Irish Oracle BI SIG Meeting – 23rd June
The next Irish Oracle BI SIG meeting will be on Thursday 23rd June starting at 6:30pm.
The format of this SIG meeting is a bit different from the previous ones.
This time the SIG meeting will be an informal networking event and there will be no demos or presentations.
The SIG event will be in the River View Bistro Bar, which is on the the MV Cillairne boat, that is moored beside the new convenion center on the quays. Check out its website
Creating ODM Schemas & Repository for ODM 11g R2
Before you can start using the Oracle Data Miner features that are now available in SQL Developer 3, there are a few steps you need to perform. This post will walk you through these steps and I have put together a video which goes into more detail. The video is available on my YouTube channel.
I will be posting more How To type videos over the coming weeks and months. Each video will focus in one one particular feature within the new Oracle Data Mining tool.
So following steps are necessary before you can start using the ODM tool
Set up of Oracle Data Miner tabs
To get the ODM tabs to display in SQL Developer, you need to go to the View menu and select the following from the Data Miner submenu
- Data Miner Connections
- Workflow Jobs
- Property Inspector
Create an ODM Schema
There are two main ways to create a Schema. The first and simplest way is to use SQL Developer. To do this you need to create a connection to SYS. Right click on the Other Users option and select Create User.
The second option is to use SQL*Plus to create the user. Using both methods you need to grant Connect & Resource privileges to the user.
Create the Repository
Before you can start using Oracle Data Mining, you need to create an Oracle Data Miner Repository in the database. Again there are two ways to do this. The simplest is to use the inbuilt functionality in SQL Developer. In the Oracle Data Miner Connections tab, double click on the ODM schema you have just created. SQL Developer will check the database to see if the ODM Repository exists in the database. If it will create the repository for you. But you will need to provide the SYS password.
The other way to create the repository is to run the installodmr.sql script that in available in the ‘datamining’ directory.
example: @installodmr.sql USER TEMP
Create another ODM Schema
It is typical that you would need to have more than one schema for your data mining work. After creating the default Oracle schema, the next step is to grant the schema the privileges to use the Data Mining Repository. This script is called
example: @usergrants.sql DMUSER
Hint: The schema name needs to be in upper case.
IMPORTANT: The last grant statement in the script may give an error. If this occurs then it is due to an invalid hidden character on the line. If you do a cut and paste of the grant statement and execute this statement, everything should run fine.
If you want to demo data to be created for this new ODM schema then you need to run
example: @instdemodata.sql DMUSER
All of these scripts can be found in SQL developer directories
Great set of Data Design Articles
For anyone starting out on data and database design there are lots and lots of books and articles to help get them started.
But for those people who have been doing database design for a while, it is always good to reflect on your approaches and techniques.
I recently attended a presentation by Steve Hoberman. If you ever get a chance to attend a data design presentation by him, I would highly recommend it.
He addition to his presentations and database design courses, he also writes for the website Information Management.
His series of articles can be found at
and his company website is
Guest Post: Can Database Developers do Data Mining ?
I was recently invited by Sandro Saitta, who runs the Data Mining Research blog (http://www.dataminingblog.com/), to write a guest blog post for him. The topic for this guest post was Can Database Developers do Data Mining ?
The original post is available at Guest Post- Can Database Developers do Data Mining –
Here is the main body of the post
Over the past 20 to 30 years Data Mining has been dominated by people with a background in Statistics. This is primarily due to the type of techniques employed in the various data mining tools. The purpose of this post is to highlight the possibility that database developers might be a more suitable type of person to have on a data mining project than someone with a statistics type background.
Lets take a look at the CRISP-DM lifecycle for data mining (Figure 1). Most people involved in data mining will be familiar with this life cycle.
Figure 1 – CRoss Industry Standard Process for Data Mining.
It is will documented that the first three steps in CRISP-DM can take up to 70% to 80% of the total project time. Why does it take so much time. Well the data miner has to start learning about the business in question, explore the data that exists, re-explore the business rules and understand etc. Then can they start the data preparation step.
Database developers within the organisation will have gathered a considerable amount of the required information because they would have been involved in developing the business applications. So a large saving in time can be achieved here as this will already have most of the business and data understanding. They are well equipped at querying the data, getting to the required data quicker. The database developers are also best equipped to perform the data preparation step.
If we skip onto the deployment step. Again the database developers will be required to implement/deploy the selected data mining model in the production environment.
The two remaining steps, Modelling and Evaluation, are perhaps the two steps that database developers are less suited too. But with a bit of training on Data Mining techniques and how to evaluate data mining models, they would be well able to complete the full data mining lifecycle.
If we take the stages of CRISP-DM that a database developer is best suited to, Business Understanding, Data Understanding, Data Preparation and Deployment, this would equate to approximately 80% to 85% of the total project. With a little bit of training and up skilling, database developers are the based kind of person to perform data mining within their organisation.
Oracle Data Miner Comes of Age
I’ve recently had an article titled Oracle Data Miner Comes of Age accepted for the June edition of the UKOUG Oracle Scene article.
I’ve been thinking of ways to try to promote this article and I’ve decided I would create two videos and post them on YouTube.
The first video is a short 1 minute introduction to the article. A taster kind of video. I’ve learned from my initial attempts at producing the video that
- It is more difficult than it looks
- The camera on my laptop is not install straight. That is why I’m looking to one side
- I need a better quality microphone
But perhaps the most interesting thing was that within a couple of hours of posting it up on YouTube (and not telling anyone about it), it was found and tweeted by Charlie Burger. Charlie is the Senior Director in charge of the Oracle Data Miner tool. He also very kindly tweeted about one of my blog postings on the New Features of Oracle Data Miner 11g R2.
You can find the introduction video to the article at
I will be posting an much long view, which will be based on the full article over the next couple of weeks
Job: ETL/Data Warehouse Consultant
Distinct Partners are a new opening for a ETL/Data Warehouse Consultant.
Following a period of growth, we are now looking for experienced ETL professionals to join our consultancy team. If you are looking for a challenge in management consultancy and believe that you have the qualities to succeed in a dynamic and high growth consultancy environment, then we would love to hear from you.
- Data Integration skills (at least one of the following)
- Proprietary: Informatica, SAS Data Integration Studio, IBM Data Stage, Oracle.
- Open Source: Talend, Postgres, My SQL, CUDA, Python.
- Strong querying , data analysis and data flow mapping skills (must)
- Data quality skills – checks, standardisation, house holding etc (understanding)
- Data architecture skills (understanding)
- data modelling (normalisation, referential integrity etc)
- dimension modelling (dimensions, facts, SCDs etc
- XML scripting and open source data integration skills (strong plus)
- Database/ETL performance tuning and programming skills
Full details can be found at
If you would like to apply for the job you can email your CV to
Gina Cassidy email@example.com
and mention you heard about the job from me
My Blog & others
Over the past few years I have been contributing on Data Mining and Oracle Data Miner topics on the BI-Quotient blog
Over the past few months I have decided to expand my blog postings to include all the things I’m currently doing or things that I find interesting. The main theme will be ‘Data is King’
The new blog will include posts on the following topics:
- Oracle Data Miner
- Data Mining
- Data Management
- My research
- Database Design
- and generally anything else that I find interesting and relating to Data.
This is where this blog come into its own. This will be my main blog going forward. It will contain all my posts, including a copy of these that I post on the BI-Quotient blog
Deputy Editor for Oracle Scene (June edition)
Today I got a phone call from Jennifer from the UKOUG office asking me would I be interested in helping out with some (minor) editing of 4 articles for the June edition of Oracle Scene.
I will also have an article in this edition of Oracle Scene (a 5 page spread).
I’ve had a quick look through the 4 articles and they are an interesting bunch of articles.
Oracle Scene will be holding elections over the coming months for a more longer term deputy editor. This will go out to the user community for a public vote. I might put might name forward for this.
VirtaThon–Online Confernce July
Yesterday I received an email telling me that my presentation submission for VirtaThon (Virtual Conference for the Oracle, Java & MySQL Communities).
The presentation is titled, Getting Started with Oracle Data Miner 11G R2.
I would really like to give an online demo of the tools or even to be able to show a view of the demo, but it looks like I may have to do it with good old Powerpoint.
Recent ODM activity
Over the past couple of weeks I’ve been a little bit busy with some Oracle Data Miner 11gR2 related activities. These include
- Writing an article called Oracle Data Miner Comes of Age for submission to Oracle Scene, the UKOUG quarterly magazine. I was told on 20th April that my article was accepted and will be in the June edition
- The call for presentations opened for the annual UKOUG conference in Birmingham in December. I submitted a presentation which will be based on the article in Oracle Scene.
- I submitted 2 presentations to Oracle Open World in October. But funding might be a problem here. I’ve asked the ODM development group to see if they could sponsor some of the costs. One presentation is on Oracle Data Miner. The second is on
- I also submitted a presentation to an online (virtual) Oracle conference called VirtaThon, again on Oracle Data Miner.
Some other things that I have planned are
- Create two videos for the Oracle Scene article. The first video is a short intro to the article. The plan is to have this on the UKOUG website to promote the article. The second video will be based on the article, covering the material and the demo in the article
- Create a video on creating an ODM repository and getting started with ODM
- Create a video on removing the ODM repository
- Create a video on saving/exporting a DM model from ODM
- Write an article on what Oracle products can be used throughout the Data Mining LifeCycle (CRISP-DM). Hopefully I will submit this for the autumn edition of Oracle Scene.
- Get all the documentation available on the data manipulation stage in the new ODM tool and write an article based on this, produce a video of it, etc
All of this to be finished by the middle of June.
So I have a busy few weeks ahead of me.
Oracle Data Miner 11g R2 – New Features
There are many new features in the new tool and these can be grouped under the following headings:
- Data Exploration
: The first step of every data mining project involves investigating the data to try to learn from the data, gather some initial information and investigate if there are any patterns in the data
- Workflow interface
: This gives the user a more intuitive way to work with the tool and with the overall process of data mining. It allows for the repeated rerunning of the data modelling process without having to input and define each step again. You had to do this in the previous version of the tool
- Generate multiple models at the same time
: This is one of the major improvements in the tool. It allows you to create models using each of the algorithms available for each data mining techniques, in one step, instead of repeatedly defining each in the pervious version of the tool.
- Graphical representations of models
: Another major new feature. The tool now produces Decision Trees and Clusters graphically. With the Decisions Trees we can now see on the screen how the tree looks and then to investigate the different branches of it to see how the tree was built. We can also see what rules were generated to create these branches.
Evaluation of all the developed models
: Another major new feature. In the previous version of the tool you were presented with a set of evaluation diagrams and measures for each model. You were not able to see all the results on one graph and you had to resort to having multiple windows open at the same time to try to compare the results. Now we can get the evaluation measures and graphs for all the models on the one set of graphs. This allows a data miner to concentrate on determining the most appropriate model to use.
Each of these new features really deserve a post by themselves to illustrate their new capabilities. These posts will follow over the coming weeks.
2010 Rexer Analytics Data Mining Survey
The Rexer Analytics 4th Annual Rexer Analytics Data Miner Survey for 2010 is now available. 735 data miners participated in the 2010 survey. The main highlights of the survey are:
• FIELDS & GOALS: Data miners work in a diverse set of fields. CRM / Marketing has been the #1 field in each of the past four years. Fittingly, “improving the understanding of customers”, “retaining customers” and other CRM goals are also the goals identified by the most data miners surveyed.
• ALGORITHMS: Decision trees, regression, and cluster analysis continue to form a triad of core algorithms for most data miners. However, a wide variety of algorithms are being used. This year, for the first time, the survey asked about Ensemble Models, and 22% of data miners report using them.
A third of data miners currently use text mining and another third plan to in the future.
• MODELS: About one-third of data miners typically build final models with 10 or fewer variables, while about 28% generally construct models with more than 45 variables.
• TOOLS: After a steady rise across the past few years, the open source data mining software R overtook other tools to become the tool used by more data miners (43%) than any other. STATISTICA, which has also been climbing in the rankings, is selected as the primary data mining tool by the most data miners (18%). Data miners report using an average of 4.6 software tools overall. STATISTICA, IBM SPSS Modeler, and R received the strongest satisfaction ratings in both 2010 and 2009.
• TECHNOLOGY: Data Mining most often occurs on a desktop or laptop computer, and frequently the data is stored locally. Model scoring typically happens using the same software used to develop models. STATISTICA users are more likely than other tool users to deploy models using PMML.
• CHALLENGES: As in previous years, dirty data, explaining data mining to others, and difficult access to data are the top challenges data miners face. This year data miners also shared best practices for overcoming these challenges.
• FUTURE: Data miners are optimistic about continued growth in the number of projects they will be conducting, and growth in data mining adoption is the number one “future trend” identified. There is room to improve: only 13% of data miners rate their company’s analytic capabilities as “excellent” and only 8% rate their data quality as “very strong”.
You can request a copy of the full report by going to their data mining survey webpage
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