Sunday, February 25, 2007

New Book : Java Data Mining: Strategy, Standard, and Practice: A Practical Guide for architecture, design, and implementation

Java Data Mining: Strategy, Standard, and Practice: A Practical Guide for architecture, design, and implementation
By Mark F. Hornick, Erik Marcadé, Sunil Venkayala

Seems as if books on BI are just like buses. You wait ages for one and then two turn up together. While your on Amazon ordering your copy the Oracle Data Warehousing (see the posting last week) you could also order Mark Hornick’s latest book on Java Data Mining. As you can see from the heading Mark has worked with other key data mining experts to provide a great introduction to not only java data mining but data mining in general.

Both Mark and Sunil work for Oracle in the Server Technologies group and Erik is the founder and current CTO of KXEN. All the authors are well known within the data mining community.

Unfortunately it is not possible to use the usual Amazon feature of looking inside the book online. However, a review on the site indicates this book has excellent content (interestingly, the and sites have different reviews of this book. Personally, I find the site more useful) . A quick extract from the site:

“Whether you are a software developer, systems architect, data analyst, or business analyst, if you want to take advantage of data mining in the development of advanced analytic applications, Java Data Mining, JDM, the new standard now implemented in core DBMSs and data mining/analysis software, is a key solution component. And this book is the essential guide to the usage of the JDM standard interface. The reference that will help you produce applications with advanced analytics and predictive analytic capabilities. The first and authoritative guide to JDM, written by contributors to the JDM standard”

I have posted a few entries on using data mining in conjunction with multi-dimensional models so I am keen to get started with this book as quickly as possible. As a result, I added this to my list of books that were ordered over weekend from Amazon, along with the Oracle Data Warehousing book. Now I am just waiting for a huge parcel to arrive. I am sure both these books are going to contain a lot of useful information that will find its way on to these pages in one form or another.

You can view the book online using the following link to by clicking here.

And don't forget the Oracle Data Mining blog by Marcos Campos
, which can be viewed here

Thursday, February 22, 2007

New Book : Oracle Data Warehousing and Business Intelligence Solutions

Oracle Data Warehousing and Business Intelligence Solutions
by Robert Stackowiak, Joe Rayman, Rick Greenwald

Amazon have just made available an interesting new book on Oracle data warehousing and business intelligence. Three very senior people within the BI community have written it:
  • Robert Stackowiak, Vice President, Business Intelligence, Oracle Corporation
  • Joe Rayman, Consulting VP - BI/Analytics, Oracle Corporation
  • Rick Greenwald, author of numerous books on Oracle technology

Information about the book is limited at the moment. There is a brief synopsis on the Amazon UK website, however, the content looks very promising. Based on the synopsis it would appear the book provides content that is designed to demonstrate how to design and build a data warehouse and then use Oracle's business intelligence tools to analyze data. Information is provided on best practices for using business intelligence tools to mine a data warehouse. It covers all the usual Oracle BI and data warehouse tools:
  • Warehouse Builder
  • JDeveloper & BI Beans
  • BI/XML Publisher
  • Reports
  • BI EE
  • Discoverer
  • OLAP Spreadsheet Addin for MS Excel
  • Portal
  • Data Miner
  • Enterprise Manager
Overall there is a lot of information in this book covering all aspects of data warehousing. Some sample code is provided, via a link to a related website, to help you follow the examples included in the book.

Fortunately it is possible to use the usual Amazon feature of looking inside the book online, but very strangely this feature only available on the US based and is not available via the Amazon UK website. However, the US website does not show a synopsis of the book, which is available on the UK site.

I have already ordered my copy of the book and will post a formal review in a while. It will be interesting to see how this compares to other books on the subject. I suppose the key question is: Can you ever have enough books on data warehousing? Personally, I don't think so but as I have over 1000 cookery books I may not be the best person to ask!

You can view the book online using the following link:

Wednesday, February 21, 2007

SQL Access to OLAP Cubes

SQL Access to Cubes

Many people have asked: is possible to use normal SQL to access a multi-dimensional cube and associated dimensions? The answer is yes. What this means is that customers using BI EE, BI Publisher, SQL Developer and any other SQL reporting tool can access a 10g R2 OLAP multi-dimensional cube. However, a small warning -
If you plan to use SQL access with an end-user query tool, that tool needs to be able to support embedded total objects.

The SQL access to an OLAP cube returns all the required data points already precomputed so there is no need to issue an aggregation function (for example SUM()) with a GROUP BY command as the aggregation is managed internally within the cube itself. Using SQL Developer most DBAs can probably work around this feature by manually writing the query themselves. But for business users the tool is supposed to do all the work for them so it needs to be “embedded total” aware. Fortunately, BI EE can support this type of data source, so BI EE customers can combine Oracle 10g OLAP data with data from their other non-olap data sources quickly and easily.

(Unfortunately, Discoverer does not at the moment support embedded total objects so integrating SQL access to OLAP into the EUL is currently not possible).

How do you SQL enable an OLAP cube?

For this example I will use the common schema sample shipped with BI 10g (SH_OLAP schema) which contains a dimension called PRODUCTS that has the following structure:

  • Levels:
  • All Products
    • Categories
    • SubCategories
    • Products
  • Attributes
    • Long Description
    • Short Description
    • Level Description
    • Supplier ID
    • Unit of Measure
    • Weight Class
    • Pack size

To query the PRODUCTS dimension using SQL you have to create a view, which would look something like this:


The SQL query used to provide the data needs to be defined as follows:

















FROM table(OLAP_TABLE ('SH_OLAP.SH_AW duration session',
'', '', '&(PRODUCTS_LIMITMAP)'))

Now the view looks reasonably straightforward until you get about half through the code and notice the use of an OLAP_TABLE() function. This function returns a normal two-dimensional relational table structure from the source multi-dimensional object. The function has a number of inputs one of which is a call to a LIMIT_MAP. The OLAP_TABLE uses a limit map to map dimensions and measures defined in an analytic workspace to columns in a logical table. The limit map combines with the WHERE clause of a SQL SELECT statement to generate a series of OLAP DML LIMIT commands that are executed in the analytic workspace.

Oracle OLAP Reference Manual 10g Release 2 provides more details on the OLAP_TABLE() function.

In this example the limit map is called PRODUCTS_LIMITMAP and is stored within the analytic workspace and it looks like this:








It maps the AW objects to the columns referenced within the view. Having the limit map defined within the AW as a text variable makes it easier to modify the data that can be returned into the view by the analytic workspace.

The same techniques can be used to create a view to return data form a cube. Making a view over the sales cube, which contains 35 measures and attributes relating to 4 dimensions (Time, Channel, Geography, Products), would look something like this:























With a view over the cube it is now possible to select values from the cube from any level without having to issue an aggregation function or worry about using a GROUP BY command. For example, to create a report that shows the revenue, 12 month moving average, 6 month moving average, % growth from prior period, % growth from prior year for 1999, 2000 and 2001, for products at the Category level, for all channels and for all geographies the query could be written as follows:


COLUMN SR_MA_12M FORMAT 999,999,999.99
COLUMN SR_MA_6M FORMAT 999,999,999.99

break on PRODUCTS_LDSC skip 1

, SR_MA_12M
, SR_MA_6M
AND TIME IN ('1803', '1804', '1805')




The process for creating these special SQL views is now very simple thanks to a new plug-in for Analytic Workspace Manager. This has been created by OLAP product management team (Marty Gubar) and is an example of how to extend AWM using the new extensibility interface. Once you have downloaded and installed the addin you create your OLAP schema in the normal way. It is possible to create a relational view over both dimensions and cubes with the process being the same for both objects. Once you have loaded data into your analytic workspace right-mouse click on a dimension or cube and the “Create Relational View…” option should be visible at the bottom of the menu:

The first step of the wizard allows you to select the attributes and measures to make visible via the view. Be warned, you may have to deselect some attributes and/or measures to make this work. The reason being there is a SQL limit (4000 characters I think) within the process of defining a view, therefore, you may have to deselect some measures and/or attributes to successfully create the view. Unfortunately, no error message is displayed if things go wrong during the definition, however, when you try to select from the SQL view you may get an error message saying the LIMIT MAP does not exist. This implies you selected too many columns.

I am sure this issue will be resolved in a later build. For the moment it is not a major issue and the problem is quickly and easily resolved. Expand each node in the tree and deselect items that are not actually required:

Ideally when thinking about which views to create and the contents of those views you want to try and create views that minimize the need to use joins to create a result within the reporting tool such as BI EE. Therefore, it may be necessary to do some research first to determine which measures and attributes users actually need to allow them to create their reports. In other words do not try and expose every single attribute and measure.

This new addin to Analytic Workspace Manager (thanks to Marty Gubar) makes it very easy for BI EE customers to include data contained within a 10g OLAP cube as part of a BI EE report.

Useful Links

Analytic Workspace Manager

Relational View Generator software


Thursday, February 15, 2007

Using Market Basket Analysis to add value to OLAP

Using Market Basket Analysis to add value to OLAP

Anyone who has an interest in data warehousing and/or business intelligence understands the concepts of dimensions and hierarchies, which are key parts of a multi-dimensional model. In most cases the structure of a hierarchy is driven the corporate business environment. For example, if we consider the common schema (SH) that is shipped with the 10g version of the database, the product dimension has one single hierarchy that is based on normal business levels: Categories, sub-categories and products.

Regular readers of this blog will might remember a posting from last year where I explained how you could use attribute importance to help determine the most important dimensions within a cube. This posting uses another data mining feature to help business users analyse product groupings.

Most people are aware of the concepts of a shopping cart from buying products online, and it can be extremely useful to track the contents of these baskets. Each basket can provide valuable insight into customer buying patterns and provide the opportunity to identify options for cross selling. Therefore, to analyse customer-purchasing behaviour we need to extract the following information

  • Which product combinations are bought?
  • When they are purchased? and in
  • What sequence?
Using the information extracted from this analysis new product dimensions (or product hierarchies) can be created to track these baskets and provide business users with the opportunity to create new types of reports:
  • What are my top 5 baskets?
  • What are my bottom 5 baskets?
  • Which baskets are the most profitable?
  • How are my top 5 baskets performing in my key target regions
  • How have my top 5 baskets been performing over the last 2 years
  • What is the revenue forecast for next year for my top 5 baskets

One of the many benefits of Oracle’s 10g database is packed with features and the trick to getting the maximum benefit from the investment in Enterprise Edition is to make these features work together.

In this case we can use an association rules algorithm, part of Oracle Data Mining, to create an alternative product hierarchy that can be used to provide additional insight into business operations.

The association rules (AR) algorithm predicts the probability of co-occurrence among sets of attribute values. The most well-know use case for AR is market basket analysis, which predicts items occurring together in a market checkout session. This type of hierarchy can be used to help identify cross sell opportunities and product bundle strategies as well as product placement within online and the more traditional bricks and mortar stores.

Creating a Market Basket Analysis

Creating the association rules model
The sources for this example are the tables within the BI10g common schema. The SALES table contains point of sale data, which can be used as the main input to the market basket algorithm. To get started, you will need to have installed the Oracle Data Mining (ODM) Option (the default) during installation of the Oracle Database 10g. This ODM algorithm requires that the data be in this “transactional” format, with one item being represented in each row. This is an efficient representation, as any shopping basket will typically include several items from a possible hundreds or thousands of store items. The alternative, storing the data in a “single record per market basket”, would cause there to be hundreds to thousands of possible store items (attributes) with most of them being blank or null

Launch Oracle Data Miner client (for download details see the links at the end of this posting). Select Build from the Activity pull-down menu to launch the activity, and select Association Rules from the Function Type pull-down menu. Click Next.

The transactions (market baskets) are contained in the SALES table; select PROD_ID as the identifier for the items purchased. However, the products are identified only by an item code; the names of the products are in the PRODUCTS table, so click the checkbox indicating that there is a Name Lookup table, and select the table, the item identifier (PROD_ID), and the column containing the item description (PROD_NAME). This will then create meaningful product descriptions in the results table that can then used to populate the description for the dimension.

Click Next to continue.

Two columns are required to identify a single market basket. Click the checkboxes for CUST_ID and TIME_ID, then click Next.

The final step is to enter a name for the activity and click Next. On the final page of the wizard, click Advanced Settings to see the parameters available for Association Rules. In this case the default settings can be used. However, there are a lot of parameters that can used to tune the model. For more information regarding these parameters please refer to the documentation for Oracle Data Mining 10g Release 2. This can be downloaded from OTN.

Click OK to return to the last page of the wizard and click Finish to run the activity. When the activity completes, click Result in the Build step to access the rules defined by the model.

In this example, 115 rules were defined using the default parameter settings based on the transactions listed in the sales table

As well as the details of the products that comprise the rule two additional columns are provided: Confidence (%) and Support (%). The Support for the rule is the percentage of baskets containing the items in the rule. In the example, Support is the percentage of all baskets containing the three items mouse, keyboard and printer.
The Confidence for the rule is the percentage of baskets containing the item(s) in the antecedents that also contain the consequent. In the example, consider only baskets containing keyboard and mouse and calculate the percentage of those baskets that contain printers.

Saving the results
Finally, the results from the model can be published to a table. This table will then form the raw source for the new OLAP product dimension. Notice at this point there is no information relating to revenue, costs or quantity. All that information is already contained in the existing fact table that is used to feed the standard sales revenue, sales costs and sales quantity measures. All that needs to be done is to link the product bundles to the original fact table to extract the revenue, costs and quantity for each product within a bundle. The process for doing this will be explained later.

Designing the product dimension

The results from the algorithm table have to be transformed to provide a correctly formatted source for the dimension mapping. This requires the use of either OLAP DML (for those used to this powerful language) or PL/SQL. The aim is to create a new dimension table that confirms to the normal requirements for a source table. Therefore, a new top level has to be added to provide a total for “All Rules” and columns need to be populated for each of the attributes.

Each rule has to be broken down into its constituent parts:
  • The rule itself
  • The products that are part of the initial purchase
  • The products that are normally purchased in conjunction with the original items
From each rule the original product can be determined and referenced from the master Product Dimension.

This will allow us to track and analyze product bundles as part of the normal reporting process. Using the attributes associated with each rule, OLAP query steps can be created that further refine the analysis of product bundles by allowing business users to filter the results based on the value for % confidence associated with the rule.

In this example the decision has been taken to create a separate dimension to hold the results from the algorithm. The dimension is based around a single hierarchy with the following levels:
  • Top level – All Baskets
  • Rule – Rule derived from the algorithm
  • Group – Contains the IF and THEN parts of the rule
  • Product – Base product
In an ideal world, this information would be used to create a custom aggregate for each rule within the Product Dimension. However, although Discoverer for OLAP now supports custom aggregates there is no public API to help with the creation process. Therefore, the only way forward with this is to use a separate dimension to hold the rules.

This structure is outlined below with the associated attributes of:
  • Long description
  • Short description
  • Source product
  • Confidence %
  • Support %

The finished mapping looks like this:

Once the dimension has been maintained you can use the Dimension Viewer to check the results.

Designing the cube

The next requirement is to create a new source table that can be used in the mapping for the cube. Again some coding is required to create this new fact table. The code needs to extract the source products from each rule and identify the associated costs, revenue and quantity from the original fact table. This will then be moved to a new fact table to make mapping and loading data much easier to manager.

The new fact table looks like this:

When the cube is aggregated it will be possible to view the total revenue, total cost, total quantity, margin etc derived from the various products that are part of the rule.

A new cube called “MB” is created and the all the usual dimensions except in this case the new market basket dimension replaces PRODUCT dimension. The definition of the cube follows the same pattern as the other cubes in the sample schema.

In this case the figure for “All Baskets” which is the top level of the new market basket dimension is largely irrelevant. The reason for this is because the same product can appear in multiple rules, therefore, the value for total revenue, or total cost or total quantity is based on double counting of values. So this top value has to be treated with caution.

Now it is possible to create all the usual calculated measures for each of the base measures associated with a product bundle (revenue, costs, quantity, and margin) , such as those linked to the other cubes that are part of the common schema sample. For example:
  • 12 month moving average
  • 6 month moving average
  • Percent growth prior period
  • Percent growth prior year
  • Forecast
  • % Share of total revenue
  • % Share of total cost
  • % Share of total quantity
For the share calculations the formula needs to reach back to the original sales revenue cube that is dimensioned by the original product dimension. For this an OLAP DML formula is used which looks like this:


All these new measures can finally be added to a new measure folder called Market Basket Analysis and everything is ready for access by Discoverer OLAP, BI Beans and/or Spreadsheet Addin.

So what does this provide? With this cube it will now be possible to answer the following types of questions:
  • What are my top 5 baskets?
  • What are my bottom 5 baskets?
  • Which baskets are the most profitable?
  • How are my top 5 baskets performing in my key target regions
  • How have my top 5 baskets been performing over the last 2 years
  • What is the revenue forecast for next year for my top 5 baskets

Viewing the Results in Excel

Using Spreadsheet Addin it is now possible to connect to the updated schema. Launching the query builder shows the new dimension (Product Market Baskets) and the associated new measures for the market basket analysis

With this new dimension it is now possible to analyze the performance of each basket in the context of % growth prior period, % growth prior year etc. An obvious report to create is to show the performance of the top 5 baskets based on % growth prior year for year 2000, within the USA across All Channels.

The results from this query can be plotted on a line graph to show the revenue trend over the last 8 quarters as shown below.

To help you get started creating your own market basket analysis here are some links that might be useful:

Data Mining
Oracle Data Mining on OTN:

Oracle Data Miner Client on OTN:

Data Miner Client software:

Data Miner Client installation instructions:

Data Miner Tutorial

Oracle OLAP on OTN:

Analytic Workspace Manager software:

Analytic Workspace Manager demo:

Oracle OLAP Spreadsheet Addin on OTN:

Spreadsheet Addin software:

Vanity searches and the blog

On Google and Yahoo, the BI Blog (this blog) is ranked within the top 5 hits: #3 on Google, #4 on Yahoo. On Microsoft Live Search it comes up at #6.


And for the first time since I started the blog in 2005, it's been getting 1000+ hits every weekday for two weeks now. For some reason people don't like to surf the BI Blog as much on weekends. Hmm, that certainly is inexplicable. What can be more exciting to do on a weekend?

The flip side is that my personal blog has not seen a new post in three weeks.

I don't really know what else label to apply to this blog...

Tuesday, February 13, 2007

Other posts on BI EE

Mark Rittman has a couple of posts on his blog on BI EE:
Oracle BI Suite EE On General Release
Looking Closer at BI Suite EE 10gR3

Separately, Mark is also writing a book on Oracle BI (no, I am not involved with this book; but was, in a very, very small way :-), officially titled "Oracle Business Intelligence Suite Developers Guide". And there is a placeholder page for the book on You can track Mark's progress using this label page.

Oracle Docs being indexed by Google and misc OTN posts

This from the OTN blog post by Justin Kestelyn: "official Oracle Documentation is being indexed by Google as we speak."

Also, there is an Oracle DBA Toolbar for IE available.

There is also a custom OTN button available for the Google Toolbar (IE only :-)

Finally, check the OTN Semantic Web at, that was announced last month on the OTN blog here.

Sunday, February 11, 2007

BI Enterprise Edition - Admin Tool

The Admin Tool will be familiar to those who had used it under its earlier avtaar under Siebel Business Analytics. As far as look and feel goes, this is perhaps the one product that has changed the least. Mind you, there are lots and lots of great new features in this tool - "Data Mart Automation" being probably the single biggest one (or "Aggregate Persistence" as it is more commonly known as).

For me this is possibly the single biggest change in the default post-install experience of the Admin Tool - the demo repository that is contained in the installation is entirely self-contained and self-sufficient.
Since Oracle BI is capable of accessing relational, multi-dimensional, Excel, XML, and other data sources, the "Paint" repository has been retained, but in XML format. This means that you do not need **any** database to start using the Admin Tool, Answers, Interactive Dashboards, and other components. Very nice :)

You do not need to rewrite any of your UI screens, or change any configuration parameters, or anything - only one change is required: that of configuring the connection to point it to an XML data source.

The XML source files are located under the [Oracle BI folder]\Server\Sample\paint folder.

You can open the "Fact.xml" file to see its contents. In a relational database each 'Fact' rowset would be one row in 'Fact' table.

Friday, February 09, 2007

Best Practices Guide for Discoverer Plus for OLAP

The Discoverer Product Management team have compiled an excellent new document covering best practices for Discoverer Plus for OLAP. This will help users create workbooks and worksheets that are easy to maintain and correctly tuned to ensure good performance.

So what is in the document? There is a lot of information and I would recommend reading it very carefully. The main highlights are:
  • Design and organisation of workbooks and worksheets
  • Configuring Discoverer OLAP
  • Managing the Discoverer catalog
  • Setting up the analytic workspace
  • Tuning tips
As with all best practice documents this will be an going piece of work. If you have any additional hints and tips then please send them to us for inclusion in future versions of this document.

For now you can download the document from OTN via this link:

Wednesday, February 07, 2007

RSS Feeds in BI EE Dashboards

One of the several hundred new features in the latest release of BI EE is the ability to subscribe to RSS feeds of alerts and catalog folders, which is a really nice way of bringing the world of analytics to the user. In this post I look at subscribing to RSS feeds for catalog folders.

From your Dashboards Editor page, add a "Folder" dashboard object - and click the "Properties" button.

From the "Folder Properties" popup, click the "Browse" button, and select the folder or sub-folder and select it. Then, and obviously enough, check the box that says "Show RSS Link".

Save your changes. On the dashboard page, in addition to seeing your folder contents, you now also see the orange "XML" icon that now almost universally stands for an RSS feed. Right click this icon and copy the link's location.

Having copied the URL of the RSS feed (the orange icon thingy), the next step to do is to add this feed URL to an RSS feedreader. Thunderbird is as good a reader as any, so let's use it for our example.

In the RSS subscriptions window, click the "Add" button, and paste the BI RSS feed link that you copied above (the orange thingammy) and click 'OK'.

You most likely shall be prompted for a user name and password, so please do the needful - enter the correct password (the truth shall set you free... free to read the RSS feeds, i.e.).

Having done that, Thunderbird updates the feed.

Back in your Thunderbird main window, you can see that my feed shows some six feeds, five of which are unread. Click on any of the feed links to see the BI report displayed right there.

Pretty darn nice I should say... Needless to say I shall be using this to keep track of my feature area's bug reports (a BI EE dashboard and reports built on top of our internal bug tracking system).

A word about privileges and all that - as you would expect, you can specify whether someone, everyone, or no one has access to RSS feeds. To do that, you have to go into the "Administration" page (from the "Settings >> Administration" link). From there, find the line that says "Manage Privileges", click it, and then from the resulting page, click the link that says "RSS Feeds". There you can add or remove users or groups that have access to RSS feeds. The default option at the time of installation is "Everyone".

Lastly, if you need more information, for more details you can refer to the "Oracle Business Intelligence Answers, Delivers, and Interactive Dashboards User Guide, Version" as well as the "Oracle® Business Intelligence Presentation Services Administration Guide", Version, December 2006, Part Number: B31766-01, available from

BI Publisher and BI EE Suite Integration - Dashboards

Continuing on my earlier post on the integration between BI Publisher and BI EE (BI Publisher and BI EE Suite Integration - 1), this post looks at how you can add BI Publisher content to your BI Dashboard.

In case you are wondering what BI Publisher is (formerly known as XML Publisher), look no further than this page on OTN (well actually, look at this page, and then come back to this post):
Oracle BI Publisher reduces the high costs associated with the development, customization and maintenance of business documents; while increasing the efficiency of reports management. Utilizing a set of familiar desktop tools users can create and maintain their own report formats based on data extracts from diverse sources.
BI Publisher is built on open standard technologies. It is a J2EE application that can be deployed to any J2EE container.
If you see the screenshot below, I have a Dashboard page in edit mode, with two columns. The right column already contains a dashboard prompt, and a couple of Answers reports.
On the left column I want to add some BI Publisher content, which I have created and have available. This could be anything: from pixel perfect reports, to invoices, to standard reports, or just about anything else I may want to create using BI Publisher.
There is a new Dashboard object that has been added in BI 10.1.3, a "BI Publisher Report". This is what I shall use to add BI Publisher content to my Dashboards page.

Select and drag this "BI Publisher Report" object and drop it into the "Section 1" placeholder.

To specify the BI Publisher content, click the "Properties" in the object region above. This pops up a dialog where you can specify the content as well as how the content should appear...

For my purposes, I navigate to the "Shared Folders >> Business Intelligence" folder and select the "Paint Demo" report.

Click 'OK' and click the 'Save' button on the Dashboard page.
See below that the Dashboard page has updated with the BI Publisher report. I have two formats available for this report - PDF and RTF. Mind you, I could have specified HTML and other supported formats also, but for this report I have these two formats specified.
Since BI Publisher also allows me to associate multiple 'templates' or layouts to a single report, I can even select from these available templates, right here in my Dashboard page.

In this case I have a second template, "Revenue Grouped by Year, Region" also available, in case the charts are not what I want to look at.

And there you have it - a nicely formatted report, with all the correct headers, footers, page breaks, etc... The world of adhoc analytics meets the world of pixel perfect reporting.

Related links (I promise to stop including these related links in every post soon, real soon):

Tuesday, February 06, 2007

BI Publisher and BI EE Suite Integration - 1

This is the Oracle Interactive Dashboards page that is available by default (based on the default cataog and repositories available). If you click the "More Products" link, it drops down a list of additional products available to you - this list is dependent on the products you select during the installation. In the Siebel Business Analytics days, this list was actually governed by the license XML file you had. In Oracle you can download and install all the products you want - use them under a developer's license to evaluate them, and pay for them if you intend using them otherwise. If you chose to install BI Publisher, then this product will be listed as an option in the dropdown. Click the link.

You are taken to the Oracle BI Publisher Enterprise home page. Note that you do not have to re-login.

You can create new report by clicking the "Create a new report" and typing in the name of the report. The report is created under the current folder. You could always go and create a BI Publisher report against any supported data source, and that now includes RSS feeds also (have to check if that's been available before also...). But in this case I shall create a report that goes against the BI Answers presentation layer.

If I click the "Edit" link for the report (see the list of links available - "View", "Schedule", "History", "Edit", "Configure"), it takes you to the page where you can edit the report, and change such things as the report's data source, data model, add/remove templates, etc...
Note below that there is a new "Data Source" available, named "Oracle BI EE".

That is the data source I shall use. Click the "Query Builder" button and it takes you to BI Publisher's "Online Query Builder". From the top right hand drop down I can select from either of the two subject areas available to me: "Paint" or "Paint Exec". The list of (logical) folders is based on the schema I select, in this case "Paint".
I can drag and drop any folder to the 'canvas', and check/uncheck the fields that I want included in my report. Note that I do not have to specify any joins here, as the BI Analytic Server shall take care of resolving any joins.

Click on the "Results" link, and the results of the query are fetched.

Click the "Save" button and you see the "SQL Query" field updated with the sql for the report. At this point, you can upload a template if you have one available, or use the "Oracle BI Publisher Template Builder for Word" to create a (or more than one) template and associate it with the report. That is a topic for another post, another day.
Also, there is the other side of this integration, which is the fact that you can publish BI Publisher reports to Interactive Dashboards. That also, I shall post soon.

To take a peek at how this integration has been done, click the "Admin" tab. On the Admin page, at the bottom you shall see a section named "Integration". "Oracle BI Presentation Services" is the link that you use to configure BI Publisher to integrate with Oracle BI Presentation Services.

The page shows you all the details - if you install Oracle BI EE using the complete install option, these values are filled in by the installer. Else you can always go back and add/change them.

Related posts: