How-to: Bridge Tables and Many to Many Relationships Demystified in OBIEE 11g

Bridge tables - entire books have been devoted to this concept, countless blogs write about it, and organizations offer entire classes dedicated to demystifying this idea. Ralph Kimball, creator of Kimball Dimensional Modeling and founder of the Kimball Group has written quite a few great articles discussing the theory of bridge tables.

Yet when researching for comprehensive guides on how to actually implement a bridge table in OBIEE 11g, the documentation available is either:

• Out of date
• Contains implementation steps for OBIEE 10g which has since been deprecated
• Does not contain adequate detail 
• e.g. missing key steps

This guide is going to outline the basic use case of a many to many relationship, how OBIEE 11g resolves this dilemma and how to successfully implement a bridge table model within the 11g platform.

First thing's first - what is a bridge table and why do we need it?

At its core, bridge table solve the many to many relationship we encounter in many datasets. Many to many relationships in itself are not "bad", but when attempting to conform a data set to a star schema - many to many relationships just do not work. Star schemas assume a one to many (1:N) cardinality from the dimension to the fact. This means "one attribute of a dimension row can be found in many rows of the fact table".

For Example:

• One job (job dimension) can be performed by many people
• You would see the same JOB_WID repeating in the fact table
• One employee (employee dimension) can have many jobs
• You would see the same EMPLOYEE_WID  repeating in the fact table
• One call at a call center(ticket dimension) can have many ticket types
• You would see the same CALL_WID repeating in the fact table
• One patient (patient dimension) can have many diagnosis
• You would see the same PATIENT_WID repeating in the fact table

This 1:N cardinality is represented in OBIEE as (using call center/employee example) :

"Cardinality of '1' applied to the dimension and cardinality of 'N' applied to the fact'

But what happens when in the above examples, the cardinality is actually N:N? 

For Example:

• Many employees can have multiple jobs and each job can be performed by multiple employees
• Many patients can have multiple diagnosis and each diagnosis can be 'assigned' to many patients
• Many calls can have multiple call ticket types and each ticket type can belong to multiple calls

This many to many relationship is initially (and incorrectly) represented in OBIEE 11g as:

'Cardinality of '1' is applied to the two dimension tables and cardinality of 'N' is applied to the fact'

Any BI Architect should recognize the above model - it's a traditional star schema! If you stop here and decided to ignore the issue with your dataset and 'hope' OBIEE aggregates the model correctly, you're about to be disappointed.

Why star schemas dont work for N:N cardinality

Consider the following scenario: You're a call center manager and you want to capture the number of calls with positive feedback per employee. You also want to capture the type of calls an employee answers in any given day.

Upon analysis of the requirements you conclude that "each call received by an employee can have many call types and each call type can be answered by multiple employees".

For example:

• I answer a take a call that is classified as a 'new call', 'urgent', and 'out of state transfer' (three different call types) - this is the "each call received by an employee can have many call types".
• A colleague also received a phone call that is classified as 'out of state transfer' - this is the 'each call type can be answered by multiple employees"

Now let's put this data in a traditional star schema fact table as modeled below:

ID	EMPLOYEE_WID	CALL_TYPE_WID	NUMBER_OF_GOOD_CALLS
1	1	1	300
2	1	2	300
3	1	3	300
4	2	2	500
5	2	3	500
6	3	1	200

You can see in the above data set that:

• EMPLOYEE 1:
• Has 3 different call types
• Has 300 positive reviews (NUMBER_OF_GOOD_CALLS) 
• This metric is at the EMPLOYEE level and not the call type level!
• EMPLOYEE 2:
• Has 2 different call types
• Has 500 positive reviews (NUMBER_OF_GOOD_CALLS)
• This metric is at the EMPLOYEE level and not the call type level
• EMPLOYEE 3:
• Has 1 different call type
• Has 200 positive reviews (NUMBER_OF_GOOD_CALLS)

Now you receive a requirement to create a KPI that displays the Number of Good Calls as a stand alone widget.

PROBLEM 1 - Aggregation :

The number of good calls you received based on the above fact table is not 2100 - it's 300 + 500 + 200 = 1000

• Employee 1 received 300 good calls
• Employee 2 received 500 good calls
• Employee 3 received 200 good calls

but due to the many to many cardinality of the data, the star schema duplicates the measures because each employee can take calls of many call types and each call type can be assigned to many employees!

PROBLEM 2 - Grand Totaling:

What if you don't care about aggregates? What if you just want a report that contains the employee, call type and a summation/grand total?

Notice how NUMBER_OF_GOOD_CALLS is repeated across multiple call types and the grand total is still incorrect. It's being duplicated due to the many to many relationship that exists between call type and employee. Furthermore, it paints an incorrect picture that NUMBER_OF_GOOD_CALLS is some how related to CALL_TYPE

How do we resolve this many to many cardinality with a bridge table?

When all is said and done, the incorrectly built star schema:

should be modified to:

Let's break this down:

The bridge table:

This the purpose of the bridge table is to resolve the many to many relationship between the call type and employee. It will contain, at a minimum, the following four columns:

1. The primary key of the table
2. The EMPLOYEE_WID
3. The CALLTYPE_WID
4. The weight factor

The weight factor is what's going to resolve the issue of double counting. 

• If an employee has 3 call types, there would be 3 rows and the weight factor of each row would be .33
• If an employee has 10 call types, there would be 10 rows and the weight factor of each row would be .1

In our bridge table data set, we're going to use the same 3 EMPLOYEE_WIDs and create the following:

ID	CALL_TYPE_WID	EMPLOYEE_WID	WEIGHT
11	1	1	0.33
12	2	1	0.33
13	3	1	0.33
23	2	2	0.5
24	3	2	0.5
31	1	3	1

You can see from this example that we've taken the N:N dataset in the fact table and moved it into this bridge.

The dimension that is joined to both the fact and bridge

This is a generic dimension that contains the unique EMPLOYEE IDs in your organization's dataset.

For example:

ID	EMPLOYEE_ID
1	1
2	2
3	3
4	4
5	5
6	6
7	7
8	8
9	9
10	10

The dimension that is joined to only the bridge table

This dimension contains all of the possible call types. Note how this table is not physically joined to the fact. This is because this specific dimension (CALL_TYPE) is what's causing the N:N cardinality

For example:

ID	DESC
1	Call Type 1
2	Call Type 2
3	Call Type 3
4	Call Type 4
5	Call Type 5
6	Call Type 6
7	Call Type 7
8	Call Type 8
9	Call Type 9
10	Call Type 10

The Fact Table

We've moved the N:N cardinality from the original fact table to the bridge table so the new fact table now contains exactly one row per employee and does not have the CALL_TYPE_WID.

ID	EMPLOYEE_WID	NUMBER_OF_GOOD_CALLS
1	1	300
2	2	500
3	3	200

How do we implement this model in OBIEE 11g?

Step 1: Import Tables into Physical Layer

This is always the first step performed when creating a model regardless of its type. In the above example i'm importing four tables:

Step 2: Create the Physical Data Model

Based on our data set above the join conditions would be implemented as follows:

• 1:N relationship from employee dimension to fact table
• 1:N relationship from employee dimension to bridge
• 1:N relationship from call type dimension to bridge

Notice how employee_demo_d is the only dimension that is joined to the fact. w_call_type_d is not joined to the fact because that is the dimension that is causing the many to many relationship issue.

Step 3:  Create the Logical Data Model
The creation of the BMM is where we deviate from our standard build steps of a traditional star schema:

1. All associated dimension tables referencing the bridge table will be stored in a single BMM table
2. The single BMM table will have two logical table source

Step 3.1 : Drag the fact table and dimension table that is connected to the fact table into the BMM. 

In our example, we are dragging w_calls_f and w_employee_demo_d into the BMM:

Step 3.2: Create a 2nd LTS in the existing dimension table

1. Right click W_EMPLOYEE_DEMO_D -> New Object -> New Logical Table Source
2. Name it 'Bridge'
3. Add W_BRIDGE_D and W_CALLTYPE_DEMO_D (the two dimensions not directly joined to the fact table) under the 'Map to these tables' section

1. Next add the remaining dimension columns from W_CALLTYPE_DEMO_D and W_BRIDGE_DEMO_D to the Dimension table in the BMM

Step 3.3: Create a level-based dimension hierarchy for the dimension BMM

1. This step should be completed whether or not the schema is a star or bridge

Step 3.4: Confirm the BMM model has a 1:N relationship from the dimension to fact

Step 3.5: Set aggregation rule of NUMBER_OF_GOOD_CALLS to sum 

All measures in the BMM must have a mathematical operation applied to the column

Step 3.5: Set the Content level of the dimension table to 'detail' in within the LTS of the fact table

Again, this is something that should always take place regardless of the type of model

Step 4: Create the Presentation Layer

This part is straight forward, just drag the folders from the BMM into the new subject area:

The moment of truth

So why did we go through this elaborate exercise again? To fix the aggregation issues we were having with NUMBER_OF_GOOD_CALLS due to the N:N cardinality of the data set. Let's create that 'standalone KPI' Number of Good Calls:

Notice how the metric correctly sums to 1000. Let's check the back end physical query to confirm:

Notice how it's hitting the fact table and not the bridge or the call type dimension. 

But what about the weight factor?

Let's go back to the scenario where we want to compare across dimensions joined via the bridge table (EMPLOYEE and CALL_TYPE):

• When creating a report that uses a measure from the fact table, a dimension value from the the employee table, and a dimension value from the table that causes the N:N cardinality - you need to use the weight factor to make sure your measure isn't getting double or triple counted:

• Notice column is using the the NUMBER_OF_GOOD_CALLS multiplied by the WEIGHT factor in column 2
• Each row in column 1 correctly represents the NUMBER_OF_GOOD_CALLS in the fact table despite having the repeated values of multiple call types
• Note the aggregation of grand total sums to 997. This is because the weight factor is rounded to the 2nd decimal for EMPLOYEE_WID = 1 (.33%)

In order for grand totaling to work correctly with bridge table measures that use weight facts you must set the aggregation rule of the column (in this case column 1) to sum within Answers:

So what did we accomplish in this guide?

• A basic understanding of many to many (N:N) cardinality
• A basic understanding of why the star schema won't work for N:N cardinality
• How to resolve the cardinality issue with a bridge table
• How to implement a bridge table in OBIEE 11g

keywords: bridge table, cardinality, many-to-many, OBIEE 11g, helper table, answers, analytics, aggregation, LTS, measures, kimball

FYI: GoURL Primer with OBIEE 11g

The Oracle BI Presentation Services Go URL command is for use in incorporating specific Oracle Business Intelligence results into external portals or applications. The Go URL is used when you add a result to your favorites, or add a link to a request to your dashboard or an external Web site. It has a number of forms and optional arguments that can be used to control its behavior.

Oracle has provided limited documentation on goURL parameters and with use cases that require end users to navigate to specific dashboards or request dashboards be embedded into pre-existing web pages, i've decided to create a Primer on commonly used GoURL functionality:

Common goURL Parameters:

Parameters	Syntax	Definition
NQUser	&NQUser=x	User ID
NQPassword	&NQPassword=x	Password
Path	&Path=x	Path of the answer to execute. You can find it in the properties page (Answers/Manage Catalog/ and click on the properties icon (a little hand) of the object that you want. See picture above
Link Options	&Options=x	The x can be one or more of the following letters: * m : Modify Request * f : Printer Friendly * d : Download to Excel * r : Refresh Results
Printer Friendly	&Action=print	Results are in a printer-friendly format, without the paging controls, hot links, and so on.
Passing Filters	&Action=Navigate	To apply filters to the answer (see section below on Passing Filters)
Application Friendly	&Action=Extract &Action=Scroll	Results are displayed in an application-friendly format, such as for Microsoft Excel, without the paging control, hot links, and so on. The Extract action also acts as a Navigate action (read Passing Filters to the Oracle BI Presentation Services Go URL Through a URL (Navigation)) so you can filter the results that are returned by the call.
Specific View	&ViewName=x	This shows an individual result view rather than the default compound view
Specific View	&ViewID=go~x	This shows an individual result view rather than the default compound view where x is the name of the view
Specific Style	&Style=x	This shows the results using a specified style. If the style does not exist, the default is used.
Result Format	&Format=x	This controls the format of the results. This is the format, where x can be xml, html, txt (tab separator), csv (comma separator)
File Extension	&Extension=.csv	This controls the file extension of the download file
Language	&Lang=fr	This controls the language of the report. The value permitted are the values of weblanguage
done	&done=portalPages	parameter allows you to create a return link equal to a path that you have specified

Passing Filters

You can use the &Action=Navigate in conjuction with the with the parameters below to pass filters directly to a report via goURL. Use &P0=n where n equals the number of parameters you wish to filter and P1...to P6 with the one or more of the operators below:

Operator	Meaning
eq	Equal to or in.
neq	Not equal to or not in.
lt	Less than.
gt	Greater than.
ge	Greater than or equal to.
le	Less than or equal to.
bwith	Begins with.
ewith	Ends with.
cany	Contains any (of the values in &P3).
call	Contains all (of the values in &P3).
like	You need to type %25 in place of the usual % wildcard. See the examples that follow.
top	&P3 contains 1+n, where n is the number of top items to display.
bottom	&P3 contains 1+n, where n is the number of bottom items to display.
bet	Between (&P3 must have two values).
null	Is null (&P3 must be 0 or omitted).
nnul	Is not null (&P3 must be 0 or omitted).
&P2=ttt.ccc	In this parameter, ttt is the table name and ccc is the column name. If the table or column contains spaces, it must be quoted with double-quotes.  Spaces should be escaped as %20, for example, Measures."Dollar%20Sales".
&P3=n+xxx+yyy+...+zzz	In this parameter, n is the number of values, and xxx, yyy, and zzz are the actual values. Note: If the value of P3 begins with a numeric character, the entire value must be enclosed in quotes. example: saw.dll?Go&Path=/Shared/Test/SB2&Action=Navigate&P0=1&P1=top &P2=Customers.Region&P3="7

Passing Filters Examples:

his returns records for the East and Central regions:

Saw.dll?Go&Path=/Shared/Test/SB2&Action=Navigate&P0=1&P1=eq&P2=Customers.Region&P3=2+Central+East

This returns records for like Regions E....t:

saw.dll?Go&Path=/Shared/Test/SB2&Action=Navigate&P0=1&P1=like&P2=Customers.Region&P3=1+E%25t

This returns the top two regions by dollars sold:

saw.dll?Go&Path=/Shared/Test/SB2&Action=Navigate&P0=1&P1=top&P2="Sales%20Facts".Dollars&P3=1+2

This is an example where the number of arguments is not included in the syntax:

saw.dll?Go&Path=/Shared/Test/SB2&Action=Navigate&P0=1&P1=top&P2=Customers.Region&P3=Central

This returns records with between 2,000,000 and 2,500,000 in sales:

saw.dll?Go&Path=/Shared/Test/SB2&Action=Navigate&P0=1&P1=top&P2="Sales%20Facts".Dollars&P3=2+2000000+2500000

This returns records for Regions beginning with the letter E:

saw.dll?Go&Path=vate&P0=1&P1=bwith&P2=Customers.Region&P3=1+E

This returns records for Regions containing the letter E and having more than 20 million in sales:

saw.dll?Go&Path=/Shared/Test/SB2&Action=Navigate&P0=2&P1=cany&P2=Customers.Region&P3=1+e&P4=gt&P5="Sales%20Facts".Dollars&P6=1+20000000

Generating SQL Statements using goURL:

The Go URL command can be used to issue Oracle Business Intelligence SQL. These forms of the Go URL return tabular results. The basic options from &Style= and &Options= can be used here as well.

To issue Oracle Business Intelligence's simplified SQL, include the escaped SQL as a parameter to the Go URL. For example:

saw.dll?Go&SQL=select+Region,Dollars+from+SupplierSales

where the FROM clause is the name of the Subject Area to query.

Alternatively, the command IssueRawSQL can be used to bypass the Web processing and issue SQL directly against the BI Server.

 

keywords: OBIEE 11g, goURL, answers, ad-hoc, analysis, analytics

How-to: Automatically Refreshing Dashboards in OBIEE 11g via iFrame Bursting- The Flat Screen TV Use Case

With production costs associated to flat screen LCD/plasma/LED TVs dropping into the 100s of dollars over the past few years, these TVs are no longer relegated to the home theater space. Now enterprises can utilize these products as part of their campaign to bring analytics to the forefront of their business. Consider the following scenarios:

• A call center needs a mechanism to communicate performance metrics to the office without daily emails, meetings and paper-printed dashboards
• An IT organization needs to track hardware utilization 'in real time' with passive notifications activated during peaks and irregular activities
• The firm's testing team needs the ability to communicate defect and resolution metrics against service level agreements across multiple teams

Short of daily emails and unnecessary meetings, it was up to the individual worker to take the initiative to stay informed. The above scenarios can be effectively resolved through the use of 'Dashboard Displays' on flat screen TVs strategically placed throughout the office to keep leadership informed and team members accountable.

When we think of how OBIEE is used, the typical scenario that comes to mind is a user logging into the analytics front page. While little documentation exists, it is certainly possible to transform OBIEE from a 'user-based interactive analytics tool' to a 'dashboard style display fitted to large monitors'.

The main issue OBIEE Architects face is how resolve the issue of stale data across the technology stack. Stale data can exist within the BI Server, BI Presentation Services and even in the browser. Each component has its own resolution technique as well as potential issues. When a dashboard is displayed on a static monitor, a mechanism must be in place to ensure it's refreshed and timed correctly with existing agents.

 This guide is going to outline the steps required to implement OBIEE for the above scenarios. The technology used includes:

1. iFrame Bursting (disabled by default in OBIEE 11g)
2. Oracle WebLogic to redeploy modified XML components
3. goURL
4. HTML  (Meta Tags)
5. Oracle BI 11.1.1.7.0

Some blogs on the web recommend using javascript to achieve this but I do not recommend this approach as javascript execution is based on the permission of the browser and has the potential to be disabled. At the very least a pop up box will appear asking the user if they authorize the javascript function to execute - not a pleasant end user experience.

Furthermore, Oracle has acknowledged that the mechanism they developed for auto-refresh does not work and no known resolution exists - see Doc ID 1416002.1 and  Bug 13058029. What is interesting is that Oracle's method used to work in OBIEE 10g!

Step 1: Enable iFrame Bursting

By default, OBIEE 11g server does not allow dashboards to be displayed in an iFrame. The reason this is done is to better secure the dashboards and prevent hackers from using an iFrame to overlay malicious content.

Step 1.1. Navigate to export/obiee/11g/instances/instance1/config/OracleBIPresentationServicesComponent/coreapplication_obips1 and modify instanceconfig.xml 

Add the following red code to the file:

 <Security>
<InIFrameRenderingMode>allow</InIFrameRenderingMode>
 </Security>

Step 1.2 Download analytics.ear

Navigate to:

/export/obiee/11g/Oracle_BI1/bifoundation/jee and download analytics.ear

The analytics.ear file contains two war files that house the web.xml that needs to be modified.

Step 1.3 Modify web.xml

Decompress the analytics.war and analytics-ws.war file using 7zip and modify the web.xml in each war file by adding the red code to the file:

<servlet-name>RelatedContent</servlet-name>
<url-pattern>/RelatedContent</url-pattern> </servlet-mapping>
<context-param> <param-name>oracle.adf.view.rich.security.FRAME_BUSTING</param-name> <param-value>never</param-value> </context-param><login-config> <auth-method>CLIENT-CERT</auth-method> </login-config>

Step 1.4 Upload & Recompress the war files

Upload the two war files back to /export/obiee/11g/Oracle_BI1/bifoundation/jee

Step 1.4.1 Recompress analytics.war

Navigate to:

 /export/obiee/11g/Oracle_BI1/bifoundation/jee/analytics/analytics

Execute:

 jar cvf0 ../analytics.war 

(Note: Include the space and period at the end of the command)

Navigate up one level to:

/export/obiee/11g/Oracle_BI1/bifoundation/jee/analytics

Delete the analytics folder so that the only files that remain in that directory are: 

(1) META_INF, (2) analytics.war, and (3) analytics-ws

Step 1.4.2 Recompress analytics-ws.war 

Navigate to:

 /export/obiee/11g/Oracle_BI1/bifoundation/jee/analytics/analytics-ws

Execute: 

jar cvf0 ../analytics-ws.war . (Note: Include the space and period at the end of the command)

Navigate up one level to:

/export/obiee/11g/Oracle_BI1/bifoundation/jee/analytics

Delete the analytics-ws folder so that the only files that remain in that directory are:

 (1) META_INF, (2) analytics.war, and (3) analytics-ws.war

Step 1.5 Recompress the analytics.ear file

Navigate to:

 /export/obiee/11g/Oracle_BI1/bifoundation/jee/

 Rename the old analytics.ear file as a backup

Execute: jar –cf analytics.ear –C analytics . (Note: Include the space and period at the end of the command)e)Delete the analytics folder.

Make sure that the analytics.ear file was produced in the export/obiee/11g/Oracle_BI1/bifoundation/jee/ folder. 

Delete the analytics folder.

Step 2: Restart BI Services

This can be achieved by either running opmnctl stopall via command line or through Enterprise Manager as depicted below:

Step 3: Create a test HTML file with the embedded iFrame:

Open a text editor of your choice and paste in the following:

<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Frameset//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-frameset.dtd">
<HTML>
<HEAD>
<TITLE>Important Dashboard</TITLE>
<META http-equiv=no-cache>
<META content=900 http-equiv=refresh>
</HEAD>
<BODY>
<IFRAME style="Z-INDEX: 998; POSITION: absolute; WIDTH: 100%; HEIGHT: 100%; TOP: 0px; LEFT: 0px" src="http://yourdashboard.com/ImportantDashboard" frameBorder=no scrolling=auto> </IFRAME>
</BODY>
</HTML>

Note the two important components highlighted in red:

1. The Meta tags forcing the automatic refresh every 900 seconds. 
2. The iFrame that embeds the URL of your dashboard

So at the end of the day what do we have?

1. The ability to display dashboards on a static device (large monitor) that will have little human interaction but requires up to second (or in this case every 900 seconds) data
2. The ability to embed a dashboard in an iFrame.

 

keywords:  OBIEE 11g, iFrame, auto-refresh, answers, web.xml, instanceconfig.xml, goURL, analytics