31 High Performance Data Analytics SAP HANA HP Superdoom X HPC 32. 2015 SAP SE or an SAP affiliate company. All rights reserved. 32 High Performance Data Analysis 33. 2015 SAP SE or an SAP affiliate company. All rights reserved. 33 HW 612 12:30-13:20Room D 34. 2015 SAP SE or an SAP affiliate company. All rights reserved. SAP toshihisa.hanaki@sap.com