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Author: Admin | 2025-04-28
Provides performance advantages. All Oracle Data Mining scoring routines support parallel execution, which significantly reduces the time required for executing complex queries and scoring large data sets. In-database mining minimizes the IT effort needed to support data mining initiatives. Using standard database techniques, models can easily be refreshed (re-created) on more recent data and redeployed. The deployment is immediate since the scoring query remains the same; only the underlying model is replaced in the database. 3.4.2 SQL Functions for Model Apply and Dynamic Scoring In Oracle Data Mining, scoring is performed by SQL language functions. Understand the different ways involved in SQL function scoring. The functions perform prediction, clustering, and feature extraction. The functions can be invoked in two different ways: By applying a mining model object (Example 3-1), or by executing an analytic clause that computes the mining analysis dynamically and applies it to the data (Example 3-2). Dynamic scoring, which eliminates the need for a model, can supplement, or even replace, the more traditional data mining methodology described in "The Data Mining Process". In Example 3-1, the PREDICTION_PROBABILITY function applies the model svmc_sh_clas_sample, created in Example 2-1, to score the data in mining_data_apply_v. The function returns the ten customers in Italy who are most likely to use an affinity card. In Example 3-2, the functions PREDICTION and PREDICTION_PROBABILITY use the analytic syntax (the OVER () clause) to dynamically score the data in mining_data_apply_v. The query returns the customers who currently do not have an affinity card with the
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