Monday, November 28, 2011

Exalytics Events over the next week

The BIWA SIG is hosting a techcast called “Using Oracle R Enterprise” on Wednesday 30th November, 2011 at noon EST (approx 6pm GMT).

The TechCast is being presented by Mark Hornick, Senior Manager, Oracle Advanced Analytics Development

URL for TechCast:
-- Web Conference ID: 303397
-- Web Conference Key: 608880
-- Dialup: 1-866-682-4770, ID 5548204, passcode 1234

Several analytic tool vendors have added R-integration to their software. However, Oracle is the largest company to throw their weight behind R. On October 3, Oracle unveiled their integration of R: Oracle R Enterprise ( as part of their Oracle Big Data Appliance announcement (

Oracle R Enterprise allows users to perform statistical analysis with advanced visualization on data stored in Oracle Database. Oracle R Enterprise enables scalable R solutions, while facilitating production deployment of R scripts and Hadoop based solutions, as well as integration of R results with Oracle BI Publisher and OBIEE dashboards.

Check out the Oracle YouTube video (5min), that demos how an Exalytics application that can analyse almost a billion records instantly.

If you are attending the UKOUG Conference in Birmingham, Jon Mead (RittmanMead) is giving a presentation called “What can Exalytics do for me?” and is on Tuesday 5th December @15:35, in the area above the box office.

Thursday, November 24, 2011

Applying an ODM Model to new data in Oracle – Part 2

This is the second of a two part blog posting on using an Oracle Data Mining model to apply it to or score new data. The first part looked at how you can score data the DBMS_DATA_MINING.APPLY procedure for scoring data batch type process.

This second part looks at how you can apply or score the new data, using our ODM model, in a real-time mode, scoring a single record at a time.


The PREDICTION SQL function can be used in many different ways. The following examples illustrate the main ways of using it. Again we will be using the same data set with data in our (NEW_DATA_TO_SCORE) table.

The syntax of the function is

PREDICTION ( model_name, USING attribute_list);

Example 1 – Real-time Prediction Calculation

In this example we will select a record and calculate its predicted value. The function will return the predicted value with the highest probability

SELECT cust_id, prediction(clas_decision_tree using *)
WHERE cust_id = 103001;

---------- ------------------------------------
    103001                                    0

So a predicted class value is 0 (zero) and this has a higher probability than a class value of 1.

We can compare and check this results with the result that was produced using the DBMS_DATA_MINING.APPLY function (see previous blog post).

SQL> select * from new_data_scored
  2  where cust_id = 103001;

---------- ---------- -----------
    103001          0           1
    103001          1           0

Here we can see that the class value of 0 has a probability of 1 (100%) and the class value of 1 has a probability of 0 (0%).

Example 2 – Selecting top 10 Customers with Class value of 1

For this we are selecting from our NEW_DATA_TO_SCORE table. We want to find the records that have a class value of 1 and has the highest probability. We only want to return the first 10 of these

SELECT cust_id
WHERE PREDICTION(clas_decision_tree using *) = 1
AND       rownum <=10;


Example 3 – Selecting records based on Prediction value and Probability

For this example we want to find our from what Countries do the customer come from where the Prediction is 0 (wont take up offer) and the Probability of this occurring being 1 (100%). This example introduces the PREDICTION_PROBABILITY function. This function allows use to use the probability strength of the prediction.

select country_name, count(*)
from   new_data_to_score
where  prediction(clas_decision_tree using *) = 0
and    prediction_probability (clas_decision_tree using *) = 1
group by country_name
order by count(*) asc;

COUNTRY_NAME                               COUNT(*)
---------------------------------------- ----------
Brazil                                            1
China                                             1
Saudi Arabia                                      1
Australia                                         1
Turkey                                            1
New Zealand                                       1
Italy                                             5
Argentina                                        12
United States of America                        293

The examples that I have give above are only the basic examples of using the PREDICTION function. There are a number of other uses that include using the PREDICTION_COST, PREDICTION_SET, PREDICTION_DETAILS. Examples of these will be covered in a later blog post

Tuesday, November 22, 2011

Oracle Ireland: Data Centre Transformation Event 7th December

Oracle in Ireland is hosting a session called Data Centre Transformation on 7th December (9:30-13:00), in the Guinness Storehouse, St James Gate, Dublin 8.

The agenda for this session is

9:00 Registration & Coffee
10:00 The 21st Century Data Centre, Delivered by Oracle Solaris – Mike Ramchand
10:30 Oracle Enterprise Manager 12c – John Caulfield, Solutions Director
11:00 Oracle Virtualised Systems (VM 3.0) – Dave Patterson, Oracle Hardware
11:30 Coffee Break
12:00 Transformative Oracle Storage Solutions – Neil Caughey, Oracle Storage Business Unit
12:30 Extreme Performance with Oracle Exadata and Exalogic – Brian Grant, Oracle Exalogic Business Development Manager

To book your place on this event email

Or register by following this web link.

I wont be at this event as I’ll be presenting in the afternoon at the UKOUG conference in Birmingham.

Monday, November 21, 2011

Applying an ODM Model to new data in Oracle – Part 1

This is the first of a two part blog posting on using an Oracle Data Mining model to apply it to or score new data.  This first part looks at the how you can score data using the DBMS_DATA_MINING.APPLY procedure in a batch type process.

The second part will be posted in a couple of days and will look how you can apply or score the new data, using our ODM model, in a real-time mode, scoring a single record at a time.


Instead of applying the model to data as it is captured, you may need to apply a model to a large number of records at the same time. To perform this bulk processing we can use the APPLY procedure that is part of the DBMS_DATA_MINING package. The format of the procedure is

      model_name           IN VARCHAR2,
      data_table_name      IN VARCHAR2,
      case_id_column_name  IN VARCHAR2,
      result_table_name    IN VARCHAR2,
      data_schema_name     IN VARCHAR2 DEFAULT NULL);

Parameter Name Description
Model_Name The name of your data mining model
Data_Table_Name The source data for the model. This can be a tree or view.
Case_Id_Column_Name The attribute that give uniqueness for each record. This could be the Primary Key or if the PK contains more than one column then a new attribute is needed
Result_Table_Name The name of the table where the results will be stored
Data_Schema_Name The schema name for the source data

The main condition for applying the model is that the source table (DATA_TABLE_NAME) needs to have the same structure as the table that was used when creating the model.

Also the data needs to be prepossessed in the same way as the training data to ensure that the data in each attribute/feature has the same formatting.

When you use the APPLY procedure it does not update the original data/table, but creates a new table (RESULT_TABLE_NAME) with a structure that is dependent on what the underlying DM algorithm is. The following gives the Result Table description for the main DM algorithms:

For a Classification algorithms

case_id      VARCHAR2/NUMBER
prediction   NUMBER / VARCHAR2  -- depending a target data type
probability  NUMBER

For Regression

case_id     VARCHAR2/NUMBER
prediction  NUMBER

For Clustering

case_id      VARCHAR2/NUMBER
cluster_id   NUMBER
probability  NUMBER

Example / Case Study

My last few blog posts on ODM have covered most of the APIs for building and transferring models. We will be using the same data set in these posts. The following code uses the same data and models to illustrate how we can use the DBMS_DATA_MINING.APPLY procedure to perform a bulk scoring of data.

In my previous post we used the EXPORT and IMPORT procedures to move a model from one database (Test) to another database (Production). The following examples uses the model in Production to score new data. I have setup a sample of data (NEW_DATA_TO_SCORE) from the SH schema using the same set of attributes as was used to create the model (MINING_DATA_BUILD_V). This data set contains 1500 records.

Name                                 Null?    Type
------------------------------------ -------- ------------
CUST_ID                              NOT NULL NUMBER
CUST_GENDER                          NOT NULL CHAR(1)
AGE                                           NUMBER
CUST_MARITAL_STATUS                           VARCHAR2(20)
COUNTRY_NAME                         NOT NULL VARCHAR2(40)
CUST_INCOME_LEVEL                             VARCHAR2(30)
EDUCATION                                     VARCHAR2(21)
OCCUPATION                                    VARCHAR2(21)
HOUSEHOLD_SIZE                                VARCHAR2(21)
YRS_RESIDENCE                                 NUMBER
AFFINITY_CARD                                 NUMBER(10)
BULK_PACK_DISKETTES                           NUMBER(10)
FLAT_PANEL_MONITOR                            NUMBER(10)
HOME_THEATER_PACKAGE                          NUMBER(10)
BOOKKEEPING_APPLICATION                       NUMBER(10)
PRINTER_SUPPLIES                              NUMBER(10)
Y_BOX_GAMES                                   NUMBER(10)
OS_DOC_SET_KANJI                              NUMBER(10)

SQL> select count(*) from new_data_to_score;


The next step is to run the the DBMS_DATA_MINING.APPLY procedure. The parameters that we need to feed into this procedure are

Parameter Name Description
Model_Name CLAS_DECISION_TREE  -- we imported this model from our test database
Case_Id_Column_Name CUST_ID  -- this is the PK
Result_Table_Name NEW_DATA_SCORED   -- new table that will be created that contains the Prediction and Probability.

The NEW_DATA_SCORED table will contain 2 records for each record in the source data (NEW_DATA_TO_SCORE). For each record in NEW_DATA_TO_SCORE we will have one record for the each of the Target Values (O or 1) and the probability for each target value. So for our NEW_DATA_TO_SCORE, which contains 1,500 records, we will get 3,000 records in the NEW_DATA_SCORED table.

To apply the model to the new data we run:

  model_name => 'CLAS_DECISION_TREE',
  data_table_name => 'NEW_DATA_TO_SCORE',
  case_id_column_name => 'CUST_ID',
  result_table_name => 'NEW_DATA_SCORED');

This takes 1 second to run on my laptop, so this apply/scoring of new data is really quick.

The new table NEW_DATA_SCORED has the following description

Name                            Null?    Type
------------------------------- -------- -------
CUST_ID                         NOT NULL NUMBER
PREDICTION                               NUMBER
PROBABILITY                              NUMBER

SQL> select count(*) from NEW_DATA_SCORED;


We can now look at the prediction and the probabilities

SQL> select * from NEW_DATA_SCORED where rownum <=12;

---------- ---------- -----------
    103001          0           1
    103001          1           0
    103002          0  .956521739
    103002          1  .043478261
    103003          0  .673387097
    103003          1  .326612903
    103004          0  .673387097
    103004          1  .326612903
    103005          1  .767241379
    103005          0  .232758621
    103006          0           1
    103006          1           0

12 rows selected.

How Many Sleeps to Santa

to_date('25/12/2011','DD/MM/YYYY') - trunc(sysdate) "How Many Sleep to Santa"
from dual;

How Many Sleep to Santa

Thursday, November 17, 2011

Call for Presentations : OUG Ireland Conference 2012

The call for presentations for the annual Oracle User Group Ireland conference has been posted in last few days.
The conference is planned for March 2012 and the venue will be picked over the next few weeks.
I’m on organising committee this year. It is hoped to have a number of parallel streams covering core Database Technology, BI (&EPM), Development (including Fusion).
If you are interested in presenting a short presentation of approx. 45 minutes (including time for questions), then you will need to submit your Topic and Abstract using the following link :
The conference is not limited to presenters from Ireland and it is hoped to get a number of well known Oracle experts and Oracle ACEs to come to Dublin for the day.
What kind of topics are of interest. Well pretty much anything Oracle. We have all come across something interesting in our jobs that we could share, be it using a particular technique, new features, sharing experiences, best practices, product demos, etc
I’ve already submitted a presentation on Oracle Data Miner.
There is a Twitter hash tag for the Oracle Conference #oug_ire2012.  So add this to your Twitter tool to follow developments and announcements about the conference.
If you have any question about the conference drop me a email.

Wednesday, November 16, 2011

My UKOUG Conference 2011 Schedule

UK Oracle User Group Conference 2011

The UKOUG conference will be in a couple of weeks. I have my flights and hotel booked, and I’ve just finished selecting my agenda of presentations. I really enjoy this conference as it serves many purposes including, finding new directions Oracle is taking, new product features, some upskilling/training, confirming that the approaches that I have been using on projects are valid, getting lots of hints and tips, etc.

One thing that I always try to do and I strongly everyone (in particular first timers) to do is to go to 1 session everyday that is on a topic or product that you know (nearly) nothing about.  You might discover that you know more than you think or you may learn something new that can be feed into some project on your return or over the next 12 months.

My agenda for the conference currently looks Very busy and in between these session, there is the exhibition hall, meetings with old and new friends, meetings with product/business unit managers, asking people to write articles for Oracle Scene, checking out possible presenters to come to Ireland for our conference in March 2012, etc.  Then there is my presentation on the Wednesday afternoon.


I’ll miss most of the Oak Table event on the Sunday but I hope to make it in time for

16:40-17:30 : Performance & High Availability Panel Session


9:20-9:50 : Keynote by Mark Sunday, Oracle (H1)
10:00-10:45 : The Future of BI & Oracle roadmap, Mike Durran, Oracle (H5)
11:05-12:05 : Implementing Interactive Maps with OBIEE 11g, Antony Heljula, Peak Indicators (H10A)
12:15-13:15 : OBI 11g Analysis & Reporting New Features, Mark Rittman (8A)
14:30-15:15 : Master Data Management – What is it & how to make it work – Robert Barnett, Hub Solutions Designs (H10A)
16:20-17:35 : Dummies Guide to Oracle ADF, Grant Ronald, Oracle, (Media Suite)
16:35-18:30 : The DB Time Performance Method, Graham Wood, Oracle (H8A)
17:45-18:30 : Performance & Stability with Oracle 11g SQL Plan Management, Doug Burns (H1)
17:45-18:30 : Experiences in Virtualization, Michael Doherty (H10A)
19:45-20:45 : Exhibition Welcome Drinks
20:45-Late : Focus Pubs


9:00-11:00 : Next Generation BI Architectures Masterclass, Andrew Bond, Oracle (H10B)
10:10-10:55 : Who’s afraid of Analytic Functions, Alex Nuijten, Maxima (H5)
11:15-12:15 : Analysing Your Data with Analytic Functions, Carl Dudley, (H9)
11:25-13:25 : Using a Physical Standby to Minimize Downtime for DB Release or Server Change, Michael Abbey, Pythian (Media Suite)
14:40-15:25 : How note to make the headlines, Mark Clewett, Hitachi (H10A)
14:40-15:25 : APEX Back to Basics, Paul Broughton, APEX Evangelists (H9)
15:35-16:20 : Can People be identified in the database, Pete Finnigan (H1)
16:40-18:35 : OTN Hands-on Workshop, Todd Trichler, Oracle (H8A)
17:50-18:35 : SQL Developer Data Modeler as a replacement for Oracle Designer, Paul Bainbridge, Fujitsu, (H8B)
18:45-19:45 : Keynote : Future of Enterprise Software and Oracle, Ray Wang, Constellation Research (H1)
20:00-Late : Evening Social & Networking


9:00-10:00 : Oracle 11g Database: Automatic Parallelism, Joel Goodman, Oracle (H9)
9:00-10:00 : Big Data: Learn how to predict the future, Keith Laker, Oracle (H8B)
10:10-10:55 : All about indexes – What to index, when and how, Mark Bobak, ProQuest (H5)
11:20-12:30 : Using Application Express to Build Highly Accessible Products, Anthony Rayner, Oracle (H8A)
12:30-13:30 : Practical uses for APEX Dictionary, John Scott, APEX Evangelists (H8A)
15:20-16:05 : How to deploy you Oracle Data Miner 11g R2 Workflows in a Live Environment – Me  (H7B)
16:15-17:00 : Next Generation Data Warehousing, Kulvinder Hari, Oracle (H8A)
16:15-17:00 : Beyond RTFM and WTF Message Moments. Introducing a new standard: Oracle Fusion Applications User Assistance, Ultan O’Broin (Executive Room 7)

I know I have some overlapping sessions, but I will decide on the date which of these I will attend.

As you an see I will be following the BI stream mainly, with a few sessions on the Database and Development streams too.

This year there is a smart phone app help us organise our agenda, meetings, etc, The only downside is that the app does not import the agenda that I created on the website. So I have to do it again. Maybe for next year they will have an import agenda feature.

New UKOUG mobile app – Launched October 2011

Wednesday, November 9, 2011

ODM–PL/SQL API for Exporting & Importing Models

In a previous blog post I talked about how you can take a copy of a workflow developed in Oracle Data Miner, and load it into a new schema.
When you data mining project gets to a mature stage and you need to productionalise the data mining process and model updates, you will need to use a different set of tools.

As you gather more and more data and cases, you will be updating/refreshing your models to reflect this new data. The new update data mining model needs to be moved from the development/test environment to the production environment. As with all things in IT we would like to automate this updating of the model in production.
There are a number of database features and packages that we can use to automate the update and it involves the setting up of some scripts on the development/test database and also on the production database.

These steps include:

  • Creation of a directory on the development/test database
  • Exporting of the updated Data Mining model
  • Copying of the exported Data Mining model to the production server
  • Removing the existing Data Mining model from production
  • Importing of the new Data Mining model.
  • Rename the imported mode to the standard name

The DBMS_DATA_MINING PL/SQL package has 2 functions that allow us to export a model and to import a model. These functions are an API to the Oracle Data Pump. The function to export a model is DBMS_DATA_MINING.EXPORT_MODEL and the function to import a model is DBMS_DATA_MINING.IMPORT_MODEL.The parameters to these function are what you would expect use if you were to use Data Pump directly, but have been tailored for the data mining models.

Lets start with listing the models that we have in our development/test schema:

SQL> connect dmuser2/dmuser2
SQL> SELECT model_name FROM user_mining_models;


Create/define the directory on the server where the models will be exported to.

CREATE OR REPLACE DIRECTORY DataMiningDir_Exports AS 'c:\app\Data_Mining_Exports';

The schema you are using will need to have the CREATE ANY DIRECTORY privilege.

Now we can export our mode. In this example we are going to export the Decision Tree model (CLAS_DT_1_6)

The function has the following structure

     filename IN VARCHAR2,
     directory IN VARCHAR2,
     model_filter IN VARCHAR2 DEFAULT NULL,
     operation IN VARCHAR2 DEFAULT NULL,
     remote_link IN VARCHAR2 DEFAULT NULL,

If we wanted to export all the models into a file called Exported_DM_Models, we would run:

DBMS_DATA_MINING.EXPORT_MODEL('Exported_DM_Models', 'DataMiningDir');

If we just wanted to export our Decision Tree model to file Exported_CLASS_DT_Model, we would run:

DBMS_DATA_MINING.EXPORT_MODEL('Exported_CLASS_DT_Model', 'DataMiningDir', 'name in (''CLAS_DT_1_6'')');

Before you can load the new update data mining model into your production database we need to drop the existing model. Before we do this we need to ensure that this is done when the model is not in use, so it would be advisable to schedule the dropping of the model during a quiet time, like before or after the nightly backups/processes.


Warning : When importing the data mining model, you need to import into a tablespace that has the same name as the tablespace in the development/test database.  If the USERS tablespace is used in the development/test database, then the model will be imported into the USERS tablespace in the production database.

Hint : Create a DATAMINING tablespace in your development/test and production databases. This tablespace can be used solely for data mining purposes.

To import the decision tree model we exported previously, we would run

DBMS_DATA_MINING.IMPORT_MODEL('Exported_CLASS_DT_Model', 'DataMiningDir', 'name=’CLAS_DT_1_6''', 'IMPORT', null, null, 'dmuser2:dmuser3');

We now have the new updated data mining model loaded into the production database.

The final step before we can start using the new updated model in our production database is to rename the imported model to the standard name that is being used in the production database.


Scheduling of these steps
We can wrap most of this up into stored procedures and have schedule it to run on a semi-regular bases, using the DBMS_JOB function. The following example schedules a procedure that controls the importing, dropping and renaming of the models.

DBMS_JOB.SUBMIT(jobnum.nextval, 'import_new_data_mining_model', trunc(sysdate), add_month(trunc(sysdate)+1);

This schedules the the running of the procedure to import the new data mining models, to run immediately and then to run every month.

Saturday, November 5, 2011

What Conference ? If I had the time and money

If I had lots of free time and enough money what conferences would I go to around the world. I regularly get asked for recommendations on what conferences should a person attend. It all depends on what you want to get out of your conference trip. Be is training, education, information building, networking, etc. or to enjoy the local attractions.

The table below is my preferred list of conferences to attend. All of the conferences below are focused on two main areas. The first area is Oracle  and the second area is that of Data Mining/Predictive Analytics.

I hope you find the list useful. If you can recommend some others let me know.

Month Conference


Annual Ireland Oracle Conference – Dublin, Ireland

Predictive Analytics World – USA (San Francisco)

Text Analytics World

Hotsos Symposium


Collaborate (IOUG Conference USA)

Enterprise Data World (USA)

Miracle OpenWorld (Denmark)


OUG Harmony (Finland)


Oracle Development Tools User Group Kaleidoscope (Kscope)

Data Governance – Summer Conference

Oracle Benelux User Group Conference


VirtaThon – Online Oracle Conference


ACM SIGKDD Conference on KDD & Data Mining


Oracle Open World – San Francisco, USA

Predictive Analytics World – USA (New York)

SAS Analytics Conference


TDWI World Conference

Data Governance – Winter Conference (USA)

Predictive Analytics World – UK

International Conference on Data Mining & Engineering (ICDMKE)

Australia Oracle User Group Conference

Germany Oracle User Group Conference (DOAG)


Annual UKOUG Conference – Birmingham, UK

IEEE International Conference on Data Mining (ICDM)

Oracle Open World Latin America

There is a lot of conferences in the October, November and December months. Some of these are on overlapping dates, which is a pity. Perhaps the organisers of some of these conferences. Also during the January and February months there does not seem to be any conferences in the areas.

If you would like to sponsor a trip to one or more of these then drop me an email Smile

Thursday, November 3, 2011

ODM 11.2 Data Dictionary Views.

The Oracle 11.2 database contains the following Oracle Data Mining views. These allow you to query the database for the metadata relating to what Data Mining Models you have, what the configurations area and what data is involved.


Describes the high level information about the data mining models in the database.  Related views include DBA_MINING_MODELS and USER_MINING_MODELS.

Attribute Data Type Description
OWNER Varchar2(30) NN Owner of the mining model
MODEL_NAME Varchar2(30) NN Name of the mining model
MINING_FUNCTION Varchar2(30) What data mining function to use
ALGORITHM Varchar2(30) Algorithm used by the model
CREATION_DATE Date NN Date model was created
BUILD_DURATION Number Time in seconds for the model build process
MODEL_SIZE Number Size of model in MBytes
COMMENTS Varchar2(4000)  
Lets query the my DMUSER2 data mining schema. This was created during a previous post where we exported some ODM models from schema and loaded them into DMUSER2 schema

SELECT model_name, 

-------------  ---------------- -------------------------- -------------- ----------
CLAS_SVM_1_6   CLASSIFICATION    SUPPORT_VECTOR_MACHINES                     3      .1515
CLAS_DT_1_6    CLASSIFICATION    DECISION_TREE                               2      .0842
CLAS_GLM_1_6   CLASSIFICATION    GENERALIZED_LINEAR_MODEL                    3      .0877
CLAS_NB_1_6    CLASSIFICATION    NAIVE_BAYES                                 2      .0459


Describes the attributes of the data mining models.  Related views are DBA_MINING_MODEL_ATTRIBUTES and USER_MINING_MODEL_ATTRIBUTES.

Attribute Data Type Description
OWNER Varchar2(30) NN Owner of the mining model
MODEL_NAME Varchar2(30) NN Name of the mining mode
ATTRIBUTE_NAME Varchar2(30) NN Name of the attribute
ATTRIBUTE_TYPE Varchar2(11) Logical type of attribute
NUMERICAL – numeric data
CATEGORICAL – character data
DATA_TYPE Varchar2(12) Data type of attribute
DATA_LENGTH Number Length of data type
DATA_PRECISION Number Precision of a fixed point number
DATA_SCALE Number Scale of the fixed point number
USAGE_TYPE Varchar2(8) Indicated if the attribute was used to create the model (ACTIVE) or not (INACTIVE)
TARGET Varchar2(3) Indicates if the attribute is the target

If we take one of our data mining models that was listed about and select what attributes are used by that model;

SELECT attribute_name,
from all_mining_model_attributes
where model_name = 'CLAS_DT_1_6';

------------------------------ ----------- -------- ---
AGE                            NUMERICAL   ACTIVE   NO
Y_BOX_GAMES                    NUMERICAL   ACTIVE   NO

The first thing to note here is that all the attributes are listed as ACTIVE. This is the default and will be the case for all attributes for all the algorithms, so we can ignore this attribute in our queries, but it is good to check just in case.

The second thing to note is for the last row we have the AFFINITY_CARD has a target attribute value of YES. This is the target attributes used by the classification algorithm.


Describes the setting of the data mining models. The settings associated with a model are algorithm dependent. The Setting values can be provided as input to the model build process. Alternatively, separate settings table can used.  If no setting values are defined of provided, then the algorithm will use its default settings.

Attribute Data Type Description
OWNER Varchar2(30) NN Owner of the mining model
MODEL_NAME Varchar2(30) NN Name of the mining model
SETTING_NAME Varchar2(30) NN Name of the Setting
SETTING_VALUE Varchar2(4000) Value of the Setting
SETTING_TYPE Varchar2(7) Indicates whether the default value (DEFAULT) or a user specified value (INPUT) is used by the model

Lets take our previous example of the 'CLAS_DT_1_6' model and query the database to see what the setting are.

column setting_value format a30
select setting_name, 
from all_mining_model_settings
where model_name = 'CLAS_DT_1_6';

SETTING_NAME            SETTING_VALUE                SETTING
----------------------- ---------------------------- -------
ALGO_NAME               ALGO_DECISION_TREE           INPUT
PREP_AUTO               ON                           INPUT
TREE_TERM_MINPCT_NODE   .05                          INPUT
TREE_TERM_MINREC_SPLIT  20                           INPUT
TREE_TERM_MINPCT_SPLIT  .1                           INPUT
TREE_TERM_MAX_DEPTH     7                            INPUT
TREE_TERM_MINREC_NODE   10                           INPUT

Wednesday, November 2, 2011

Tom Kyte Seminar Day–Dublin

On Wednesday 2nd December, I attended a full day of presentations given by Tom Kyte of Oracle ( Tom covered a number of topics and these included some of his Oracle Open World presentations.

The topics that were covered included

  • 5 things about SQL (OOW11)
  • Database Option Packs
  • 5 things about PL/SQL (OOW11)
  • Q&A Ask Tom Session

All of these presentations can be downloaded from Tom’s website

Tom wont be presenting at the annual UKOUG conference in December, but he is hoping to be there next year (2012).


Monday, October 31, 2011

ODM 11.2–Data Mining PL/SQL Packages

The Oracle 11.2 database contains 3 PL/SQL packages that allow you to perform all (well almost all) of your data mining functions.

So instead of using the Oracle Data Miner tool you can write some PL/SQL code that will you to do the same things.

Before you can start using these PL/SQL packages you need to ensure that the schema that you are going to use has been setup with the following:

  • Create a schema or use and existing one
  • Grant the schema all the data mining privileges: see my earlier posting on how to setup an Oracle schema for data mining – Click here and YouTube video
  • Grant all necessary privileges to the data that you will be using for data mining

The first PL/SQL package that you will use is the DBMS_DATA_MINING_TRANSFORM. This PL/SQL package allows you to transform the data to make it suitable for data mining. There are a number of functions in this package that allows you to transform the data, but depending on the data you may need to write your own code to perform the transformations. When you apply your data model to the test or the apply data sets, ODM will automatically take the transformation functions defined using this package and apply them to the new data sets.

The second PL/SQL package is DBMS_DATA_MINING. This is the main data mining PL/SQL package. It contains functions to allow you to:

  • To create a Model
  • Describe the Model
  • Exploring and importing of Models
  • Computing costs and text metrics for classification Models
  • Applying the Model to new data
  • Administration of Models, like dropping, renaming, etc

The next (and last) PL/SQL package is DBMS_PREDICTIVE_ANALYTICS.The routines included in this package allows you to prepare data, build a model, score a model and return results of model scoring. The routines include EXPLAIN which ranks attributes in order of influence in explaining a target column. PREDICT which predicts the value of a target attribute based on values in the input. PROFILE which generates rules that describe the cases from the input data.

Over the coming weeks I will have separate blog posts on each of these PL/SQL packages. These will cover the functions that are part of each packages and will include some examples of using the package and functions.