SAP HANA Predictive Analysis Library (PAL)

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Substitute Missing Values. ................................................. 484 Variance Test. ..........................................................489 3.7 Statistics Algorithms. ....................................................... 493 Chi-Squared Test for Goodness of Fit. ......................................... 494 Chi-Squared Test for Independent. ............................................497 Cumulative Distribution Function. ............................................500 Distribution Fitting. .......................................................504 Grubbs' Test. ...........................................................514 Kaplan-Meier Survival Analysis. ..............................................518 Multivariate Statistics. .................................................... 525 Quantile Function. ....................................................... 528 Univariate Statistics. ......................................................532 Variance Equal Test. ......................................................536 3.8 Social Network Analysis Algorithms. .............................................540 Link Prediction. ......................................................... 540 3.9 Miscellaneous. ............................................................ 544 ABC Analysis. ...........................................................544 Weighted Score Table. .................................................... 547 4 End-to-End Scenarios. ......................................................552 4.1 Scenario: Predict Segmentation of New Customers for a Supermarket. ....................552 4.2 Scenario: Analyze the Cash Flow of an Investment on a New Product. ..................... 556 4.3 Scenario: Survival Analysis. ................................................... 565 5 Best Practices. ........................................................... 576 4 P U B L I C SAP HANA Predictive Analysis Library (PAL) Content
1 What is PAL? SAP HANA’s SQLScript is an extension of SQL. It includes enhanced control-flow capabilities and lets developers define complex application logic inside database procedures. However, it is difficult to describe predictive analysis logic with procedures. For example, an application may need to perform a cluster analysis in a huge customer table with 1T records. It is impossible to implement the analysis in a procedure using the simple classic K-means algorithms, or with more complicated algorithms in the data-mining area. Transferring large tables to the application server to perform the K-means calculation is also costly. The Predictive Analysis Library (PAL) defines functions that can be called from within SQLScript procedures to perform analytic algorithms. This release of PAL includes classic and universal predictive analysis algorithms in nine data-mining categories: ● ● ● ● ● ● ● ● ● Clustering Classification Regression Association Time Series Preprocessing Statistics Social Network Analysis Miscellaneous The algorithms in PAL were carefully selected based on the following criteria: ● ● ● The algorithms are needed for SAP HANA applications. The algorithms are the most commonly used based on market surveys. The algorithms are generally available in other database products. PAL on SAP HANA Smart Data Streaming PAL also provides several incremental machine learning algorithms that learn and update a model on the fly, so that predictions are based on a dynamic model. For detailed information, see the "Machine Learning with Streaming" section in the SAP HANA Smart Data Streaming: Developer Guide. Note SAP HANA Smart Data Streaming is only supported on Intel-based hardware platforms. SAP HANA Predictive Analysis Library (PAL) What is PAL? P U B L I C 5
Page 1 and 2: SAP HANA Platform SPS 12 Document V
Page 3: Naive Bayes. ......................
Page 7 and 8: 2 Getting Started with PAL This sec
Page 9 and 10: The result will tell you whether th
Page 11 and 12: ● : The name of the schema where
Page 13 and 14: CREATE COLUMN TABLE PAL_ARIMAX_DATA
Page 15 and 16: For more information on how to use
Page 17 and 18: Category PAL Algorithm Built-in Fun
Page 19 and 20: Category PAL Algorithm Built-in Fun
Page 21 and 22: The procedure name is the same as s
Page 23 and 24: ● USER1 has been assigned the AFL
Page 25 and 26: 3.1.2 Agglomerate Hierarchical Clus
Page 27 and 28: Table 13: Name Data Type Default Va
Page 29 and 30: Name Data Type Default Value Descri
Page 31 and 32: INSERT INTO DATA_TBL VALUES ('17',
Page 33 and 34: Prerequisites ● ● The input dat
Page 35 and 36: Name Data Type Default Value Descri
Page 37 and 38: INSERT INTO PAL_AD_DATA_TBL VALUES
Page 39 and 40: The signature table should contain
Page 41 and 42: CREATE TYPE PAL_KMEANS_CENTERS_T AS
Page 43 and 44: PAL_CLUSTER_ASSIGNED_TBL: For DBSCA
Page 45 and 46: For SOM: SET SCHEMA DM_PAL; DROP TY
Page 47 and 48: PAL_CLUSTER_ASSIGNED_TBL: Related I
Page 49 and 50: Table 24: Name Data Type Descriptio
Page 51 and 52: DROP TYPE PAL_CONTROL_T; CREATE TYP
Page 53 and 54: 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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The signature table should contain
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Name Data Type Default Value Descri
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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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● USER1 has been assigned the AFL
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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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PAL_MISSING_VALUES_RESULT_TBL: PAL_
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The procedure name is the same as s
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INSERT INTO PAL_VT_DATA_TBL VALUES
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The procedure name is the same as s
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3.7.2 Chi-Squared Test for Independ
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DISTRPROB This function calculates
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"VALUE" DOUBLE ); DROP TABLE PDATA_
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"DOUBLEARGS" DOUBLE, "STRINGARGS" V
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"TYPE_NAME" NVARCHAR(256), "PARAMET
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2. Calculate PAL also supports the
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The Kaplan-Meier estimator can be r
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PAL_KMSURV_LOGRANK_STAT2_TBL: 3.7.7
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CREATE TYPE PAL_DISTRQUANTILE_RESUL
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where x' = x - Table 404: Definitio
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Let the sample mean of x and y be:
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CREATE TYPE PAL_VAREQUALTEST_DATA1_
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The quantity is computed as a weigh
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Prerequisites ● ● Input data ca
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"PARAMETER_TYPE" VARCHAR(7) ); INSE
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Signature Input Tables Table 425: T
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CALL "SYS".AFLLANG_WRAPPER_PROCEDUR
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"DISTANCE" DOUBLE ); DROP TYPE PAL_
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"STRINGARGS" VARCHAR(100) ); --crea
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The projected estimates are single
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INSERT INTO PDATA VALUES (2, 'DM_PA
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----product revenue---- DELETE FROM
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---- calculate net present value of
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UPDATE PAL_DISTRPROB_DISTRPARAM_TBL
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Then we get the survival estimates
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INSERT INTO PAL_TRIAL_DATA_TBL VALU
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INSERT INTO PAL_DISTRFITCENSORED_DA
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The expected results are as follows
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); INSERT INTO PAL_DISTRPROB_PDATA_
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Important Disclaimers and Legal Inf
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SAP HANA Predictive Analysis Library (PAL)

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