SAP HANA Predictive Analysis Library (PAL)

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● Overheads: Uniform distribution and min and max values are listed in the following table. ● Capital Investment (for year 1 and year 2) Gamma distribution and shape and scale values are listed in the following table. Table 428: Year 1 Year 2 Year 3 Year 4 Year 5 Product Revenue Mean £0 £3,000 £8,000 £18,000 £30,000 Standard Deviation 0 300 800 1800 3000 Product Costs Mean £1,000 £1,000 £2,500 £7,000 £10,000 Standard Deviation 75 75 187.5 525 750 Overheads Min £1,400 £1,800 £2,200 £2,600 £3,000 Max £1,500 £2,200 £2,800 £3,400 £4,000 Capital Investment Mean £10,000 £2,000 Standard Deviation 500 100 Run the Random Distribution Sampling algorithm for each variable and generate 1,000 sample sets. The number of sample sets is a choice for the analysis. The larger the value then the more smooth the output distribution and the closer it will be to a normal distribution. SET SCHEMA DM_PAL; ----------------------------------------- ---Random sampling process--------------- ----------------------------------------- DROP TYPE PAL_DISTRRANDOM_DISTRPARAM_T; CREATE TYPE PAL_DISTRRANDOM_DISTRPARAM_T AS TABLE( NAME VARCHAR(50), VAL VARCHAR(50)); DROP TYPE PAL_DISTRRANDOM_RESULT_T; CREATE TYPE PAL_DISTRRANDOM_RESULT_T AS TABLE(ID INTEGER, RANDOM DOUBLE); DROP TYPE PAL_CONTROL_T; CREATE TYPE PAL_CONTROL_T AS TABLE(NAME VARCHAR (50), INTARGS INTEGER, DOUBLEARGS DOUBLE, STRINGARGS VARCHAR (100)); DROP TYPE PAL_CASHFLOW_T; CREATE TYPE PAL_CASHFLOW_T AS TABLE(ID INTEGER, CASH DOUBLE); DROP TABLE PDATA; CREATE COLUMN TABLE PDATA("POSITION" INT, "SCHEMA_NAME" NVARCHAR(256), "TYPE_NAME" NVARCHAR(256), "PARAMETER_TYPE" VARCHAR(7)); INSERT INTO PDATA VALUES (1, 'DM_PAL','PAL_DISTRRANDOM_DISTRPARAM_T', 'IN'); 558 P U B L I C SAP HANA Predictive Analysis Library (PAL) End-to-End Scenarios
INSERT INTO PDATA VALUES (2, 'DM_PAL','PAL_CONTROL_T', 'IN'); INSERT INTO PDATA VALUES (3, 'DM_PAL','PAL_DISTRRANDOM_RESULT_T', 'OUT'); call SYS.AFLLANG_WRAPPER_PROCEDURE_DROP('DM_PAL','PAL_DISTRRANDOM'); call SYS.AFLLANG_WRAPPER_PROCEDURE_CREATE('AFLPAL', 'DISTRRANDOM', 'DM_PAL','PAL_DISTRRANDOM', PDATA); --------------------------- ----------YEAR 1 ----------- -----Product revenue------ DROP TABLE PAL_DISTRRANDOM_DISTRPARAM_TAB; CREATE COLUMN TABLE PAL_DISTRRANDOM_DISTRPARAM_TAB LIKE PAL_DISTRRANDOM_DISTRPARAM_T; INSERT INTO PAL_DISTRRANDOM_DISTRPARAM_TAB VALUES ('DISTRIBUTIONNAME', 'NORMAL'); INSERT INTO PAL_DISTRRANDOM_DISTRPARAM_TAB VALUES ('MEAN', '0'); INSERT INTO PAL_DISTRRANDOM_DISTRPARAM_TAB VALUES ('VARIANCE', '0.0001'); DROP TABLE PAL_CONTROL_TAB; CREATE COLUMN TABLE PAL_CONTROL_TAB LIKE PAL_CONTROL_T; INSERT INTO PAL_CONTROL_TAB VALUES ('NUM_RANDOM',5000,null,null); INSERT INTO PAL_CONTROL_TAB VALUES ('SEED', 0, null, null); INSERT INTO PAL_CONTROL_TAB VALUES ('THREAD_NUMBER', 8, null, null); DROP TABLE PAL_DISTRRANDOM_RESULT_TAB_REVENUE; CREATE COLUMN TABLE PAL_DISTRRANDOM_RESULT_TAB_REVENUE LIKE PAL_DISTRRANDOM_RESULT_T; CALL DM_PAL.PAL_DISTRRANDOM(PAL_DISTRRANDOM_DISTRPARAM_TAB, PAL_CONTROL_TAB, PAL_DISTRRANDOM_RESULT_TAB_REVENUE) with overview; --SELECT * FROM PAL_DISTRRANDOM_RESULT_TAB_REVENUE; ---------Product Costs--------- DELETE FROM PAL_DISTRRANDOM_DISTRPARAM_TAB; INSERT INTO PAL_DISTRRANDOM_DISTRPARAM_TAB VALUES ('DISTRIBUTIONNAME', 'NORMAL'); INSERT INTO PAL_DISTRRANDOM_DISTRPARAM_TAB VALUES ('MEAN', '1000'); INSERT INTO PAL_DISTRRANDOM_DISTRPARAM_TAB VALUES ('VARIANCE', '5625'); DROP TABLE PAL_DISTRRANDOM_RESULT_TAB_COSTS; CREATE COLUMN TABLE PAL_DISTRRANDOM_RESULT_TAB_COSTS LIKE PAL_DISTRRANDOM_RESULT_T; CALL DM_PAL.PAL_DISTRRANDOM(PAL_DISTRRANDOM_DISTRPARAM_TAB, PAL_CONTROL_TAB, PAL_DISTRRANDOM_RESULT_TAB_COSTS) with overview; --SELECT * FROM PAL_DISTRRANDOM_RESULT_TAB_COSTS; --------OVERHEADS--------- DELETE FROM PAL_DISTRRANDOM_DISTRPARAM_TAB; INSERT INTO PAL_DISTRRANDOM_DISTRPARAM_TAB VALUES ('DISTRIBUTIONNAME', 'UNIFORM'); INSERT INTO PAL_DISTRRANDOM_DISTRPARAM_TAB VALUES ('MIN', '1400'); INSERT INTO PAL_DISTRRANDOM_DISTRPARAM_TAB VALUES ('MAX', '1500'); DROP TABLE PAL_DISTRRANDOM_RESULT_TAB_OVERHEADS; CREATE COLUMN TABLE PAL_DISTRRANDOM_RESULT_TAB_OVERHEADS LIKE PAL_DISTRRANDOM_RESULT_T; CALL DM_PAL.PAL_DISTRRANDOM(PAL_DISTRRANDOM_DISTRPARAM_TAB, PAL_CONTROL_TAB, PAL_DISTRRANDOM_RESULT_TAB_OVERHEADS) with overview; -- SELECT * FROM PAL_DISTRRANDOM_RESULT_TAB_OVERHEADS; -------INVESTMENT--------- DELETE FROM PAL_DISTRRANDOM_DISTRPARAM_TAB; INSERT INTO PAL_DISTRRANDOM_DISTRPARAM_TAB VALUES ('DISTRIBUTIONNAME', 'NORMAL'); INSERT INTO PAL_DISTRRANDOM_DISTRPARAM_TAB VALUES ('MEAN', '10000'); INSERT INTO PAL_DISTRRANDOM_DISTRPARAM_TAB VALUES ('VARIANCE', '250000'); DROP TABLE PAL_DISTRRANDOM_RESULT_TAB_INVESTMENT; CREATE COLUMN TABLE PAL_DISTRRANDOM_RESULT_TAB_INVESTMENT LIKE PAL_DISTRRANDOM_RESULT_T; CALL DM_PAL.PAL_DISTRRANDOM(PAL_DISTRRANDOM_DISTRPARAM_TAB, PAL_CONTROL_TAB, PAL_DISTRRANDOM_RESULT_TAB_INVESTMENT) with overview; --SELECT * FROM PAL_DISTRRANDOM_RESULT_TAB_INVESTMENT; --------calculate cash flow ------- DROP TABLE PAL_CASHFLOW_YEAR1; CREATE COLUMN TABLE PAL_CASHFLOW_YEAR1(ID INTEGER,CASH DOUBLE); INSERT INTO PAL_CASHFLOW_YEAR1 SELECT PAL_DISTRRANDOM_RESULT_TAB_REVENUE.ID, PAL_DISTRRANDOM_RESULT_TAB_REVENUE.RANDOM - PAL_DISTRRANDOM_RESULT_TAB_COSTS.RANDOM - PAL_DISTRRANDOM_RESULT_TAB_OVERHEADS.RANDOM - PAL_DISTRRANDOM_RESULT_TAB_INVESTMENT.RANDOM FROM PAL_DISTRRANDOM_RESULT_TAB_REVENUE left SAP HANA Predictive Analysis Library (PAL) End-to-End Scenarios P U B L I C 559
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SAP HANA Platform SPS 12 Document V
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Naive Bayes. ......................
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1 What is PAL? SAP HANA’s SQLScri
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2 Getting Started with PAL This sec
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The result will tell you whether th
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● : The name of the schema where
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CREATE COLUMN TABLE PAL_ARIMAX_DATA
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For more information on how to use
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Category PAL Algorithm Built-in Fun
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Category PAL Algorithm Built-in Fun
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The procedure name is the same as s
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● USER1 has been assigned the AFL
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3.1.2 Agglomerate Hierarchical Clus
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Table 13: Name Data Type Default Va
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Name Data Type Default Value Descri
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INSERT INTO DATA_TBL VALUES ('17',
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Prerequisites ● ● The input dat
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INSERT INTO PAL_AD_DATA_TBL VALUES
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The signature table should contain
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CREATE TYPE PAL_KMEANS_CENTERS_T AS
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PAL_CLUSTER_ASSIGNED_TBL: For DBSCA
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For SOM: SET SCHEMA DM_PAL; DROP TY
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PAL_CLUSTER_ASSIGNED_TBL: Related I
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Table 24: Name Data Type Descriptio
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DROP TYPE PAL_CONTROL_T; CREATE TYP
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3.1.6 Gaussian Mixture Model (GMM)
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Table 29: Name Data Type Descriptio
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Output Tables Output format 0: Tabl
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INSERT INTO PAL_GMM_PDATA_TBL VALUE
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Example 2 SET SCHEMA DM_PAL; DROP T
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PAL_GMM_RESULTSMODEL_TBL: 3.1.7 K-M
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Table 43: Table Column Column Data
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INSERT INTO #PAL_CONTROL_TBL VALUES
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PAL_KMEANS_MODEL_TBL: VALIDATEKMEAN
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"V000" DOUBLE, "A0" INTEGER, "A1" I
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Expected Result PAL_SILHOUETTE_RESU
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Table Column Column Data Type Descr
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Example Assume that: ● ● ● DM
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PAL_KMEDIANS_ASSIGN_TBL: 3.1.9 K-Me
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Position Schema Name Table Type Nam
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The input, parameter, and output ta
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INSERT INTO PARAMETERS_TBL VALUES (
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DOCTOPICDIST_TBL: GENERALINFO_TBL:
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Procedure Calling CALL .(, , ) with
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Expected Result DOCTOPICDIST_TBL: 3
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The following parameters are option
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CREATE TYPE PAL_SOM_MODEL_T AS TABL
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PAL_SOM_RESASSIGN_TBL: PAL_SOM_MODE
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INSERT INTO PAL_SLIGHT_SIL_PDATA_TB
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AUC Procedure Generation CALL SYS.A
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Table 85: Name Data Type Default Va
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Example 2 SET SCHEMA DM_PAL; DROP T
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3.2.2 Back Propagation Neural Netwo
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CREATEBPNN This function trains a B
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INSERT INTO PAL_TRAIN_NN_DATA_TBL V
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PAL_TRAIN_NN_RESULT_TBL: PAL_REGRES
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Classification example: SET SCHEMA
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"NAME" VARCHAR(100), "MODEL" CLOB )
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CREATE TYPE PAL_C45_DATA_T AS TABLE
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CART This function is used for clas
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Prerequisites ● ● The target co
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PAL_CHAID_TREEMODEL_TBL: PAL_CHAID_
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INSERT INTO PAL_CM_DATA_TBL VALUES(
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PAL_KNN_RESULTS_TBL: 3.2.8 Logistic
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Procedure Calling CALL .(, , , , )
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PAL_LOGISTICR_STAT_TBL: PAL_LOGISTI
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Procedure Generation CALL SYS.AFLLA
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Assume that: ● ● ● DM_PAL is
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PAL_FLOGISTICR_FITTED_TBL: Related
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Expected Result PAL_LRMC_MODEL_TBL:
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Table 137: Name Data Type Default V
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SAP HANA Predictive Analysis Librar
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CALL SYS.AFLLANG_WRAPPER_PROCEDURE_
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3.2.10 Naive Bayes Naive Bayes is a
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● ● USER1 has been assigned the
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"DOUBLEARGS" DOUBLE, "STRINGARGS" V
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Position Schema Name Table Type Nam
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DROP TABLE CV_LR_FUNC_TBL; CREATE C
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CREATE COLUMN TABLE FUNC_TBL LIKE F
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DROP TABLE EVALUATION_RESULT_TBL; C
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INSERT INTO PAL_PARA_TBL VALUES('TR
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3.2.13 Random Forest The ransom for
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INSERT INTO PAL_RF_DATA_TBL VALUES
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3.2.14 Support Vector Machine Suppo
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Table 168: Name Data Type Descripti
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CREATE TABLE PAL_SVM_PDATA_TBL("POS
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STRINGARGS VARCHAR(100) ); DROP TYP
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Example 3: Support vector classific
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Related Information SAP HANA Smart
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INSERT INTO PAL_GR_PDATA_TBL values
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Procedure Calling CALL .(, , , ) wi
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3.3.2 Bi-Variate Natural Logarithmi
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CREATE LOCAL TEMPORARY COLUMN TABLE
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Where β0…βn are parameters that
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Assume we have m observation pairs
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Example 1: Fitting multiple linear
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CREATE COLUMN TABLE PAL_ENET_MLR_PD
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3.3.5 Polynomial Regression Polynom
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insert into PAL_PR_PDATA_TBL values
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The input, parameter, and output ta
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3.4 Association Algorithms This sec
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"STRINGARGS" VARCHAR (100) ); INSER
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Optional Parameters The following p
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INSERT INTO PAL_APRIORI_DATA_TBL VA
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PAL_APRIORI_CONS_ITEMS_TBL: LITEAPR
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INSERT INTO PAL_LITEAPRIORI_DATA_TB
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FPGROWTH (Relational Output) FP-Gro
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); DROP TYPE PAL_FPGROWTH_POSTRULE_
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PAL_FPGROWTH_VALUES_TBL: 3.4.3 K-Op
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INSERT INTO PAL_KORD_DATA_TBL VALUE
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Prerequisite No missing or null dat
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. Initial P 0 can be calculated by
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Output Table (Model Table) Table 24
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DROP TYPE PAL_ARIMAX_DATA_T; CREATE
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CALL "SYS".AFLLANG_WRAPPER_PROCEDUR
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We get And variance of the forecast
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CREATE COLUMN TABLE PAL_ARIMA_PDATA
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INSERT INTO PAL_ARIMAX_DATA_TBL VAL
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3.5.2 Auto ARIMA This function auto
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Example 3: Auto Seasonal ARIMA SET
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INSERT INTO PAL_ARIMA_DATA_TBL VALU
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INSERT INTO PAL_ARIMA_DATA_TBL VALU
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F 1 = a 0 + b 0 2. Calculation: S t
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INSERT INTO PAL_BROWNSMOOTH_DATA_TB
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3.5.4 Croston's Method The Croston
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SELECT * FROM PAL_CROSTON_STATISTIC
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PAL_FORECASTACCURACYMEASURES_RESULT
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PAL_OUTPARAMETER_TBL: PAL_FORECASTS
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PAL_OUTPARAMETER_TBL: SAP HANA Pred
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CREATE TYPE PAL_CONTROL_T AS TABLE
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PAL_FORECASTTRIPLESMOOTHING_RESULT_
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PAL_OUTPARAMETER_TBL: PAL_FORECASTM
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PAL_OUTPARAMETER_TBL: PAL_FORECASTM
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Procedure Calling CALL .(, , , ) wi
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PAL_FORECASTSLR_RESULT_TBL: PAL_FOR
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Note The algorithm is backward comp
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Note F 0 is not defined because the
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CREATE COLUMN TABLE PAL_DOUBLESMOOT
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3.5.10 Triple Exponential Smoothing
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PAL calculates the prediction inter
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Output Table Table 294: Table Colum
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PAL_TRIPLESMOOTH_RESULT_TBL: PAL_TR
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Once the trend and seasonality are
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CREATE COLUMN TABLE PAL_TSSEASONALI
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The difference-sign test counts the
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Optional Parameters The following p
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PAL_TSTREND_TREND_TBL: PAL_TSTREND_
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Parameter Table Mandatory Parameter
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3.6 Preprocessing Algorithms The re
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● ● Smoothing by bin medians: e
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3.6.2 Binning Assignment Binning as
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3.6.3 Convert Category Type to Bina
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"NAME" VARCHAR(100), "INTARGS" INTE
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Procedure Generation CALL SYS.AFLLA
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INSERT INTO PAL_IQR_TESTDT_TBL VALU
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PARTITION This function reads the i
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3.6.6 Posterior Scaling Posterior s
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Expected Result PAL_NEW_SCALING_TBL
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INSERT INTO PAL_PCA_DATA_TBL VALUES
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INSERT INTO PAL_PCAPROJ_PDATA_TBL V
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INSERT INTO PAL_DISTRRANDOM_PDATA_T
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INSERT INTO PAL_SAMPLING_DATA_TBL V
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If method is 4 and SAMPLING_SIZE is
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DROP TABLE PAL_SAMPLING_PDATA_TBL;
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Expected Result 3.6.10 Scaling Rang
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Prerequisite Each column must conta
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Assume that: ● ● ● DM_PAL is
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PAL_MISSING_VALUES_RESULT_TBL: PAL_
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INSERT INTO PAL_VT_DATA_TBL VALUES
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3.7.2 Chi-Squared Test for Independ
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DISTRPROB This function calculates
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Page 507 and 508: "VALUE" DOUBLE ); DROP TABLE PDATA_
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Page 515 and 516: 2. Calculate PAL also supports the
Page 517 and 518: Assume that: ● ● ● DM_PAL is
Page 519 and 520: The Kaplan-Meier estimator can be r
Page 521 and 522: The signature table should contain
Page 525 and 526: PAL_KMSURV_LOGRANK_STAT2_TBL: 3.7.7
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Page 529 and 530: The signature table should contain
Page 531 and 532: CREATE TYPE PAL_DISTRQUANTILE_RESUL
Page 533 and 534: where x' = x - Table 404: Definitio
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Page 537 and 538: Let the sample mean of x and y be:
Page 539 and 540: CREATE TYPE PAL_VAREQUALTEST_DATA1_
Page 541 and 542: The quantity is computed as a weigh
Page 545 and 546: Prerequisites ● ● Input data ca
Page 547 and 548: "PARAMETER_TYPE" VARCHAR(7) ); INSE
Page 549 and 550: Signature Input Tables Table 425: T
Page 551 and 552: CALL "SYS".AFLLANG_WRAPPER_PROCEDUR
Page 553 and 554: "DISTANCE" DOUBLE ); DROP TYPE PAL_
Page 555 and 556: "STRINGARGS" VARCHAR(100) ); --crea
Page 557: The projected estimates are single
Page 561 and 562: ----product revenue---- DELETE FROM
Page 563 and 564: ---- calculate net present value of
Page 565 and 566: UPDATE PAL_DISTRPROB_DISTRPARAM_TBL
Page 567 and 568: Then we get the survival estimates
Page 569 and 570: INSERT INTO PAL_TRIAL_DATA_TBL VALU
Page 571 and 572: INSERT INTO PAL_DISTRFITCENSORED_DA
Page 573 and 574: The expected results are as follows
Page 575 and 576: ); INSERT INTO PAL_DISTRPROB_PDATA_
Page 577 and 578: Important Disclaimers and Legal Inf
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