Thursday, April 25, 2013

Oracle Magazine-September/October 1999

The headline articles in the September/October 1999 edition of Oracle Magazine focused on how the Oracle technology can be used to educate staff and to keep their skills up to date. either on site or remote via on-demand training resources.


Other articles included:

  • Oracle announce that they have acquired Thinking Machine’s data mining business. This data mining product was called Darwin and is now called Oracle Data Mining. I will have a separate blog post for this announcement.
  • Oracle 8i Lite has shipped and comes with three component: Oracle Lite a single user (50K to 750K foot print), Web-to-Go allows users to access the same data and web applications both online and offline, iConnect that was a flexible architecture that enables reliable and scalable bi-directional synchronization of data and applications. Oracle 8i Lite was supported on MS Windows 95, 98 and NT, Windows CE, Palm OS and EPOC 32.
  • Oracle XML Parser for C and Oracle XML Parser for C++ are released and supports DOM and Simple API for XML (SAX) interfaces.
  • Oracle XML SQL utilities and XSQL Servlet facilitates the reading and writing of XML information from and to the Oracle database.
  • Siemens announce that they plan to build an Oracle 8i Applicance on its Primergy line of servers, based on Intel Pentium II Xeon processors.
  • Singapore Telecom’s Magix Server delivers the World’s first nationwide video on demand service. Their 12,000 subscriber were able to use a web-browser to select a video from the Megix Web side and SingTel automates the streaming of them to their computer.
  • Oracle 8i comes with some improvements in PL/SQL. These included Autonomous Transactions, Native Dynamic SQL, Invoker rights procedures, user-defined operators, new operators, bulk binds.
  • Part 2 of the article on exporting an Oracle Database to a Flat File. In this part of the article it looks at how you can use the UTL_FILE package.
  • How you can speed up query response times by using a Materialized Views. The article suggests the following steps to analyze the performance impact:
    • Configure the server parameters
    • Grant privileges to the appropriate schema
    • Create a materialized view
    • Refresh the optimizer statistics
    • Confirm that the materialized view is being used
    • Manually refresh a materialized view
  • Oracle introduces Oracle Log Miner to allow a DBA to analyze the REDO log files

Tuesday, April 23, 2013

Oracle buys Darwin back in 1999

The following is an extract from 1999 September/October edition of Oracle Magazine, about Oracle buying Thinking Machines. Their data mining software Darwin was integrated into the Oracle Database and renamed Oracle Data Miner.

Oracle Corporation’s recent acquisition of Thinking Machines’ data mining business extends Oracle’s data warehouse platform and business intelligence solution to include enterprise reporting, ad hoc query, advanced analysis and data mining software based on a common internet platform.

Oracle plans to incorporate the data mining software as an integral feature of Oracle Applications Customer Relationship Management site, which will facilitate the implementation of the e0business solutions developed by Oracle customers. In addition o the software technology, Oracle will receive rights to the domains and

About Thinking Machines

Originally founded in 1983, Thinking Machines Corporation revolutionized high performance computing with its massively parallel supercomputing technology. The company has since evolved to focus exclusively on its Darwin data mining software for database marketing in the financial services and telecommunications industries. Darwin analyzes massive volumes of customer transaction, demographic and psychographic data, which can often amount to hundreds of millions of customer data records.

These advanced analyses help companies profile and target customers with greater accuracy, which allows companies to reduce customer attrition, assess customer profitability, cross sell to existing customers and detect fraud.

Darwin puts powerful data mining techniques in the hands of general business users and experienced analysts alike. Each to use wizards automate data mining while providing advanced users with full control over all options and parameters. The Darwin software combines advanced analytics - including neural networks, decisions trees and memory based reasoning, with impressive power and performance.

The solution’s one button model code generation, powerful scripting language and robust software development kit bring prediction capabilities to sales, call center, marking and the web.

Platforms and Languages

Darwin runs on Sun Microsystems and Hewlett-Packard servers and exports data mining models in C, C++ and Java for execution within Oracle Databases. A Microsoft Windows NT release is planned for later this year.”

Friday, April 19, 2013

Part 2–Getting start with Statistics for Oracle Data Science projects

This is the second blog on getting started with Statistics for Oracle Data Science projects.

In this blog post I will look at 3 more useful statistical functions that are available in the Oracle database. Remember these come are standard with the database. The first function I will look at is the WIDTH_BUCKET function. This can be used to create some histograms of the data. A common task in analytics projects is to produce some cross tabs of the data. Oracle has the STATS_CROSSTAB. The last function I will look the different ways you an sample the data.

Histograms using WIDTH_BUCKET

When exploring your data it is useful to group values together into a number of buckets. Typically you might want to define the width of each bucket yourself before passing the data into your data mining tools, but before you can decide what these are you need to do some exploring using a variety of widths. A good way to do this is to use the WIDTH_BUCKET function. This takes the following inputs:

Expression: This is the expression or attribute on which the you want to build the histogram.

Min Value: This is the lower or starting value of the first bucket

Max Value: This is the last or highest value for the last bucket

Num Buckets: This is the number of buckets you want created.

Typically the Min Value and the Max Value can be calculated using the MIN and MAX functions. As a starting point you generally would select 10 for the number of buckets. This is the number you will change, downwards as well as upwards, to if a particular pattern exists in the attribute.

Using the example scenario that I used in the first blog post, let us start by calculating the MIN and MAX for the AGE attribute.


Lets say that we wanted to create 10 buckets. This would create a bucket width of 7.3 for each bucket, giving us the following.

Bucket 1 : 17-24.3
Bucket 2: 24.3-31.6
Bucket 3: 31.6-38.8
Bucket 4: 38.8-46.1
Bucket 5: 46.1-53.4
Bucket 6: 53.4-60.7
Bucket 7: 60.7-68
Bucket 8: 68-75.3
Bucket 9: 75.3-82.6
Bucket 10: 82.6-90

These are the buckets that the WIDTH_BUCKET function gives us in the following:

SELECT cust_id,
                    (SELECT min(age) from mining_data_build_v),
                    (select max(age)+1 from mining_data_build_v),
                    10)  bucket
from mining_data_build_v
where rownum <=12
group by cust_id, age


An additional level of detail that is needed to allow us to plot the histograms for AGE, we need to aggregate up for all the records by bucket.

select intvl, count(*) freq
from (select width_bucket(salary,
(select min(salary) from employees),
(select max(salary)+1 from employees), 10) intvl
from HR.employees)
group by intvl
order by intvl;


We can take this code and embed it into the GATHER_DATA_STATS procedure that I gave in my Part 1 blog post.

Cross Tabs using STATS_CROSSTAB

Typically cross tabulation (or crosstabs for short) is a statistical process that summarises categorical data to create a contingency table. They provide a basic picture of the interrelation between two variables and can help find interactions between them.

Because Crosstabs creates a row for each value in one variable and a column for each value in the other, the procedure is not suitable for continuous variables that assume many values.

In Oracle we can perform crosstabs using one of their reporting tools. But if you don’t have one of these we will need to use the in-database function STATS_CROSSTAB. This function takes three parameters, the first two of these are the attributes you want to compare and the third is what test we want to perform. The tests available include:

  • CHISQ_OBS: Observed value of chi-squared
  • CHISQ_SIG: Significance of observed chi-squared
  • CHISQ_DF: Degree of freedom for chi-squared
  • PHI_COEFFICIENT: Phi coefficient
  • CRAMERS_V: Cramer’s V statistic
  • CONT_COEFFICIENT: Contingency coefficient
  • COHENS_K: Cohen’s kappa

CHISQ_SIG is the default.

Now let us look at some examples using our same data set.


Sampling Data

When our datasets are of relatively small size consisting of a few hundred thousand records we can explore the data is a relatively short period of time. But if your data sets are larger that that you may need to explore the data by taking a sample of it. What sampling does is that it takes a “random” selection of records from our data set up to the new number of records we have specified in the sample.

In Oracle the SAMPLE function takes a percentage figure. This is the percentage of the entire data set you want to have in the Sampled result. 


There is also a variant called SAMPLE BLOCK and the figure given is the percentage of records to select from each block.


Each time you use the SAMPLE function Oracle will generate a random seed number that it will use as a Seed for the SAMPLE function. If you omit a Seed number (like in the above examples), you will get a different result set in each case and the result set will have a slightly different number of records. If you run the sample code above over and over again you will see that the number of records returned varies by a small amount.

If you would like to have the same Sample data set returned each time then you will need to specify a Seed value. The Seed much be an integer between 0 and 4294967295.


In this case because we have specified the Seed we get the same “random” records being returned with each execution.

Thursday, April 11, 2013

Part 1–Getting started with Statistics for Oracle Data Science projects

With all analytics or data science projects one of the first steps typically involves the extraction of data from various sources, merging the data and then performing various statistics.

The extraction and merging of the data is well covered by lots of other people blogging about how to use Oracle Data Integration (ODI), Oracle Warehouse Builder (OWB), among many others.

What I’m going to look at in this series of blog posts will be what statistical functions you might look at using in the Oracle and how to use them.

  • This the first blog post in the series will look at the DBMS_STAT_FUNCS PL/SQL package, what it can be used for and I give some sample code on how to use it in your data science projects. I also give some sample code that I typically run to gather some additional stats.
  • The second blog post will look at some of the other statistical functions that exist in SQL that you will/may use regularly in your data science projects.
  • The third blog post will provide a summary of the other statistical functions that exist in the database.

These statistical functions can be grouped into 2 main types. The first is the descriptive statistics that are available by using the DBMS_STAT_FUNCS PL/SQL package and then there is the extensive list of other SQL stats functions.  It is worth mentioning at this point that all these stats packages and functions come as standard in the database (i.e. they are FREE, you do not have to pay for an add on option for the database to use them). So once you have you Oracle database installed you can start using them. There is no need to spend money buying another stats package to do stats. All you need to know is some SQL and what the stats functions are.


One of the stats package that I use a lot is the SUMMARY function. This is part of the DBMS_STAT_FUNC PL/SQL package. This package calculates a number of common statistics for an attribute in a table. Yes that’s correct, it only gather statistics for just one attribute. So you will have to run it for all the numeric attributes in the table.

For does people who are familiar with the Oracle Data Miner tool, the explore data node produces a lot of these statistics that the SUMMARY function produces. See below for details of how to produce the Histograms.

The SUMMARY function has the following parameters


Although you will probably be running this this function on the data in your schema you still have to give the schema name. The table name is the name of the table where the data exists, the column name is the name of the column that contains the actual data you want to analyse, and the ‘s’ is the record that will be returned by the SUMMARY function that contains all the summary information.

An example of the basic script to run the SUMMARY function is given below. It will use the data that is available in the sample schemas and the views that where setup for the Oracle Data Mining sample schemas. The table (or in this case the view) that we are going to use is the MINING_DATA_BUILD_V. What we are going to do is to replicate some of what the Explore Node does in the Oracle Data Miner tool.

set serveroutput on

   s         DBMS_STAT_FUNCS.SummaryType;

   dbms_output.put_line('SUMMARY STATISTICS');
   dbms_output.put_line('Count  : '||s.count);
   dbms_output.put_line('Min    : '||s.min);
   dbms_output.put_line('Max    : '||s.max);
   dbms_output.put_line('Range  : '||s.range);
   dbms_output.put_line('Mean   : '||round(s.mean));
   dbms_output.put_line('Mode Count : '||s.cmode.count);
   dbms_output.put_line('Mode        : '||s.cmode(1));
   dbms_output.put_line('Variance    : '||round(s.variance));
   dbms_output.put_line('Stddev      : '||round(s.stddev));
   dbms_output.put_line('Quantile 5  : '||s.quantile_5);
   dbms_output.put_line('Quantile 25 : '||s.quantile_25);
   dbms_output.put_line('Median      : '||s.median);
   dbms_output.put_line('Quantile 75 : '||s.quantile_75);
   dbms_output.put_line('Quantile 95 : '||s.quantile_95);
   dbms_output.put_line('Extreme Count : '||s.extreme_values.count);
   dbms_output.put_line('Extremes      : '||s.extreme_values(1));
   dbms_output.put_line('Top 5 : '||s.top_5_values(1)||','||
   dbms_output.put_line('Bottom 5 : '||s.bottom_5_values(5)||','||


We can compare this to what is produced by the Explore Node in ODM



We can see that the Explore Node gives us more statistics to help us with understanding the data.

What Statistics does the Explore Node produce

We can see the actual SQL code that the Explore Node runs to get the statistics that are displayed in the Explore Node View Data window. To do this you will need to right-click on the Explore Node and move the mouse down to the Deploy option. The submenu will open and select ‘SQL to Clipboard’ from the list. Open a text editor and past the code. You  will need to tidy up some of this code to point it at the actual data source you want. You will get the following

SELECT /*+ inline */  ATTR, 

Where OUTPUT_1_23 is a working table that ODM has created to store intermediate results from some of its processing. In this case the Explore Node. You will need to change this to the ODM working table in your schema.

This query does not perform any of the statistics gathering. It just presents the results.

Creating our own Statistics gathering script – Part 1

The attribute names in the above SQL query tells us what statistics functions within Oracle that were used. We can replicate this statistics gathering task using the following script. There are four parts to this script. The first part gathers most of the common statistics for the attribute. The second and third parts calculate the Skewness and Kurtosis for the attribute. The final (fourth) part combines the first three parts and lists the outputs.

The one statistic function that we are not including at this point is the Histogram information. I will cover this in the next (second) blog post on statistics.

The following script has the data source table/view name included (MINING_DATA_BUILD_V) and the attribute we are going to use (AGE).  You will need to modify this script to run it for each attribute.

    basic_statistics AS (select (sum(CASE WHEN age IS NULL THEN 1 ELSE 0 END)/COUNT(*))*100 null_percent,
          count(*)    num_value,
          count(distinct age)   distinct_count,
          (count(distinct age)/count(*))*100     distinct_percent,
          avg(age)      avg_value,
          min(age)      min_value,
          max(age)     max_value,
          stddev(age)  std_value,
          stats_mode(age)   mode_value,
          variance(age)       var_value
        from   mining_data_build_v),
    skewness AS (select avg(SV) S_value
                 from (select power((age - avg(age) over ())/stddev(age) over (), 3) SV
                       from mining_data_build_v) ),
    kurtosis AS (select avg(KV) K_value
                 from (select power((age - avg(age) over ())/stddev(age) over (), 4) KV
                       from mining_data_build_v) )
SELECT null_percent,
from basic_statistics,


Part 2 – Lets do it for all the attributes in a table

In the code above I’ve shown how you can gather the statistics for one particular attribute of one table.But in with an analytics project you will want to gather the statistics on all the attributes.

What we can do is to take the code above and put it into a procedure. This procedure accepts a table name as input, loops through the attributes for this table and calculates the various statistics. The statistics are saved in a table called DATA_STATS (see below).

drop table data_stats;

create table DATA_STATS (
table_name VARCHAR2(30) NOT NULL,
column_name VARCHAR2(30) NOT NULL,
data_type VARCHAR2(106) NOT NULL,
data_length NUMBER,
data_percision NUMBER,
data_scale NUMBER,
num_records NUMBER,
distinct_count NUMBER,
null_percent NUMBER,
distinct_percent NUMBER,
avg_value NUMBER,
min_value NUMBER,
max_value NUMBER,
std_value NUMBER,
mode_value VARCHAR2(1000),
var_value NUMBER,
s_value NUMBER,
k_value NUMBER,
PRIMARY KEY (table_name, column_name));

This is one of the first things that I do when I start on a new project. I create the DATA_STATS table and run my procedure GATHER_DATA_STATS for each table that we will be using. By doing this it allows me to have a permanent records of the stats for each attribute and saves me time in having to rerun various stats at different points of the project. I can also use these stats to produces some additional stats or to produce some graphs.

He is the code for the GATHER_DATA_STATS procedure.

CREATE OR REPLACE PROCEDURE gather_data_stats(p_table_name IN varchar2) AS

   cursor c_attributes (c_table_name varchar2)
                       is SELECT table_name,
                          FROM user_tab_columns
                          WHERE table_name = upper(c_table_name);

   v_sql     NUMBER;
   v_rows    NUMBER;
   dbms_output.put_line('Starting to gather statistics for '||upper(p_table_name)||' at '||to_char(sysdate,'DD-MON-YY HH24:MI:SS'));

   FOR r_att in c_attributes(p_table_name) LOOP
      -- remove any previously generated stats
      v_sql := DBMS_SQL.OPEN_CURSOR;
      DBMS_SQL.PARSE(v_sql, 'delete from DATA_STATS where table_name = '''||r_att.table_name||''' and column_name = '''||r_att.column_name||'''', DBMS_SQL.NATIVE);
      v_rows := DBMS_SQL.EXECUTE(v_sql);
--dbms_output.put_line('delete from DATA_STATS where table_name = '''||r_att.table_name||''' and column_name = '''||r_att.column_name||'''');

      IF r_att.data_type = 'NUMBER' THEN
         dbms_output.put_line(r_att.table_name||' : '||r_att.column_name||' : '||r_att.data_type);

         -- setup the insert statement and execute
         v_sql := DBMS_SQL.OPEN_CURSOR;
         DBMS_SQL.PARSE(v_sql, 'insert into data_stats select '''||r_att.table_name||''', '''||r_att.column_name||''', '''||r_att.data_type||''', '||r_att.data_length||', '||nvl(r_att.data_precision,0)||', '||nvl(r_att.data_scale,0)||', count(*) num_value, (sum(CASE WHEN '||r_att.column_name||' IS NULL THEN 1 ELSE 0 END)/COUNT(*))*100 null_percent, count(distinct '||r_att.column_name||') distinct_count, (count(distinct '||r_att.column_name||')/count(*))*100 distinct_percent, avg('||r_att.column_name||') avg_value, min('||r_att.column_name||') min_value, max('||r_att.column_name||') max_value, stddev('||r_att.column_name||') std_value, stats_mode('||r_att.column_name||') mode_value, variance('||r_att.column_name||') var_value, null, null from '|| r_att.table_name, DBMS_SQL.NATIVE);
         v_rows := DBMS_SQL.EXECUTE(v_sql);

      ELSIF r_att.data_type IN ('CHAR', 'VARCHAR', 'VARCHAR2') THEN
         dbms_output.put_line(r_att.table_name||' : '||r_att.column_name||' : '||r_att.data_type);

         -- We need to gather a smaller number of stats for the character attributes
         v_sql := DBMS_SQL.OPEN_CURSOR;

         DBMS_SQL.PARSE(v_sql, 'insert into data_stats select '''||r_att.table_name||''', '''||r_att.column_name||''', '''||r_att.data_type||''', '||r_att.data_length||', '||nvl(r_att.data_precision,0)||', '||nvl(r_att.data_scale,0)||', count(*) num_value, (sum(CASE WHEN '||r_att.column_name||' IS NULL THEN 1 ELSE 0 END)/COUNT(*))*100 null_percent, count(distinct '||r_att.column_name||') distinct_count, (count(distinct '||r_att.column_name||')/count(*))*100 distinct_percent, null, null, null, null, stats_mode('||r_att.column_name||') mode_value, null, null, null from '|| r_att.table_name, DBMS_SQL.NATIVE);
         v_rows := DBMS_SQL.EXECUTE(v_sql);

-- dbms_output.put_line('insert into data_stats select '''||r_att.table_name||''', '''||r_att.column_name||''', '''||r_att.data_type||''', '||r_att.data_length||', '||nvl(r_att.data_precision,0)||', '||nvl(r_att.data_scale,0)||', count(*) num_value, (sum(CASE WHEN '||r_att.column_name||' IS NULL THEN 1 ELSE 0 END)/COUNT(*))*100 null_percent, count(distinct '||r_att.column_name||') distinct_count, (count(distinct '||r_att.column_name||')/count(*))*100 distinct_percent, null, null, null, null, stats_mode('||r_att.column_name||') mode_value, null, null, null from '|| r_att.table_name);
         when others then

         dbms_output.put_line('Unable to gather statistics for '||r_att.column_name||' with data type of '||r_att.data_type||'.');
      END IF;

   dbms_output.put_line('Finished gathering statistics for '||upper(p_table_name)||' at '||to_char(sysdate,'DD-MON-YY HH24:MI:SS'));

Then to run it for a table:

exec gather_data_stats('mining_data_build_v');

We can view the contents of the DATA_STATS table by executing the following in SQL*Plus or SQL Developer

select * from DATA_STATS;


Tuesday, April 2, 2013

OTN has links to two of my blog posts

Over the past couple of weeks I’ve noticed that I had a bit of a spike in my blog stats (I don’t check them often). In particular there was 2 groups of blog posts that were getting a lot of the hit.

After a bit of investigation I found out that it was do to referrals from one particular website. It was OTN or Oracle Technology Network, and more specifically it was from their webpage dedicated for Database Admins and Developer.

Yes OTN had links to my blog posts on Clustering in Oracle Data Miner and to my blog post on Are you a Type I and Type II Data Scientists.


What a surprise this was to discover!!!  and what a honour Smile

I don’t know how long they will be on the OTN webpage, but hopefully lots of people in the Oracle community will find them useful.

I’m working on my next set of Oracle Data Miner blog posts, so watch this space. Plus I’ve started work on two technical articles that I’ll be submitting to OTN over the next few weeks. So hopefully you will see these up on OTN soon.