What time the Oracle Data Mining 10g  ?

What time the Oracle Data Mining 10g ?

If you are referring to the 10.1 release of the ODM user interface, that should be available in beta form very shortly(a couple weeks) with the final release very soon after that.

Similar Messages

  • From where to download the The Oracle Data Mining sample programs for DB11g

    In the Administrator's Guide to Oracle Data Mining 11g. One of the steps is to install the the Oracle Data Mining sample programs, which, according to the author, is included in the Oracle Database Companion. The author did not specify from where to download neither the sample programs nor the database companion CD/DVD!
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    Regards,

    You should be able to download ODM DB11g sample programs from the following OTN link:
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    Choose 'Oracle Database 11g Examples (formerly Companion)'

  • How to get the Oracle Data Mining

    Hi
    I downloaded Oracle Database 10g r2 on Oracle's download website, and I want the ODM, but it is said in the white paper that ODM is a priced option. I'd like to know how to get it.

    Data Mining is a priced option shipped with Oracle Enterprise Edition. You can get Data Mining option by installing Oracle 10gR2 and select 'general purpose' with the pre-configured database. Data Mining option is installed as default in that choice.
    If for production purposes, customers do need to purchase Data Mining option lisence through Oracle sales.
    Xiafang

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    Hi All,
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    Hi,
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  • Error while installing Oracle Data miner 10G Release 2

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  • Is oracle data mining support to other databases to work datamining

    Dear CB,
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    thanks & regards
    Suresh

    Suresh,
    1) Is ODM talk to other database or not ,like can we use ODM to prepare data mining work using other database
    Yes, you can use Oracle Data Mining to talk to other databases, but ODM will need to "have" the data inside the Oracle Database during model build and model apply. You can use DB links to pull/push data to/from other Oracle and non-Oracle DBs, but all the data mining work and data transformations occur in-DB by our Oracle design. You can perform data prep, data transformation, build models and then compute dm predictions inside Oracle DB and then publish ODM results to any other dashboard/Q&R tool that can make a SQL call to Oracle to query results or ask ODM predictive model to make a real-time prediction based on current input data.
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  • Insert current date and time into Oracle date type field

    I have a JDBC current date and time insert into Oracle 9i that almost works. It submits the current date and a fixed time into the Oracle date type field. I am using Tomcat 6.0.20.
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  • Oracle Data Mining - How to use PREDICTION function with a regression model

    I've been searching this site for Data Mining Q&A specifically related to prediction function and I wasn't able to find something useful on this topic. So I hope that posting it as a new thread will get useful answers for a beginner in oracle data mining.
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    Insert into T_SALES
    (PURCHASE_WEEK, WEEK, SALES)
    Values
    (TO_DATE('12/11/2010 23:59:59', 'MM/DD/YYYY HH24:MI:SS'), 3, 77248);
    Insert into T_SALES
    (PURCHASE_WEEK, WEEK, SALES)
    Values
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    Insert into T_SALES
    (PURCHASE_WEEK, WEEK, SALES)
    Values
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    Insert into T_SALES
    (PURCHASE_WEEK, WEEK, SALES)
    Values
    (TO_DATE('01/01/2011 23:59:59', 'MM/DD/YYYY HH24:MI:SS'), 6, 90304);
    Insert into T_SALES
    (PURCHASE_WEEK, WEEK, SALES)
    Values
    (TO_DATE('01/08/2011 23:59:59', 'MM/DD/YYYY HH24:MI:SS'), 7, 44608);
    Insert into T_SALES
    (PURCHASE_WEEK, WEEK, SALES)
    Values
    (TO_DATE('01/15/2011 23:59:59', 'MM/DD/YYYY HH24:MI:SS'), 8, 95744);
    Insert into T_SALES
    (PURCHASE_WEEK, WEEK, SALES)
    Values
    (TO_DATE('01/22/2011 23:59:59', 'MM/DD/YYYY HH24:MI:SS'), 9, 129472);
    Insert into T_SALES
    (PURCHASE_WEEK, WEEK, SALES)
    Values
    (TO_DATE('01/29/2011 23:59:59', 'MM/DD/YYYY HH24:MI:SS'), 10, 110976);
    Insert into T_SALES
    (PURCHASE_WEEK, WEEK, SALES)
    Values
    (TO_DATE('02/05/2011 23:59:59', 'MM/DD/YYYY HH24:MI:SS'), 11, 139264);
    Insert into T_SALES
    (PURCHASE_WEEK, WEEK, SALES)
    Values
    (TO_DATE('02/12/2011 23:59:59', 'MM/DD/YYYY HH24:MI:SS'), 12, 87040);
    Insert into T_SALES
    (PURCHASE_WEEK, WEEK, SALES)
    Values
    (TO_DATE('02/19/2011 23:59:59', 'MM/DD/YYYY HH24:MI:SS'), 13, 47872);
    Insert into T_SALES
    (PURCHASE_WEEK, WEEK, SALES)
    Values
    (TO_DATE('02/26/2011 23:59:59', 'MM/DD/YYYY HH24:MI:SS'), 14, 120768);
    Insert into T_SALES
    (PURCHASE_WEEK, WEEK, SALES)
    Values
    (TO_DATE('03/05/2011 23:59:59', 'MM/DD/YYYY HH24:MI:SS'), 15, 98463.65);
    Insert into T_SALES
    (PURCHASE_WEEK, WEEK, SALES)
    Values
    (TO_DATE('03/12/2011 23:59:59', 'MM/DD/YYYY HH24:MI:SS'), 16, 67455.84);
    Insert into T_SALES
    (PURCHASE_WEEK, WEEK, SALES)
    Values
    (TO_DATE('3/19/2011 23:59:59', 'MM/DD/YYYY HH24:MI:SS'), 17, 100095.66);
    COMMIT;
    There are a lot of linear regression models and approaches for sales forecast out on the market, however I will focus on what oracle 11g offers i.e. package SYS.DBMS_DATA_MINING to create a model using regression as mining function and then, once the model is created, to apply prediction function on the model.
    Therefore I'll have to go through few steps:
    i) normalization of data
    CREATE OR REPLACE VIEW t_sales_norm AS
    SELECT week,
    sales,
    (sales - 91423.95)/27238.3693126778 sales_norm
    FROM t_sales;
    whereas the numerical values are the mean and the standard deviation:
    select avg(sales) from t_sales;
    91423.95
    select stddev(sales) from t_sales;
    27238.3693126778
    ii) auto-correlation. For the sake of simplicity, I will safely assume that there is no auto-correlation (no repetitive pattern in sales among the weeks). Therefore to define the lag data I will consider the whole set:
    CREATE OR REPLACE VIEW t_sales_lag AS
    SELECT a.*
    FROM (SELECT week,
    sales,
    LAG(sales_norm, 1) OVER (ORDER BY week) L1,
    LAG(sales_norm, 2) OVER (ORDER BY week) L2,
    LAG(sales_norm, 3) OVER (ORDER BY week) L3,
    LAG(sales_norm, 4) OVER (ORDER BY week) L4,
    LAG(sales_norm, 5) OVER (ORDER BY week) L5,
    LAG(sales_norm, 6) OVER (ORDER BY week) L6,
    LAG(sales_norm, 7) OVER (ORDER BY week) L7,
    LAG(sales_norm, 8) OVER (ORDER BY week) L8,
    LAG(sales_norm, 9) OVER (ORDER BY week) L9,
    LAG(sales_norm, 10) OVER (ORDER BY week) L10,
    LAG(sales_norm, 11) OVER (ORDER BY week) L11,
    LAG(sales_norm, 12) OVER (ORDER BY week) L12,
    LAG(sales_norm, 13) OVER (ORDER BY week) L13,
    LAG(sales_norm, 14) OVER (ORDER BY week) L14,
    LAG(sales_norm, 15) OVER (ORDER BY week) L15,
    LAG(sales_norm, 16) OVER (ORDER BY week) L16,
    LAG(sales_norm, 17) OVER (ORDER BY week) L17
    FROM t_sales_norm) a;
    iii) choosing the training data. Again, I will choose the whole set of 17 weeks, as for this discussion in not relevant how big should be the set of training data.
    CREATE OR REPLACE VIEW t_sales_train AS
    SELECT week, sales,
    L1, L2, L3, L4, L5, L6, L7, L8, L9, L10,
    L11, L12, L13, L14, L15, L16, L17
    FROM t_sales_lag a
    WHERE week >= 1 AND week <= 17;
    iv) build the model
    -- exec SYS.DBMS_DATA_MINING.DROP_MODEL('t_SVM');
    BEGIN
    sys.DBMS_DATA_MINING.CREATE_MODEL( model_name => 't_SVM',
    mining_function => dbms_data_mining.regression,
    data_table_name => 't_sales_train',
    case_id_column_name => 'week',
    target_column_name => 'sales');
    END;
    v) finally, where I am confused is applying the prediction function against this model and making sense of the results.
    On a search on Google I found 2 ways of applying this function to my case.
    One way is the following:
    SELECT week, sales,
    PREDICTION(t_SVM USING
    LAG(sales,1) OVER (ORDER BY week) as l1,
    LAG(sales,2) OVER (ORDER BY week) as l2,
    LAG(sales,3) OVER (ORDER BY week) as l3,
    LAG(sales,4) OVER (ORDER BY week) as l4,
    LAG(sales,5) OVER (ORDER BY week) as l5,
    LAG(sales,6) OVER (ORDER BY week) as l6,
    LAG(sales,7) OVER (ORDER BY week) as l7,
    LAG(sales,8) OVER (ORDER BY week) as l8,
    LAG(sales,9) OVER (ORDER BY week) as l9,
    LAG(sales,10) OVER (ORDER BY week) as l10,
    LAG(sales,11) OVER (ORDER BY week) as l11,
    LAG(sales,12) OVER (ORDER BY week) as l12,
    LAG(sales,13) OVER (ORDER BY week) as l13,
    LAG(sales,14) OVER (ORDER BY week) as l14,
    LAG(sales,15) OVER (ORDER BY week) as l15,
    LAG(sales,16) OVER (ORDER BY week) as l16,
    LAG(sales,17) OVER (ORDER BY week) as l17
    ) pred
    FROM t_sales a;
    WEEK, SALES, PREDICTION
    1, 55488, 68861.084076412
    2, 78336, 104816.995823913
    3, 77248, 104816.995823913
    4, 106624, 104816.995823913
    As you can see for the first row there is a value of 68861.084 and for the rest of 16 values is always one and the same 104816.995.
    Question: where is my week 18 prediction ? or maybe I should say which one is it ?
    Another way of using prediction even more confusing is against the lag table:
    SELECT week, sales,
    PREDICTION(t_svm USING a.*) pred
    FROM t_sales_lag a;
    WEEK, SALES, PREDICTION
    1, 55488, 68861.084076412
    2, 78336, 75512.3642096908
    3, 77248, 85711.5003385927
    4, 106624, 98160.5009687461
    Each row out of 17, its own 'prediction' result.
    Same question: which one is my week 18th prediction ?
    Thank you very much for all help that you can provide on this matter.
    It is as always highly appreciated.
    Serge F.

    Kindly let me know how to give input to predict the values for example script to create model is as follows
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    drop table svm_settings
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    dbms_data_mining.drop_model('MODEL_SVMR1');
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    INSERT INTO data_4svm VALUES (2,1,1);
    INSERT INTO data_4svm VALUES (3,2,4);
    INSERT INTO data_4svm VALUES (4,3,9);
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    INSERT INTO svm_settings (setting_name, setting_value) VALUES
    (dbms_data_mining.algo_name, dbms_data_mining.algo_support_vector_machines);
    INSERT INTO svm_settings (setting_name, setting_value) VALUES
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    INSERT INTO svm_settings (setting_name, setting_value) VALUES
    (dbms_data_mining.svms_active_learning, dbms_data_mining.svms_al_enable);
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    END;
    --create model
    BEGIN
    DBMS_DATA_MINING.CREATE_MODEL(
    model_name => 'Model_SVMR1',
    mining_function => dbms_data_mining.regression,
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    --to show the out put
    select class, attribute_name, attribute_value, coefficient
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    order by abs(coefficient) desc
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    FROM data_4svm a;
    Here i am not sure how to predict B values . Please suggest the proper usage . Moreover In GUI (.NET windows form ) how user can give input and system can respond using the Q1

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