ODM Model View Details Views in Oracle 12.2

A new feature for Oracle Data Mining in Oracle 12.2 is the new Model Details views.

In Oracle 11.2.0.3 and up to Oracle 12.1 you needed to use a range of PL/SQL functions (in DBMS_DATA_MINING package) to inspect the details of a data mining/machine learning model using SQL.

Check out these previous blog posts for some examples of how to use and extract model details in Oracle 12.1 and earlier versions of the database

Instead of these functions there are now a lot of DB views available to inspect the details of a model. The following table summarises these various DB Views. Check out the DB views I've listed after the table, as these views might some some of the ones you might end up using most often.

I've now chance of remembering all of these and this table is a quick reference for me to find the DB views I need to use. The naming method used is very confusing but I'm sure in time I'll get the hang of them.

NOTE: For the DB Views I’ve listed in the following table, you will need to append the name of the ODM model to the view prefix that is listed in the table.

Data Mining Type	Algorithm & Model Details	12.2 DB View	Description
Association	Association Rules	DM$VR	generated rules for Association Rules
	Frequent Itemsets	DM$VI	describes the frequent itemsets
	Transaction Itemsets	DM$VT	describes the transactional itemsets view
	Transactional Rules	DM$VA	describes the transactional rule view and transactional itemsets
Classification	(General views for Classification models)	DM$VT DM$VC	describes the target distribution for Classification models describes the scoring cost matrix for Classification models
	Decision Tree	DM$VP DM$VI DM$VO DM$VM	describes the DT hierarchy & the split info for each level in DT describes the statistics associated with individual tree nodes Higher level node description describes the cost matrix used by the Decision Tree build
	Generalized Linear Model	DM$VD DM$VA	describes model info for Linear Regres & Logistic Regres describes row level info for Linear Regres & Logistic Regres
	Naive Bayes	DM$VP DM$VV	describes the priors of the targets for Naïve Bayes describes the conditional probabilities of Naïve Bayes model
	Support Vector Machine	DM$VL	describes the coefficients of a linear SVM algorithm
Regression ???	Doe	80	50
Clustering	(General views for Clustering models)	DM$VD DM$VA DM$VH DM$VR	Cluster model description Cluster attribute statistics Cluster historgram statistics Cluster Rule statistics
	k-Means	DM$VD DM$VA DM$VH DM$VR	k-Means model description k-Means attribute statistics k-Means historgram statistics k-Means Rule statistics
	O-Cluster	DM$VD DM$VA DM$VH DM$VR	O-Cluster model description O-Cluster attribute statistics O-Cluster historgram statistics O-Cluster Rule statistics
	Expectation Minimization	DM$VO DM$VB DM$VI DM$VF DM$VM DM$VP	describes the EM components the pairwise Kullback–Leibler divergence attribute ranking similar to that of Attribute Importance parameters of multi-valued Bernoulli distributions mean & variance parameters for attributes by Gaussian distribution the coefficients used by random projections to map nested columns to a lower dimensional space
Feature Extraction	Non-negative Matrix Factorization	DM$VE DM$VI	Encoding (H) of a NNMF model H inverse matrix for NNMF model
	Singular Value Decomposition	DM$VE DM$VV DM$VU	Associated PCA information for both classes of models describes the right-singular vectors of SVD model describes the left-singular vectors of a SVD model
	Explicit Semantic Analysis	DM$VA DM$VF	ESA attribute statistics ESA model features
Feature Section	Minimum Description Length	DM$VA	describes the Attribute Importance as well as the Attribute Importance rank

Normalizing and Error Handling views created by ODM Automatic Data Processing (ADP)

DM$VN : Normalization and Missing Value Handling
DM$VB : Binning

Global Model Views

DM$VG : Model global statistics
DM$VS : Computed model settings
DM$VW :Alerts issued during model creation

Each one of these new DB views needs their own blog post to explain what informations is being explained in each. I'm sure over time I will get round to most of these.

Oracle

About the Author

Brendan Tierney

About the Author

Brendan Tierney, Oracle ACE Director, is an independent consultant and lectures on Data Mining and Advanced Databases in the Dublin Institute of Technology in Ireland. He has 22+ years of extensive experience working in the areas of Data Mining, Data Warehousing, Data Architecture and Database Design. Brendan has worked on projects in Ireland, UK, Belgium and USA and is the editor of the UKOUG Oracle Scene magazine and deputy chair of the OUG Ireland BI SIG. Brendan is a regular speaker at conferences across Europe and the USA and has written technical articles for OTN, Oracle Scene, IOUG SELECT Journal and ODTUG Technical Journal.

About the Author

Brendan Tierney

About the Author

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