SAP HANA Predictive Analysis Library (PAL)

INSERT INTO PAL_LINK_PREDICTION_DATA_TBL VALUES ('7','4');
INSERT INTO PAL_LINK_PREDICTION_DATA_TBL VALUES ('7','5');
INSERT INTO PAL_LINK_PREDICTION_DATA_TBL VALUES ('6','7');
INSERT INTO PAL_LINK_PREDICTION_DATA_TBL VALUES ('5','4');
DROP TABLE PAL_LINK_PREDICTION_RESULT_TBL;
CREATE COLUMN TABLE PAL_LINK_PREDICTION_RESULT_TBL LIKE
PAL_LINK_PREDICTION_RESULT_T;
CALL DM_PAL.PAL_LINK_PREDICTION_PROC(PAL_LINK_PREDICTION_DATA_TBL,
#PAL_CONTROL_TBL, PAL_LINK_PREDICTION_RESULT_TBL) WITH OVERVIEW;
SELECT * FROM PAL_LINK_PREDICTION_RESULT_TBL ORDER BY NODE1, NODE2;
Expected Result
3.9 Miscellaneous
This section describes the ABC Analysis and Weighted Score Table algorithms that are provided by the
Predictive Analysis Library.
3.9.1 ABC Analysis
This algorithm is used to classify objects (such as customers, employees, or products) based on a particular
measure (such as revenue or profit). It suggests that inventories of an organization are not of equal value, thus
can be grouped into three categories (A, B, and C) by their estimated importance. “A” items are very
important for an organization. “B” items are of medium importance, that is, less important than “A” items and
more important than “C” items. “C” items are of the least importance.
An example of ABC classification is as follows:
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●
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“A” items – 20% of the items (customers) accounts for 70% of the revenue.
“B” items – 30% of the items (customers) accounts for 20% of the revenue.
“C” items – 50% of the items (customers) accounts for 10% of the revenue.
544 P U B L I C
SAP HANA Predictive Analysis Library (PAL)
PAL Functions