I have a few Updates since I posted the blog on 14th Sept
1. The OBIEE VMS are now available on the Virtural Box Pre-Built VM webpage, as of 15th Sept. Thanks to @oraclenerd for requesting this
2. Thanks to @rnm1978 for pointing out that the OBIEE VM is very resource intensive. The min. spec. given is not really enough. You really need more than 4GB for this VM to run.
3. The SampleApp that the OBIEE VM contains also has some examples of how Oracle Data Miner can be used in an application
I’ve recently come across an VM of Oracle Business Intelligence Enterprise Edition, which has the sample application already build and ready for you to use.
The VM files are available at the following link for download.
There are 2 files that you need to download to create the VM in VirtualBox. These are the VB Image key File (.ovf) and the VB Image Disk Files (.vmdk). The second file is ~25G, so it a while to download!
There is also a VB Image-Deployment Guide.
If you already have OBIEE installed and you don’t want to setup the VM, the setup files and data files are also available.
The Oracle BIWA SIG, which is part of the IOUG, will be having a tech cast on Wednesday 14th September 12:00 PM – 1:00 PM CDT (between 6pm and 7pm in Ireland)
It is titled ‘Building Next-Generation Predictive Analytics Applications using Oracle Data Mining’.
You can register for this by visiting http://ow.ly/6s35C
This presentation will cover how the Oracle Database has become a predictive analytics (PA) platform for next-generation applications and will include several examples including:
- Oracle Fusion Human Capital Management (HCM) Predictive Workforce,
- Oracle Adaptive Access Manager for fraud detection,
Oracle Communications Industry Model,
- Oracle Complex Event Processing and others and will be interspersed with
- Oracle Data Mining demos and PA examples where possible.
“Predictive analytics help you make better decisions by uncovering patterns and relationships hidden in the data. This new information generates competitive advantage. Oracle has invested heavily to “move the algorithms to the data” rather than current approaches. Oracle Data Mining provides 12 in-database algorithms that mine star schemas, structured, unstructured, transactional, and spatial data. Exadata, delivering 10x-100x faster performance, combined with OBIEE for dashboards and drill-down deliver an unbeatable in-database analytical platform that undergirds next-generation “predictive” analytics applications. This webcast will show you how to get started.”
Over the past couple of days I have been doing a bit of a reorganisation of my book case in my home office. On one of the shelves I keep my Oracle Magazine. My collection dates back to 1992. I began my working career as a graduate consultant with Oracle in Ireland. At that stage Oracle Magazine seemed to be published every 4 to 6 months, but around 1995 it moved to being published every 2 months.
I though that I had a full collection of Oracle Magazine from 1993 onwards, but the table below shows that I have a number of missing editions. Perhaps these gaps are due to my good nature of lending them to other people, or maybe I just lost them somewhere.
What I’m looking to do is to complete my collection. If you have one of the missing editions, can you let me know. Assuming that you don’t mind parting with it, we can arrange postal.
Looking back over the previous editions, it is interesting to see some of the topics that were discussed. Typically they were a couple years before they became commonly used.
An idea for Oracle Magazine is to have a new column that looks back at an article on a particular technique/technology/tool and reflects on how things have changed (or not) since the article was written.
The Winter edition of the UKOUG Oracle Scene magazine is now looking for articles to be submitted for consideration.
The due date for the article submission is Friday 26 August. So you have just over 2 weeks to put together your article.
Lots of people have asked me what kind of articles are you looking for. The simple answer is anything as long as it is Oracle related. The following list should give you some ideas:
- Technical article
- Application article
- Business articles
- Tool article
- SIG meetings, new, updates and plans
- New features
- Something that you discovered
- Your likes, dislikes or anything relating to Oracle
- Oracle Book reviews
- Oracle Conference reviews
So you can see anything goes really.
How long should an article be ? It can be any length really. Anything from 1/4 page to a full 5 page article, and anything in between.
Selection of Articles Process
All submitted articles are assessed by a review panel, comprised of volunteers from a variety of businesses and specialties. The review panel rates the articles and makes comments where appropriate.
An editorial meeting takes place after the submissions have been rated. The articles are assessed and the review panel’s scoring and comments are taken into account. The editorial team makes the final decision as to which articles will be selected for publication, or to be held over for a future edition. You will be notified of the result as soon as this process has been completed.
You will be contacted near the publication date by the publishing company for you to review to print version of your article.
Submitting your Article
Check out the Article Formatting Guidelines before submitting.
All pictures and images should be 300dpi.
Include a 100(max) word Bio and your photo
Email your article and images to
Oracle Data Miner functionality is now well established and proven over the years. In particular with the release of the ODM 11gR2 version of the tool. But how will Oracle Data Miner develop into the future.
There are 4 main paths or Frontiers for future developments for Oracle Data Miner:
Oracle Data Miner Tool
The new ODM 11gR2 tool is a major development over the previous version of the tool. With the introduction of workflows and some added functionality for some of the features. the tool is now comparable with the likes of SAS Enterprise Miner and SPSS.
But the new tool is not complete and still needs a bit of fine tuning of most of the features. In particular with the usability and interactions. Some of the colour schemes needs to be looked at or to allow users to select their own colours.
Apart from the usability improvements for the tool another major development that is needed, is the ability to translate the workflow and the underlying database objects into usable code. This code can then be incorporated into our applications and other tools. The tool does allow you to produce shell code of the nodes, but there is still a lot of effort needed to make this usable. Under the previous version of the tool there was features available in JDeveloper and SQL Developer to produced packaged code that was easy to include in our applications.
“A lot done – More to do”
Over the past couple of months there has been a few postings on how Oracle Data Miner (11gR2) has been, or will be, incorporated in various Oracle Applications. For example Oracle Fusion Human Capital Management and Oracle Real Time Decision (RTD). Watch out of other applications that will be including Oracle Data Miner.
“A bit done – Lots more to do”
Oracle Business Intelligence
One of the most common places where ODM can be used is with OBIEE. OBIEE is the core engine for the delivery of the BI needs for an organisation. OBIEE coordinates the gathering of data from various sources, the defining of the business measures and then the delivery of this information in various forms to the users. Oracle Data Miner can be included in this process and can add significant value to the BI needs and report.
“A lot done – Need to publicise more”
Most data mining projects are independent of various Applications and BI requirements. They are projects that are hoping to achieve a competitive insight into their organisational data. Over time as the success of some pilot projects become know they need for more data mining projects will increase. This will lead to organisations have a core data mining team to support these project. With this, the team will need tools to support them in the delivery of their project and with the delivery. This is were OBIEE and Oracle Fusion Apps will come increasingly important.
“A lot done – more to do”
Before beginning any data mining task we need to performs some data investigation. This will allow us to explore the data and to gain a better understanding of the data values. We can discover a lot by doing this can it can help us to identify areas for improvement in the source applications, as well as identifying data that does not contribute to our business problem (this is called feature reduction), and it can allow us to identify data that needs reformatting into a number of additional features (feature creation). A simple example of this is a date of birth field provides no real value, but by creating a number of additional attributes (features) we can now use the date of birth field to determine what age group they fit into.
As with most of the interface in Oracle Data Miner 11gR2, there is a new Data Exploration interface. In this blog post I will talk you through how to set-up and use the new Data Exploration interface and show you how you can use the data exploration features to gain an understanding of the data before you begin using the data mining algorithms.
The examples given here are based on my previous blog posts and we will use the same sample data sets, that were set-up as part of the install and configuration.
See my other blog post and videos on installing and setting up Oracle Data Miner.
The next step is to create the Explore Data node on our workflow. From the Data tab in the Component Palette, select and drag the Explore Data node onto the workflow. Now we need to link the Data node to the Explore Data node.
Right-click on the Explore Data mode and click Run. This will make the ODM tool go to the database and analyse the data that is specified in our Data node. The analyse results will be used in the Explore Data note.
Exploring the Data
When the Explore Data node has finished we can look at the data it has generated. Right-click the Explore Data node and select View Data.
A lot of statistical information has been generated for each of the attributes in our Data node. In addition to the statistical information we also get a histogram of the attribute distributions.
We can work through each attribute taking the statistical data and the histograms to build up a picture of the data.
The data we are using is for an Electronics Goods store.
A few interesting things in the data are:
- 90% of the data comes from the United States of America
- PRINTER_SUPPLIES attribute only has one value. We can eliminate this from our data set as it will not contribute to the data mining algorithms
- Similarly for OS_DOC_SET_KENJI, which also has one one value
The histograms are based on predetermined number of bins. This is initially set to 10, but you may need to changed this value up or down to see if a pattern exists in the data.
An example of this is if we select AGE and set the number of bins to 10. We get a nice histogram showing that most of our customers are in the 31 to 46 age ranges. So maybe we should be concentrating on these.
Now if we change the number of bins to 30 can get a completely different picture of what is going on in the data.
To change the number of bin we need to go to the Workflow pane and select the Property Inspector. Scroll down to the Histogram section and change the Numerical Bins to 25. You then need to rerun the Explore Data node.
Now we can see that there are a number of important age groups what stand out more than others. If we look at the 31 to 46 age range, in the first histogram we can see that there is not much change between each of the age bins. But when we look at the second histogram for the 25 bins for the same 21 to 34 age range we get a very different view of the data. In this second histogram we see that that the ages of the customers vary a lot. What does mean. Well it can mean lots of different things and it all depends on the business scenario. In our example we are looking at an electronic goods store. What we can deduce from this second histogram is that there are a small number of customers up to about age 23. Then there is an increase. Is this due to people having obtained their main job after school having some disposable income. This peak is followed by a drop off in customers followed by another peak, drop off, peak, drop off etc. Maybe we can build a profile of our customer based on their age just like what our financial organisations due to determine what products to sell to use based on our age and life stage.
Conclusions on the data
From this histogram we can maybe categorise the customers into the follow
• Early 20s – out of education, fist job, disposable income
• Late 20s to early 30s – settling down, own home
• Late 30s – maybe kids, so have less disposable income
• 40s – maybe people are trading up and need new equipment. Or maybe the kids have now turned into teenagers and are encouraging their parents to buy up todate equipment.
• Late 50s – These could be empty nesters where their children have left home, maybe setting up home by themselves and their parents are building things for their home. Or maybe the parents are treating themselves with new equipment as they have more disposable income
• 60s + – parents and grand-parents buying equipment for their children and grand-children. Or maybe we have very techie people who have just retired
• 70+ – we have a drop off here.
As you can see we can discover a lot in the day by changing the number of bins and examining the data. The important part of this examination is trying to relate what you are seeing from the graphical representation of the data on the screen, back to the type of business we are examining. A lot can be discovered but you will have to spend some time looking for it.
ODM 11gR2 Extra Data Exploration Functionality
In ODM 11gR2 we now have an extra feature for our data analysis feature. We can now produce the histograms that are grouped by one of the other attributes. Typically this would be the Target or Class attribute but you can also use it with the other attributes.
To set this extra feature, double click on the Explore Data node. The Group By drop down lets you to select the attribute you want to group the other attributes by.
Using our example data, the target variable is AFFINITY_CARD. Select this in the drop down and run the Explore Data node again. When you look at the newly generated histograms you will now see each bin has two colours. If you hover the mouse of each coloured part you will be able to get the number of records in each group. You can use other attributes, such as the CUST_GENDER, COUNTRY_NAME, etc. Only use the attributes where it would make sense to analyse the data by.
This is a powerful new feature that allows you to gain a deeper level of insight into the data you are analysing
Today I gave my VirtaThon presentation on the new Oracle Data Miner 11gR2 tool.
It was an interesting experience as VirtaThon was a virtual conference. The organisation and administration of the conference was excellent.
I had over 25 participants for my presentation, including Carolyn Hamm who has written a book on using Oracle Data Miner 10g. She seemed to enjoy my presentation as she was asking for more at the end, but we had run out of time.
The presentation was an unusual but interesting experience. All the participants were muted, so I could not hear anyone or be asked questions as the presentation progressed. I was not able to judge the body language or facial expressions, for me to work out how the presentation was going.
I was sitting in my living room when giving the presentation and spent almost an hour talking to myself. At time the concentration levels dipped and I have to refocus and used some visualisation to help me concentrate.
The presentation was divided into 2 parts. The first part was a presentation consisting of some background to ODM, how to get setup and running with ODM, and finally a discussion of some of the new features. This first part took approx. 30 minutes which surprised me as during my rehearsals it was talking 16 minutes. The second part of the presentation was a demo of using ODM to create a workflow, generating a classification model and then applying this model to some new data. During my rehearsals this was taking approx. 40 minutes.
I only had 50-55 minutes for my VirtaThon presentation so after my presentation I had 20-25 minutes for the demo. So I had to get through the demo quickly and I had to cut out a discussion of how the data exploration functionality in ODM can be used to get an insight into the data before you start using the data mining features. I will put together a blog post and video of this in a couple of weeks time that will explain it in more detail.
I managed to finish at 49 minutes, which left 6 minutes for questions. There was only a couple of questions, plenty of Thank You’s along with Good Presentation, which is always good to hear.
Thank you to everyone who attended the presentation and to the organisers of VirtaThon.