data mining blog

OUG Norway Agenda is now live

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The OUG Norway spring conference (17th April – 19th April) agenda is now live and is open for registrations.

Click here for the Conference Agenda

Click here for the Conference Registration

This is a 3 day conference. The first day (17th April) will be held in the Radisson BLU Scandinavia ( Holbergsplass ) and the next two (and a bit) days will be on the Color Magic boat that will be travelling between Oslo and Kiel in Germany and back to Oslo. The boat will be arriving back in Oslo on the Saturday morning (20th April).

There will be some presentations in Norwegian, but it looks like most of the presentations will be in English. There will also be some well known names from the Oracle world presenting at this conference.

In addition to these people, I will be giving two presentations on using Predictive Analytics in Oracle using the Oracle Data Miner tool and in-database functionality.

My first presentation will be an overview of the advanced analytics option and a demonstration of what you can do using the Oracle Data Miner tool (part of SQL Developer). This presentation is currently scheduled for Thursday (18th April) at 5pm.

My second presentation will be at 9:30am on the Friday morning (19th April). In this presentation we will look at the in-database features, what can we do in SQL and PL/SQL, and we will look at what you need to do deploy you Oracle Data Mining models in a production environment.

If possible we might be able to review some new 12c new features for Oracle Data Miner Smile

The ‘Oh No You Don’t’ of (Oracle) Data Science

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Over the past couple of weeks I’ve had conversations with a large number of people about Data Science in the Oracle arena.

A few things have stood out. The first and perhaps the most important of these is that there is confusion of what Data Science actually means. Some think it is just another name for Statistics or Advanced Statistics, some Predictive Analytics or Data Mining, or Data Analysis, Data Architecture, etc.. The reality is it is not. It is more than what these terms mean and this is a topic for discussion for another day.

During these conversations the same questions or topics keep coming up and the simplest answer to all of these is taken from a Pantomime (Panto).

We need to have lots of statisticians
       ‘Oh No You Don’t !’
We can only do Data Science if we have Big Data
        ‘Oh No You Don’t !’
We can only do data mining/data science if we have 10’s or 100’s of Million of records
        ‘Oh No You Don’t !’
We need to have an Exadata machine
        ‘Oh No You Don’t !’
We need to have an Exalytics machine
        ‘Oh No You Don’t !’
We need extra servers to process the data
        ‘Oh No You Don’t !’
We need to buy lots of Statistical and Predictive Analytics software
        ‘Oh No You Don’t !’
We need to spend weeks statistically analysing a predictive model
        ‘Oh No You Don’t !’
We need to have unstructured data to do Data Science
        ‘Oh No You Don’t !’
Data Science is only for large companies
        ‘Oh No You Don’t !’
Data Science is very complex, I can not do it
        ‘Oh No You Don’t !’

Let us all say it together for one last time ‘Oh No You Don’t

In its simplest form, performing Data Science using the Oracle stack, just involves learning and using some simple SQL and PL/SQL functions in the database.

Maybe we (in the Oracle Data Science world and those looking to get into it) need to adopt a phrase that is used by Barrack Obama of ‘Yes We Can’, or as he said it in Irish when he visited Ireland back in 2011, ‘Is Feidir Linn’.

Remember it is just SQL.

My Blog Stats for 2012

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Here are the stats from my blog for 2012.

In total I’ve had almost 28,000 blog post views. This is a 7 fold increase on the number of blog post views I had in 2011.

I had 92 blog posts in 2012 and the most popular blog posts were

Top search keywords used to find my blog

  • exalytics pricing
  • oracle data mining
  • oracle data miner
  • data science
  • brendan tierney

Top Countries

  • United States  52%
  • Ireland  8%
  • United Kingdom  8%
  • India  4%
  • Russia  4%
  • Germany  3%
  • France  3%
  • Netherlands  1%
  • Canada  1%
  • Turkey  1%

Top OS

  • Windows  59%
  • Macintosh  28%
  • Linux  5%
  • iPhone  2%
  • iPad  1%

Top Browsers

  • Firefox  47%
  • Internet Explorer  26%
  • Chrome  15%
  • Safari  4%

OUG Norway April 2013 – New Year’s News

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I received an email at 23:24 on the 1st January from the OUG in Norway telling me that I’ve had two presentations accepted for the Annual OUG Norway seminar event. This will be on during the 17th-19th April.

The first day of this event (17th April) will be held in a hotel in Oslo. Then on the morning of 18th April we board the Color Magic cruise for the next two days of the conference. The ferry/cruise will go from Oslo to Kiel in Germany and then back again to Oslo, returning around 10am on Saturday 20th April.

I will be giving two presentations on the Oracle Advanced Analytics Option. The first presentation, ‘Using Predictive Analytics in Oracle’, will give an overview of the Oracle Advanced Analytics Option and will then focus on the Oracle Data Miner work-flow tool. This will presentation will include a live demo of using Oracle Data Miner to create some data mining models.

The second presentation, ‘How to Deploy and Use your Oracle Data Miner Models in Production’, builds on the examples given in the first presentation and will show how you can migrate, user and update your Oracle Data Miner models using the features available in SQL and PL/SQL. Again a demo will be given.

Association Rules in ODM-Part 4

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This is a the final part of a four part blog post on building and using Association Rules in the Oracle Database using Oracle Data Miner. The following outlines the contents of each post in the series on Association Rules

  1. This first part will focus on how to building an Association Rule model
  2. The second post will be on examining the Association Rules produced by ODM – This blog post
  3. The third post will focus on using the Association Rules on your data.
  4. The final post will look at how you can do some of the above steps using the ODM SQL and PL/SQL functions.

In my previous posts I showed how you can go about setting up for Association Rule analysis in Oracle Data Miner and how to examine the rules that are generated.

This post will focus on how we build and use association rules using the functionality that is available in SQL and PL/SQL.

Step 1 – Build the Settings Table

As with all Oracle Data Mining functions in SQL and PL/SQL you will need to setup or build a settings table. This table contains all the settings necessary to run the model build functions. It is a good idea to create a separate settings table for each model build that you complete.

CREATE TABLE assoc_sample_settings (
setting_name VARCHAR2(30),
setting_value VARCHAR2(4000));

Step 2 – Define the Settings for the Model

Before you go to generate your model you need to set some of the parameters for the algorithm. To start with you need to defined that we are going to generate an Association Rules model, turn off the Automatic Data Preparation.

We can also set 3 additional settings for Association Rules.
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The ASSO_MIN_SUPPORT has a default of 0.1 or 10%. That means that only rules that exist in 10% or more of the cases will be generated. This is really a figure that is too high. In the code below we will set this to a 1%. This matches the settings that we used in SQL Developer in my previous posts.

BEGIN

INSERT INTO assoc_sample_settings (setting_name, setting_value) VALUES

(dbms_data_mining.algo_name, dbms_data_mining.ALGO_APRIORI_ASSOCIATION_RULES);

INSERT into assoc_sample_settings (setting_name, setting_value) VALUES

(dbms_data_mining.prep_auto, dbms_data_mining.prep_auto_off);

INSERT into assoc_sample_settings (setting_name, setting_value) VALUES

(dbms_data_mining.ODMS_ITEM_ID_COLUMN_NAME, ‘PROD_ID’);

INSERT into assoc_sample_settings (setting_name, setting_value) VALUES

(dbms_data_mining.ASSO_MIN_SUPPORT, 0.01);

COMMIT;

END;

/

Step 3 – Prepare the Data

In our example scenario we are using the SALE data that is part of the SH schema. The CREATE_MODEL function needs to have an attribute (CASE_ID) that identifies the key of the shopping basket. In our case we have two attributes, so we will need to use a combined key. This combined key consists of the CUST_ID and the TIME_ID. This links all the transaction records related to the one shopping event together.

We also just need the attribute that has the information that we need. In our Association Rules (Market Basket Analysis) scenario, we will need to include the PROD_ID attribute. This contains the product key of each product that was included in the basket

CREATE VIEW ASSOC_DATA_V AS (

SELECT RANK() OVER (ORDER BY CUST_ID, TIME_ID) CASE_ID,

t.PROD_ID

FROM SH.SALES t );

Step 4 – Create the Model

We will need to use the DBMS_DATA_MINING.CREATE_MODEL function. This will use the settings in our ASSOC_SAMPLE_SETTINGS table. We will use the view created in Step 3 above and use the CASE_ID attribute we created as the Case ID in the function all.

BEGIN 
   DBMS_DATA_MINING.CREATE_MODEL( 
     model_name          => ‘ASSOC_MODEL_2’, 
     mining_function     => DBMS_DATA_MINING.ASSOCIATION, 
     data_table_name     => ‘ASSOC_DATA_V’, 
     case_id_column_name => ‘CASE_ID’, 
     target_column_name  => null, 
     settings_table_name => ‘assoc_sample_settings’);

END;

On my laptop this took approximately 5 second to run on just over 918K records involving just over 143K cases or baskets.

Now that is quick!!!

Step 5 – View the Model Outputs

There are a couple of functions that can be used to extract the rules produced in our previous step. These include:

GET_ASSOCIATION_RULES : This returns the rules from an association model.

SELECT rule_id, 
       antecedent, 
       consequent, 
       rule_support,

       rule_confidence

FROM TABLE(DBMS_DATA_MINING.GET_ASSOCIATION_RULES(‘assoc_model_2’, 10));

The 10 here returns the top 10 records or rules.image GET_FREQUENT_ITEMSETS : returns a set of rows that represent the frequent item sets from an association model. In the following code we want the top 30 item sets to be returned, but filtered to only display item sets where there are 2 or more rules.

SELECT itemset_id,

       items,

       support,

       number_of_items

FROM TABLE(DBMS_DATA_MINING.GET_FREQUENT_ITEMSETS(‘assoc_model_2’, 30))

WHERE number_of_items >= 2;

image

Association Rules in ODM-Part 3

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This is a the third part of a four part blog post on building and using Association Rules in Oracle Data Miner. The following outlines the contents of each post in the series on Association Rules

  1. This first part will focus on how to building an Association Rule model
  2. The second post will be on examining the Association Rules produced by ODM – This blog post
  3. The third post will focus on using the Association Rules on your data.
  4. The final post will look at how you can do some of the above steps using the ODM SQL and PL/SQL functions.

In my previous posts I showed how you can go about setting up for Association Rule analysis in Oracle Data Miner and how to examine the rules that are generated.

This post will focus on how we can extract and use these rules in Oracle Data Miner.

Step 1 – Model Details

Association Rules are an unsupervised method of data mining. In Oracle Data Miner we cannot use the Apply node to to score new data. What we have to do is to generate the Model Details. These in turn can then be used.

The Model Details node is used when we do unsupervised learning to extract the rules that are generated.

To do this we need to click on the Model Details node in the Models section of the Component Palette and then click on our workspace, just to the right of the Association Rule node.

The Edit Model Selection window will open. Connect the Association Rule node to the Model Details node. Then Run the node. This will then generate the Association Rules in a format what we can reuse.

image

When you get the small green tick on the Model Details node you can then view what was generated.

Right click on the Model Details node and click on View Details from the menu.

image

The output is similar to what we would have seen under the Association Rule node with the addition of a few more attributes that include the schema name and model name.

We can order the rules based on the Confidence level by double clicking on the Confidence column header. You might need to do this twice to get the rule appearing based on a descending confidence value.

At this point we can no look at persisting the Association Rules. See step 2 below.

We can also view the SQL that was used to generate the Association Rules that we see in the Model Details node. While still viewing the rules, click on the SQL tab.

image

Step 2 – Persisting the Association Rules

To make the rules persist and be useable outside of ODM we can persist the Association Rules in a table. The first step to do this is to create a new Table Node. This can be found under the Data section of the Component Palette. Click this Create Table or View node in the component palette and then click on the workspace, just to the right of the Model Details node.

Connect the Model Details node to the Output node, by right clicking on the Model Details node, select Connect from the menu and then click on the Output Node.

We can now edit the format of the Output i.e. specify what attributes are to be in our Output table. Double click on the Output node or right click and select Edit from the menu. We now get the Edit Create Table or View Node.

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We can give the output a meaningful name e.g. AR_OUTPUT_RULES. We can also specify what rule properties we can to export to attributes in out table.

We will need to un-tick the Auto Input Columns Selection tick box before we can remove any of the output attributes. In my case I only want to have ANTECENDENT_ITEMS, CONSEQUENT_ITEMS, ID, LENGTH, CONFIDENCE and SUPPORT in my out put. So I need to select and highlight all the other attributes (holding the control button). After selecting all the attributes I do not want included in the final output table, I need to click on the red X icon.

SNAGHTML18859128

When complete click on the OK button to go back to the workflow.

To generate the table right click on the AR_OUTPUT_RULES node and select Run from the menu. When you get the green tick mark on the AR_OUTPUT_RULES node the table has been created with records containing the details of each rules.

image

To view the contents of the AR_OUTPUT_RULES table we can right click on this node and select view data from the menu.

image

We can now use these rules in our applications.

 

Check out the next post in the series (Part 4) where we will look at the functionality available in the ODM SQL & PL/SQL functions to perform Association Rule analysis.

Association Rules in ODM–Part 2

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This is a the second part of a four part blog post on building and using Association Rules in Oracle Data Miner.  The following outlines the contents of each post in the series on Association Rules

  1. This first part will focus on how to building an Association Rule model
  2. The second post will be on examining the Association Rules produced by ODM – This blog post
  3. The third post will focus on using the Association Rules on your data.
  4. The final post will look at how you can do some of the above steps using the ODM SQL and PL/SQL functions. 

In the previous post I looked at the steps needed to setup a data source and to setup the Association Rule node. When everything was setup we ran the workflow.

Step 1 – Viewing the Model

We the workflow has finished running we will have the green tick marks on each node. This is where we left thing at the end of the previous post (Part 1). To view the model details, right click on the Association Role Node and select View Models from the menu.

image

There are 3 main concepts that are important in relation to Association Rules:

  • Support: is the proportion of transactions in the data set that contain the item set i.e. the number of times the rule occurs
  • Confidence: is the proportion of the occurrences of the antecedent that result in the consequent e.g. how many times do we get C when we have A and B  {A, B} => C
  • Lift: indicates the strength of a rule over the random co-occurrence of the antecedent and the consequent

Support and Confidence are the primary measures that are used to access the usefulness of an association rule.

In our example we can see that the the antecedent and the consequent has numbers separated by the word AND. These numbers correspond to the product numbers.

Step 2 – Examining the Model Rules

To read the antecedent and the consequent for the first rule in our example we have:

Antecedent: 137 AND 143 AND 128

Consequent: 144

To read this association rule we would say that if a Customer bought product 137 and product 143 and product 128, then we have a Confidence value of almost 71%. This is a strong association.

We can check the ordering of the rules by changing the Sort By criteria. As Confidence and Support are the main ways to evaluate the rules, we can change the Sort By criteria to be Confidence. Then click on the Query button to refresh the rules section.

image

Here get a list of the strongest rules listed in descending order.

Below the section of the screen that has the Rules, we have the Rule Details section.

image 

Here we can see that the rule gets formatted into an IF statement. The first rule in the list has a confidence of almost 97%. As it is a simple IF statement it can be easily implemented in our applications.

We want use the information that these rules provides in a number of ways. One such consequence of these rules is that we can look at improving the ordering and distribution of these products to ensure that we have sufficient numbers of each. Another consequence is that we can enhance the front end selling mechanism to make sure that if a customer is buying product 114, 118 and 115 then we can remind the customer of product 119. We can also ensure that all these products are not located beside each other, so that the customer will have to walk past many other products in order to find them. That is why we never see milk and bread beside each other in a grocery store.

Step 3 – Applying Filters to the Model Rules

In the previous step we were able to sort our rules based on some of the measures of our Association Rules and to see how these rules are structured.

Association Rule Analysis can generate many thousands of possible rules for a small data set. In some cases the similar rules can appear and we can have lots of rules that occur so infrequently that they are perhaps meaningless.

ODM provides us with a number of filters that we can apply to the rules that enables use to look for the rules that are of must interest to use. We can access these filters by clicking on the More button, that is located just under the Query button.

We can refine our query on the rules based on the various measures and the number if items in the rule. In addition to this we can also filter based on the values of the items. This is particularly useful if we want to concentrate on specific items (in our example Products). To illustrate this use focus on the rules that involve Product 115. Click on the green + symbol on the right hand side of the window. Select 115 from the list provided. Next we need to decide if we want Product 115 involved in the Antecedent or the Consequent. In our example select the Consequent. This is located to the bottom right of the window. Then click the OK button and then click on the Query button to update the list of rules that correspond with the new filter.

image

We can see that we only have rules that have Product 115 in the Consequent column.

We can also see that we have 134 rules for this scenarios out of a total of 20,988 (your results might differ slightly to mine and that’s OK. It really depends on what version of the sample data you are using)

 

Check out the next post in the series (Part 3) where we will look at how you can use the Association Rules produced by ODM.

Association Rules in ODM–Part 1

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This is a the first part of a four part blog post on building and using Association Rules in Oracle Data Miner. The following outlines the contents of each post in the series on Association Rules

  1. This first part will focus on how to building an Association Rule model
  2. The second post will be on examining the Association Rules produced by ODM – This blog post
  3. The third post will focus on using the Association Rules on your data.
  4. The final post will look at how you can do some of the above steps using the ODM SQL and PL/SQL functions.

image

The data set we will be using for Association Rule Analysis will be the sample data that comes with the SH schema in the database. Access to this schema and it’s data was setup when we created our data mining schema and ODM Repository.

Step 1 – Getting setup

As with all data mining projects you will need a workspace that will contain your workflows. Based on my previous ODM blog posts you will have already created a Project and some workflows. You can either reuse an existing workflow you have used for one of the other ODM modeling algorithms or you can create a new Workflow called Association Rules.

Step 2 – Define your Data Set

Assuming that your database has been setup to have the Sample schemas and their corresponding data, we will be using the data that is in the SH schema. In a previous post, I gave some instructions on setting up your database to use ODM and part of that involved a step to give your ODM schema access to the sample schema data.

We will start off by creating a Data Source Node. Click on the Data Source Node under the Component Palette. Then move your mouse to your your workspace area and click. A Data Source Node will be created and a window will open. Scroll down the list of Available Tables until you find the SH.SALES table. Click on this table and then click on the Next button. We want to include all the data so we can now click the Finish Button.

image

Our Data Source Node will now be renamed to SALES.

Step 3 – Setup the Association Build Node

Under the Model section of the Component Palette select Association. Move the mouse to your work area (and perhaps just the to right of the SALES node) click. Our Association Node will be created.

image

For the next step we need to join the our data source (SALES) with the Association Build Node. Right click on the SALES data node and select Connect from the drop down menu. Then move the mouse to the Association Build node and click. You should now have the two nodes connected.

We will now get the Edit Association Build Node property window opening for us. We will need to enter the following information:

  • Transaction ID: This is the attribute(s) that can be used to uniquely identify each transaction. In our example the Customer ID and the Time ID of the transaction allows us to identify what we want to analyse by i.e. the basket. This will group all the related transactions together
  • Item ID: What is the attribute of the thing you want to analyse. In our case we want to analyse the Products purchased, so select PROD_ID in this case
  • Value: This is an identifier used to specify another column with the transaction data to combine with the Item ID. means that you want to see if there are any type of common bundling among all values of the selected Item ID. Use this.

image

Like all data mining products, Oracle has just one Algorithm to use for Association Rule Analysis, the Apriori Algorithm.

Click the OK button. You are now ready to run the Association Build Node. Right click on the node and select Run from the menu. After a short time everything should finish and we will have the little green tick makes on each of the nodes.

image

 

Check out the next post in the series (Part 2) where we will look at how you can examine the rules produced by our model in ODM.

Events for Oracle Users in Ireland-November 2012

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November (2012) is going to be a busy month for Oracle users in Ireland. There is a mixture of Oracle User Group events, with Oracle Day and the OTN Developer Days. To round off the year we have the UKOUG Conference during the first week in December.

Here are the dates and web links for each event.

Oracle User Group

The BI & EPM SIG will be having their next meeting on the Tuesday 20th November. This is almost a full day event, with presentations from End Users, Partners and Oracle product management. The main focus of the day will be on EPM, but will also be of interest to BI people.

As with all SIG meetings, this SIG will be held in the Oracle office in East Point (Block H). Things kick off at 9am and are due to finish around 4pm with plenty of tea/coffee and a free lunch too.

image

Remember to follow OUG Ireland on twitter using  #oug_ire

Oracle Day

Oracle will be having their Oracle Day 2012, on Thursday 15th, in Croke Park. Here is some of the blurb about the event,  “…to learn how Oracle simplifies IT, whether it’s by engineering hardware and software to work together or making new technologies work for the modern enterprise. Sessions and keynotes feature an elite roster of Oracle solutions experts, partners and business associates, as well as fascinating user case studies and live demos.

This is a full day event from 9am to 5pm with 3 parallel streams focusing on Big Data, Enterprise Applications and the Cloud.

Click here to register for this event.

Click here for the full details and agenda.

OTN Developer Days

Oracle run their developer days about 3 times a year in Dublin. These events are run like a Hands-on Lab. So most of the work during the day is by yourself. You are provided with a workbook, a laptop and a virtual machine configured for the hands-on lab. This November we have the following developers days in the Oracle office in East Point, Dublin.

Tuesday 27th November (9:45-15:00) : Real Application Testing

Wednesday 28th November (9:00-14:00) : Partitioning/Advanced Compression

Thursday 29th November (9:15-13:30) : Database Security

Friday 30th November (9:45-16:00) : Business Process Management Using BPM Suite 11g

As you can see we have almost a full week of FREE training from Oracle. So there is no reason not to sign up for these days.

UKOUG Conference – in Birmingham

In December we have the annual UKOUG Conference. This is the largest Oracle User Group conference in Europe and the largest outside of the USA. At this conference you will have some of the main speakers and presentations from Oracle Open World, along with a range of speakers from all over the work.

In keeping with previous years there will be the OakTable Sunday and new this year there will be a Middleware Sunday. You need to register separately for these events. Here are the links

OakTable Sunday

Middleware Sunday

The main conference kicks off on the Monday morning with a very full agenda for Monday, Tuesday and Wednesday. There are a number of social events on the Monday and Tuesday, so come well rested.

On the Monday evening there is the focus pubs. This year it seems to have an Irish Pub theme. At the focus pub event there will be table for each of the user group SIGs. 

Come and join me at the Ireland table on the Monday evening.

The full agenda in now live and you can get all the details here.

I will be giving a presentation on the Tuesday afternoon titled Getting Real Business Value from Predictive Analytics (OBIEE and Oracle Data Mining). This is a joint presentation with Antony Heljula of Peak Indicators.

Oracle Advanced Analytics Option in Oracle 12c

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At Oracle Open World a few weeks ago there was a large number of presentations on Big Data and Analytics.  Most of these were marketing type presentations, with a couple of presentations on using R and how it can not be integrated into the Oracle Database 11.2.

In addition this these there was one presentation that focused on the Oracle Advanced Analytics (OAA) Option.

The Oracle Advanced Analytics Option covers the Oracle Data Mining features and the Oracle R Enterprise features in the Database.

The purpose of this blog post is to outline and summarise what was mentioned at these presentations, and will include what changes are/may be coming in the “Next Release” of the database i.e. Oracle 12c.

Health Warning: As with all the presentations at OOW that talked about what may be in or may be in the next release, there is not guarantee that the features will actually be in the release version of the database. Here is the slide that gives the Safe Harbor statement.

image

  • 12c will come with R embedded into it. So there will be no need for any configurations.
  • Oracle R client will come as part of the server install.
  • Oracle R client will be able to use the Analytics functions that exist in the database.
  • Will be able to run R code in the database.
  • The database (12c) will be able to spawn multiple R engines.
  • Will be able to emulate map-reduce style algorithms.
  • There will be new PREDICTION function, replacing the existing (11g) functionality. This will combine a number of steps of building a model and applying it to the data to be scored into one function.  But we will still need the functionality of the existing PREDICTION function that is in 11g. So it will be interesting to see how this functionality will be kept in addition to the new functionality being proposed in 12c.
  • Although the Oracle Data Miner tool will still exits and will have many new features. It was also referred to as the ‘OAA Workflow’.  So those this indicate a potential name change?  We will have to wait and see.
  • Oracle Data Miner will come with a new additional graphing feature. This will be in addition to the Explore Node and will allow us to produce more typical attribute related graphs. From what I could see these would be similar to the type of box plot, scatter, bar chart, etc. graphs that you can get from R.
  • There will be a number of new algorithms too, including a useful One Class Support Vector Machine. This can be used when we have a data set with just one class value. This algorithm will work out what records/cases are more important and others.
  • There will be a new SQL node. This will allow us to write our own data transformation code.
  • There will be a new node to allow the calling of R code.
  • The tool also comes with a slightly modified layout and colour scheme.

Again, the points that I have given above are just my observations. They may or may not appear in 12c, or maybe I misunderstood what was being said.

It certainly looks like we will have a integrate analytics environment in 12c with full integration of R and the ODM in-database features.

Extracting the rules from an ODM Decision Tree model

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One of the most interesting of important aspects of a Decision Model is that we as a user can get to see what rules the machine learning algorithm has generated for our data.

I’ve give a number of examples in various blog posts over the past few years on how to generate a number of classification models. An example of the workflow is below.

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In the Class Build node we get four models being generated. These include a Generalised Linear Model, Support Vector Machine, Naive Bayes and a Decision Tree model.

We can explore the Decision Tree model by right clicking on the Class Build Node, selecting View Models and then the Decision Tree model, which will be labelled with a ‘DT’ in the name.

image

As we explore the nodes and branches of the Decision Tree we can see the rule that was generated for a node in the lower pane of the applications. So by clicking on each node we get a different rule appearing in this pane

image

Sometimes there is a need to extract this rules so that they can be presented to a number of different types of users, to explain to them what is going on.

How can we extract the Decision Tree rules?

To do this, you will need to complete the following steps:

  • From the Models section of the Component Palette select the Model Details node.
  • Click on the Workflow pane and the Model Details node will be created
  • Connect the Class Build node to the Model Details node. To do this right click on the Class Build node and select Connect. Then move the mouse to the Model Details node and click. The two nodes should now be connected.
  • Edit the Model Details node, uncheck the Auto Settings, select Model Type to be Decision Tree, Output to be Full Tree and all the columns.

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  • Run the Model Details node. Right click on the node and select run. When complete you you will have the little green box with a tick mark, on the top right hand corner.
  • To view the details produced, right click on the Model Details node and select View Data
  • The rules for each node will now be displayed. You will need to scroll to the right of this pane to get to the rules and you will need to expand the columns for the rules to see the full details

image

My Presentations on Oracle Advanced Analytics Option

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I’ve recently compiled my list of presentation on the Oracle Analytics Option. All these presentations are for a 45 minute period.

I have two versions of the presentation ‘How to do Data Mining in SQL & PL/SQL’, one is for 45 minutes and the second version is for 2 hour.

I have given most of these presentations at conferences or SIGS.

Let me know if you are interesting in having one of these presentations at your SIG or conference.

  • Oracle Analytics Option – 12c New Features – available 2013
  • Real-time prediction in SQL & Oracle Analytics Option – Using the 12c PREDICTION function – available 2013
  • How to do Data Mining in SQL & PL/SQL
  • From BIG Data to Small Data and Everything in Between
  • Oracle R Enterprise : How to get started
  • Oracle Analytics Option : R vs Oracle Data Mining
  • Building Predictive Analysts into your Forms Applications
  • Getting Real Business Value from OBIEE and Oracle Data Mining  (This is a cut down and merged version of the follow two presentations)
  • Getting Real Business Value from OBIEE and Oracle Data Mining – Part 1 : The Oracle Data Miner part
  • Getting Real Business Value from OBIEE and Oracle Data Mining – Part 2 : The OBIEE part
  • How to Deploying and Using your Oracle Data Miner Models in Production
  • Oracle Analytics Option 101
  • From SQL Programmer to Data Scientist: evolving roles of an Oracle programmer
  • Using an Oracle Oracle Data Mining Model in SQL & PL/SQL
  • Getting Started with Oracle Data Mining
  • You don’t need a PhD to do Data Mining

Check out the ‘My Presentations’ page for updates on new presentations.