The number one comment is, “Nice haircut.”

The same thing seems to happen every time I switch from bearded to beardless. The fact is that I didn’t get a haircut. I shaved my beard. But people are creatures of habit. When something is different but not “different enough” it seems to be hard to properly discern the true change. People correctly identify that I have less hair. They incorrectly identify the source of the change.

Can the same thing happen with our reports? Sure. The correct result can be right in front of us, but we’re so accustomed to seeing things in a particular way that we can still derive the wrong conclusion even with correct data. What does this say about our ability to be successful in our attempts to convey critical information via our various business intelligence tools? How effective can a message be (even if it’s the correct message) if it is interpreted incorrectly?

One of the biggest challenges with outer joins (other than potential performance issues) is explaining to a user why their query results changed because they added a condition to their query. Remember that users don’t (typically) look at the SQL, so they won’t know that I have created an outer join. It can be confusing. Fortunately I have options as to how my outer joins are executed, so once I determine their usage requirements I can change the way my universe behaves.

Defining the Problem

For this post I will am going to use a very simple universe with only three tables, shown here.

This universe joins a customer to an order, and an order to order lines. In my database I have one customer that does not yet have any orders. If I run a query against the current universe structure, this new customer will not show up. My requirement is to show all customers, whether they have orders or not. This must be true even if I put a condition on the order table. That’s where it gets tricky.

ANSI_92 Parameter Setting

In order to make this work, I have to make sure that my universe parameter setting for ANSI_92 is set properly. From the File menu I will select Parameters, and on the tabbed dialog box that appears I will click on the Parameters tab. I need to set the ANSI_92 parameter to “Yes” in order for this technique to work. Without this setting, my joins will be created using Oracle-specific syntax and that isn’t what I want here.

Once I have verified this setting I can close the parameter screen. Next I need to define my outer join.

Defining an Outer Join

The outer join definition is done the same way with all databases, but it’s not always done in the same direction. In some databases I have to check the primary table and in others I need to check the secondary or optional table. Rather than trying to remember which database does what, I will pick a side and check to make sure that the tiny “o” appears on the expected side of my join. For example, I want to reconfigure my existing inner join between customers and orders and make it an outer join as shown here.

The structure window looks a bit different now.

As mentioned earlier, the tiny “o” on the right side of the join denotes the optional side. If the “o” is on the wrong side, I will open the join properties window and select the other checkbox.

That’s how I create an outer join in my universe. Let me show how it works first, then I will show how I can break it.

Outer Join Queries

I have exported my outer join universe and created a very simple query. I have included the customer ID, name, and their order total. There is one customer (appropriately named “New Customer”) that has no orders and their order amount shows up blank because of the outer join setting.

So far everything is working as I expected.

Breaking an Outer Join

An outer join is designed to allow rows that don’t completely match to still show up on my report. What happens if I apply a condition to the outer relationship? For example, I might want to add a condition to limit orders to a particular date range.

Here are the results after running this query.

What went wrong? (Answered on next page…)

The Problem Definition

For the example I will present in this post I will once again use my version of Island Resorts Marketing universe which I have converted to Oracle. I will create a report that initially shows the Resort and (for simplicity) a single measure (Revenue). The report will be designed to let the user drag on additional details like Service Line and Service. But I will design my objects in such a way that if the user doesn’t want to see the information at that level of detail they don’t incur the overhead (row count) simply because the object is present in the query. In order to accomplish this, I will prompt the user with a list that includes the tokens ‘Resort’, ‘Service Line’, and ‘Service.’ The user will select the lowest level of detail they expect to use on the report. In this particular example the selections are hierarchical, meaning that selecting ‘Service Line’ implies that the Resort data will also be present. There is another option ‘None’ that can be selected if they want to deactivate the entire list.

Note that XI 3.1 offers a new feature called Query Stripping (in service pack 3) that works for BW and other OLAP queries and does this process automatically. It is not (yet) available for relational databases.

My steps are:

• Create a derived table to provide values for my prompt
• Create a custom LOV query to display the prompt values in the desired order
• Create a prompt object that allows a user to select the desired level of detail
• Create custom Level of Detail (LOD) versions of the impacted dimension objects
• Build my report

Creating the Level of Detail Prompt

First I have to create some data in my universe to feed my list of values (LOV) query. I have detailed this technique before. It’s quite simple to use a derived table and select against the DUAL table (in the case of Oracle) or equivalent and build any sort of list. Something like this will give me a list of values for the Resort level of detail (LOD) prompt.

select 1 as LOD_Order, 'Resort'  as LOD_Resort from dual
union
select 2, 'Service Line' from dual
union
select 3, 'Service' from dual
union
select 4, 'None' from dual

I have defined two columns for my derived table. The first is called LOD_Order and it will be used to order the prompt items in the way I expect to see them. The second column is the value that will populate my LOV. After creating this derived table I checked to see what the values were, and they came up as expected.

Creating the Custom LOV Query

The next step is to build the objects I need to create the list of values (LOV) that will appear in my prompt definition. I will create a class called “LOV” that includes objects for both the LOD_ORDER and the LOD_RESORT columns. Eventually these objects will be hidden, but I will need them to be visible in order to create my custom LOV query. The LOV will be built on the Resort LOD object, and initially it won’t look too special. It includes only the selected object. The trick is that I want to see the values in a specific order; that’s why I added the “order” column to my derived table. In order to create a sort on this object, I need to click on the Manage Sorts button shown here.

That button is only active if my database supports an order by clause that includes objects that do not appear in the select clause. If that button does not appear and I know that technique will work, I can update my designer parameter file as detailed in an earlier post titled, “SORT_BY_NO=NO? Very Confusing…” which shows how to accomplish that. Clicking that button allows me to define a custom sort based on the LOD Order column.

When I run the query I see the rows in the order I expect.

Defining the Prompt

I am going to reuse the prompt logic in multiple objects (Resort, Service Line and Service) so I am going to define it once and reference it using the @Select() function. Here’s the prompt:

@Prompt('Please select desired Level of Detail','A','LOV\Resort LOD',mono,constrained)

This is fairly standard syntax. The prompt text is defined, and the type of response is ‘A’ for character data. The LOV definition points to the custom query I created in the last step. I only want to allow a user to select a single value, and because the data only works if it comes in as I expect I am constraining the user. They have to pick from my list in order to proceed.

Building the Level of Detail Objects

I will be able to save some effort by reusing the prompt definition using the @Select() function. Here’s what my new Resort object looks like:

case @Select(Level of Detail\LOD Prompt)
when 'None' then 'Resort N/A'
else RESORT.resort
end

Resort is the top of the list. That means if I pick Resort, Service Line, or Service, I want to see the resort values populated. This means I can take a shortcut. If the user selects ‘None’ then I won’t show the resort values. If they pick anything else, I will.

The rest of my objects look very similar.

Service Line

case @Select(Level of Detail\LOD Prompt)
when 'None' then 'Service Line N/A'
when 'Resort' then 'Service Line N/A'
else SERVICE_LINE.service_line
end

Service

case @Select(Level of Detail\LOD Prompt)
when 'None' then 'Service N/A'
when 'Resort' then 'Service N/A'
when 'Service Line' then 'Service N/A'
else SERVICE.service
end

What I did was add a line of code to each of my objects, since each was at a lower level of detail. Note that I could have taken the opposite approach and defined my Service object like this:

case @Select(Level of Detail\LOD Prompt)
when 'Service' then SERVICE.service
else 'Service N/A'
end

Building the Report

I can now create a very simple report that includes all three of my new LOD objects and the Revenue. When I run the report at the Service level of detail it will show all of the detailed data, just as it would have before I created these custom objects. The results from Island Resorts end up being 27 rows of data.

If I rerun the exact same report at the Resort level of detail I get three rows.

The objects that I dropped from my level of detail are showing “N/A” values in the block. The prompt shows my four choices in the desired order because of the customizations I did for the LOV earlier.

What is the true impact here? What have I really accomplished?

Impact Analysis

Sample databases like Island Resorts are great for fooling around because they’re small. They’re not so great for demonstrating techniques like this because the true impact is hard to determine. I went from 27 rows down to 3, not really a big deal, right? On a larger database with more combinations of dimension values, the impact could be far more significant. I might drop from 50,000 rows down to several hundred. And the beauty of it is that if the user doesn’t need the level of detail, they don’t pay the penalty of downloading 50,000 rows only to roll it up (via projection) on the report. If they later drag on one of the suppressed objects and see the “N/A” result, all the user has to do is refresh the report and select the new level of detail to get more rows.

My sample report screen shots included all of the objects at the same time in order to show what was really happening. When I used this technique for a real report, I only included objects that I wanted to see from the beginning. The other LOD objects were listed in the available objects.

Alternate Solution

The solution I outlined here works as desired if there is a rigorous hierarchy and the user is just selecting the lowest level of detail desired on the report. What I actually had to implement was a bit more complex and I will provide a sketch of the solution here. The actual requirement was based on different combinations of dimension objects. A matrix of choices might look like this:

Resort (by itself)
Service Line (also by itself)
Service
Resort + Service Line
Resort + Service
Service Line + Service
Resort + Service Line + Service
None

Instead of the simple case statement objects I showed earlier, I had to use a pattern matching function and look for the string “Resort” anywhere in the prompt result. If the user picked anything that included the Resort as an option, then the Resort object would return a value. Ultimately I created prompt “bit” objects in order to simplify the logic.

Conclusion

I thought this was an interesting technique to share at this point, what with the recent release of SP3 with the Query Stripping feature for cube data. It worked well for the project. In some cases this strategy allowed us to reduce the number of rows by an order of magnitude (10,000s of rows down to 1,000s or even hundreds) while retaining a large number of objects in the report. The biggest drawback of this solution is that my SQL code will still hit the requested tables, even if I am not really requesting any data. Going back to my earlier example, a true query stripping process would eliminate the resort, service line, and service tables if I selected ‘None’ from the prompt. In my “level of detail” solution all of those tables are still present and therefore have an impact on the query performance.

The issue I was trying to solve in this case was related to the number of rows downloaded to the report. The queries ran fast enough that the query performance was not as big of a concern. It was my goal to provide a way to reduce the row counts without losing the flexibility to add more dimension objects to my report on the fly. This technique accomplished that.

Session Code: 804
Title: Don’t Lose Control: Change Management Strategies for Universe Designers
Day and Time: Tuesday, October 05, 2010, 2:45PM – 3:45PM

My final material is subject to change as I see how long each section gets, but here’s a brief overview of the areas I am considering covering.

• Impact of database changes
  What happens to my universe when my database changes? How can I address those issues in a way that causes the least impact on my reporting team?
• Impact of universe changes
  What happens to reports when I change my universe?
• How can I compare two versions of a universe to see what is changing?
  This is the big one… several years ago I started a script that allows me to check two different versions of a universe and see what is different. I have been using the script for a while now and it does most of what I need, but it isn’t complete. I plan to (hope to) get the code finished by October so I can distribute it at the conference, and of course here on my blog. The code will be released under the GNU GPL (open source license). Details of that license and what someone can (or cannot) do are found on the GNU GPL home page. I will demonstrate the code and how it works under various change scenarios during my presentation… assuming I get it all working the way I want it to, of course.

These were the ideas I had in mind when I submitted the abstract. Time will tell which of them make it into the final presentation. Anything that doesn’t make it will very likely show up here as blog posts.

At some point, anyway. I mean I only started working on my universe change script in 2007…

]]> http://www.dagira.com/2010/07/28/change-management-strategies-for-universe-developers/feed/ 4 http://www.dagira.com/2010/07/27/ipad-keyboard-feature/ http://www.dagira.com/2010/07/27/ipad-keyboard-feature/#comments Wed, 28 Jul 2010 04:16:09 +0000 Dave Rathbun http://www.dagira.com/?p=305 I had to laugh today as I noticed a particular feature on my iPad. I was getting ready to log in to BOB. Here’s the screen shot. If you examine it closely you might see what amused me…



I will give you a few moments to look over the screen shot. Pay careful attention to the keyboard… when you are ready for the answer, click ….

Did you get it? Here’s a closeup view of the area I noticed.



Get it yet?

If not, take a quick look at your own physical keyboard. If it’s like every keyboard I have used, you will see a tiny dot or raised ridge or marker of some kind on the “F” and “J” keys. I am a touch-typist, meaning I put my hands on the “home row” and go to town. I am a fairly quick typist because of years of piano lessons before I even started working on computers. The point of those two little marks is they denote where my index fingers rest on the keyboard if I am in the proper position for typing. Makes sense, right?

But not on an iPad. The keys are all virtual! There’s no way I am going to be able to “feel” on the keyboard and know that my fingers are in the proper location for touch typing… That remains, in fact, one of my biggest complaints about the iPad is the lack of a physical keyboard. I plan to get a bluetooth keyboard when it’s convenient to address this.

But for now, I will chuckle a little bit every time I pull up the virtual keyboard and see the home row clearly (and pointlessly) marked. There is another lesson to be shared here that will be addressed by a blog post from a friend of mine. Once it’s up, I will post a link. Until then, I think I’ll have to write Steve a quick note asking him why we have virtual bumps on a virtual keyboard.

]]> http://www.dagira.com/2010/07/27/ipad-keyboard-feature/feed/ 6 http://www.dagira.com/2010/07/26/dueling-reporting-tools/ http://www.dagira.com/2010/07/26/dueling-reporting-tools/#comments Mon, 26 Jul 2010 14:53:08 +0000 Dave Rathbun http://www.dagira.com/?p=303 I submitted several abstracts for consideration for the fall conference this year. The one I was the most excited about got accepted into the Webi/Deski track, and is called, “Discovery Versus Delivery: A Comparison of Client Reporting Tools.” Here is the formal track designation information.

Session Code: 603
Title: Discovery Versus Delivery: A Comparison of Client Reporting Tools
Day and Time: Tuesday, October 05, 2010, 1:30PM – 2:30PM

What is this talk about? A while back I wrote a post about a very short (single page) slide from SAP that shows how to pick which tool for a particular problem. For this presentation, Brian Durning and I will be doing a demonstration of strengths of two of the most important tools from BusinessObjects: Crystal and Web Intelligence. We plan to cover (and demonstrate!) several scenarios that should help show where the strengths of each tool lie. I will be covering Web Intelligence, and Brian Durning will be covering the Crystal side of the house.

It should be fun.

Related Links

• Related Blog Post: Delivery Versus Discover
]]> http://www.dagira.com/2010/07/26/dueling-reporting-tools/feed/ 4 http://www.dagira.com/2010/07/23/october-conference-season-heats-up/ http://www.dagira.com/2010/07/23/october-conference-season-heats-up/#comments Fri, 23 Jul 2010 19:09:05 +0000 Dave Rathbun http://www.dagira.com/?p=302 A few weeks back I got an acceptance notice for an abstract I had submitted to TechEd this year. TechEd is in Las Vegas and I will be speaking about universe design to SAP folks that want to learn more about how to use BusinessObjects tools outside of their ERP/BW environment. It should be fun.

Earlier today I got notified that I have also been accepted to speak at the ASUG fall conference in Orlando. This is the conference that replace the Insight conferences put on by BusinessObjects these past few years, which were then followed by the two GBN conferences in Dallas. Since GBN has been folded into ASUG now our fall conference is now under their umbrella. If you are a “classic” BusinessObjects customer who wants to continue to see new and exciting things that you can do with your tools, this is the conference to attend. There will be some great stuff coming from SAP employees about XI 4 that you won’t want to miss, as well as (I am sure) a number of “can’t miss” sessions related to XI 3.x (and perhaps even earlier versions) that will help you make the most of your current environment while we all wait for XI 4 to be generally available.

In a few days I will post more details about my abstracts. I look forward to seeing everyone in Las Vegas or Orlando or perhaps even Both.

]]> http://www.dagira.com/2010/07/23/october-conference-season-heats-up/feed/ 5 http://www.dagira.com/2010/07/07/want-to-remove-drill-indicators-use-a-variable/ http://www.dagira.com/2010/07/07/want-to-remove-drill-indicators-use-a-variable/#comments Wed, 07 Jul 2010 12:38:54 +0000 Dave Rathbun http://www.dagira.com/?p=298 In some cases I have created reports that were in drill mode that were not really drillable. For example, I showed a technique a few years back that would let a report developer swap measures out on a chart using a drill filter. It has been working great for quite a few years now. However, as a side effect any dimension on the report blocks that participates in a hierarchy becomes drillable. Sometimes I don’t want that, and there is an easy fix.

Problem Definition

When building a universe there are three types of result objects that I can build: dimensions, details, and measures. All dimensions are by default drillable, and the order of the objects in the class determines the default hierarchy. (That’s one of many reasons why trying to order objects in alphabetical order just does not work.) I can eliminate the default hierarchies (and allow my objects to be ordered alphabetically if I really need to) by creating custom hierarchies. That’s a subject for another blog post.

What I want to talk about today is how to eliminate all drill opportunities on a report, no matter whether I have used default or custom hierarchies. I want to be able to include dimension objects, put my report into drill mode, but not be able to drill on every possible dimension. Here’s how to do that.

Demote the Dimension Using a Variable

When I use a dimension object in my block and that dimension participates in a drill hierarchy and my report is in drill mode I will see the drill indicators. In the Web Intelligence Rich Client they look like this:



The “up arrow” implies that I can drill up. The underline format on the resort names implies that I can drill down. Here is a very simple fix if I don’t want either of those interface elements to appear. Create a variable that references the dimension, and use the variable instead. For this example, I will create a variable called Resort Name that has the simple formula =[Resort]. Once I do that, and I replace the original Resort object with my new variable, here is what the block looks like when the report is in drill mode:



Problem solved. The reason this works is that variables – even simple ones like this – are not drillable. Only dimension objects that come from the universe can be drilled. By converting a dimension to a variable it will no longer show any drill indicators when a report block is in drill mode. Why would I want to do that? As mentioned in the first paragraph of this post, I showed a trick several years ago that allows me to switch measures on a block by changing a selection in a drill filter. In order to do that, the report has to be in drill mode. I will write up that one soon, or you can read about it in Tales of a Universe Ninja, Part II, found on my conference presentations page.

]]> http://www.dagira.com/2010/07/07/want-to-remove-drill-indicators-use-a-variable/feed/ 5 http://www.dagira.com/2010/07/02/universe-models-for-recursive-data-part-iii-alias-versus-flattened/ http://www.dagira.com/2010/07/02/universe-models-for-recursive-data-part-iii-alias-versus-flattened/#comments Fri, 02 Jul 2010 11:45:03 +0000 Dave Rathbun http://www.dagira.com/?p=284 This is the third of several posts that will review my presentation “Universe Models For Recursive Data” which was originally presented at the 2009 GBN conference, then at the North Texas / Oklahoma ASUG chapter meeting, and finally at the Mastering BusinessObjects conference in Melbourne. As with my other presentations there is a PDF file that can be downloaded from my conference presentations page. The first post introduced the concepts of recursive (as opposed to hierarchical) data and provided a couple of examples. The second post reviewed some of the different design challenges that I have seen in working with recursive data models. In this post I will introduce four different possible solutions and present a scorecard for each, showing how well it solves the issues presented in the prior post in this series. Links to both prior posts are presented at the end of this entry. I have also included Oracle SQL scripts that can be used to create and populate the tables used in this post.

This post will cover slides 22 through 30 from the presentation and will describe the first two solutions (one with two variations) outlined in the presentation.

Solution Options

The four different solutions that I included in my presentation were: Universe aliases, Flattened structures (column or snowflake), Ancestor Model, and Depth First Tree Traversal. All of them work fine on a clean recursive hierarchy. Each of them partially works for at least some of the other challenges. Some of them present unique challenges (extra disk space requirements or lack of native drilling functionality) that will also be addressed. I am presenting the solutions in increasing order of complexity. This post will cover aliases and flattened structures (both versions). In the next post I plan to cover the ancestor model, and finally I will cover the depth first tree traversal in its own post.

Universe Aliases

This solution is the only one that can be completely self-contained within the universe. No DBA or ETL work is required. There are any number of ways to create an alias. I can:

• Right-click on a table and select Insert Alias
• Select an existing table in my structure, then select Insert + Alias from the menu
• Open my table browser and insert an existing table. An alias will automatically be created for me.
• Select an existing table in my structure and click the “Insert Alias” toolbar button

… and there are other ways to get aliases in my universe, especially if I have loops to resolve. The bottom line is that the process is quite simple.

Here’s what an alias looks like after it has been created and joined to an existing table in my structure.



The join can be a bit tricky. In this case, the employee row MGR_ID is joined to the manager row EMP_ID in order to make the relationship work. It might help to look at the raw data again from an earlier post.



See how the recursive relationship is going to work after establishing this join? Field works for Ferrerez, and Ferrerez works for Noakes. Who does Noakes work for? His MGR_ID column is empty (NULL) implying that he does not have a manager. He owns the company.

Pros of Alias Solution

The primary advantage of this solution is that it is completely self-contained in the universe. No DBA or ETL work is required. That’s about it.

Cons of Alias Solution

There are several cons to this solution. It does not represent lateral relationships at all. I have to use outer joins in order to preserve those rows with missing keys (Noakes in this example). Both of these are important, but the most substantial drawback to this solution is that the depth is determined by the number of aliases that the universe designer creates. In the image shown above there is only one link: from manager to direct employee. How can I — in one step — determine my indirect reports? With only one level of alias, I can only report one level of my hierarchy. How many can I report with this structure?



With that structure I now have two outer joins, but I can report on three levels instead of just two.

How many alias levels do I create? Generally when I have seen this solution used (or used it myself) we resort to asking how many levels are required and then creating some number above that. If I need five, I will create seven. If I need seven, I will create ten.

That means, of course, if I have created ten levels and all of a sudden we have twelve I have to update my universe. That’s not a problem (as long as I keep up with things) but it’s certainly not desirable.

Alias Scorecard

Here’s the scorecard for the alias solution for each of the four scenarios I outlined earlier.



Aliases are the easiest solution to implement but they don’t score well. Let’s move on to the next solution.

Flattened Structure – Single Table Columns

The next solution involves running either a SQL script or some form of ETL. I need to take the recursive table relationship and flatten it out much like I did with aliases, but this time in the database itself. The net result is that I will take data going down in rows:



and pivot it into columns in a table.



The net result is all of my recursion is done during the script process and I end up with one table that contains everything (or every person in my case) stored at their specific level in the hierarchy. It easily allows me to drill because it creates a very natural hierarchy.

Pros of Flattened Table Solution

It handles unbalanced hierarchies much better than aliases because missing lower nodes are simply NULL in the table. That’s fine. This solution can also handle ragged hierarchies with a proper “plug node” strategy. If I have a lower level value (Divisional Director) that reports directly to the president (top level) then level 2 (Vice President) will be empty. I need to fill something in so I can drill properly. More important, that plug node has to tell me what the path is or else I cannot drill up properly. Suppose I had a director named Smith who reported directly to Noakes. The first column in my table would include Noakes. The third column would include Smith. The second column (the missing value due to the raggedness of my data) would contain Smith VP Not Assigned or something like that.

Flattened tables cannot handle lateral hierarchies at all because I can’t store two values in a single column.

Cons of Flattened Table Solution

As already mentioned, this solution cannot handle lateral hierarchies at all. It also requires DBA or ETL work if the number of hierarchy levels changes. My column names should reflect the position (node type) in the hierarchy. That’s not a problem unless my hierarchy levels change, then I might want to update my structures.

But by far the most critical issue with this solution is the fact that it requires DBA or ETL work if my levels ever change. Much like aliases when I have seen this solution implemented I generally see extra columns at the end of my table just to allow for future expansion.

Flattened Table Scorecard

Here is my scorecard for the Flattened Table solution.



Flattened Structure – Snowflake Tables

One thing that I noticed about the data for the flattened structure is that I repeat a lot of values. For example, Noakes is the “level 1 mgr” for every person in the company. It might seem to be more efficient to use a structure like this:



This would reduce my overall storage requirements because I would end up with a single row for the highest level table.

However, it also reintroduces the need for outer joins, which the initial flattened structure avoided.

Pros of Flattened Snowflake Solution

Because the tables get smaller as I get further up the tree (ultimately to a single-row table in my simple example) my overall storage requirement should be smaller as well. If I only need the top one or two levels, my queries should be very efficient. Finally, I think it would be easier to maintain as well. If a new level appears, I add a new table to my chain with the proper restrictions on the ETL for proper table population.

Cons of Flattened Snowflake Solution

Each of the solutions defined so far suffers from some form of this issue: I have to define a table (or column) for every possible level of my hierarchy. If I do not know what the total number of levels will be, I can try to anticipate and create extra tables to support future expansion. But that is not the best solution. Because these tables are maintained in the database, I have to talk to my DBA or ETL team when changes are required. Because the tables are joined I have to consider whether to use outer join to preserve depth on unbalanced hierarchies. And finally, the “plug node” strategy I outlined earlier becomes a “plug row” strategy in this case, and that’s substantially more complicated.

Flattened Snowflake Scorecard

Here is the scorecard for the flattened snowflake solution. In my opinion, it’s a slightly worse solution than the flattened table solution simply because of the join issue and the plug row concern.



Next Time

The solutions covered in this post are the least complex and therefore offer the least flexibility. They are easy to set up; in the case of aliases the entire solution can be built within the universe designer application. All of the other solutions require some sort of database scripting. In the next post I will talk about the ancestor model and how we used it at a manufacturing client. It has some definite advantages, and it handles just about all of the different challenges I have outlined. I don’t have to worry about plug nodes, and it handles both ragged and unbalanced hierarchies quite well. However it has an impact on disk usage and it can’t be drilled using the native functionality provided by BusinessObjects. Do the pros outweigh the cons? Come back soon and see for yourself.

Related Links

• Universe Models for Recursive Data Part I: Introduction
• Universe Models for Recursive Data Part II: Design Challenges

Supplemental Material
Scripts to create and populate the basic HR table used for this presentation.

• Create table

  create table employee
  (emp_id number(5) not null
  ,emp_lastname varchar(20)
  ,emp_firstname varchar(15)
  ,emp_dob date
  ,emp_address varchar(40)
  ,emp_area_code varchar(7)
  ,emp_town varchar(15)
  ,emp_phone varchar(18)
  ,showroom_id number(4)
  ,emp_start date
  ,emp_mgr_id number(5)
  ,emp_sex varchar(1)
  ,job_id number(4));
  
  alter table employee add constraint emp_pk primary key (emp_id);
  create index emp_dept on employee(dept_id);
  create index emp_showroom on employee(showroom_id);
  create index emp_mgr on employee(emp_mgr_id);
  

• Populate table

  insert into employee (emp_id, emp_lastname, emp_firstname, emp_dob, emp_address, emp_area_code, emp_town, emp_phone, emp_start, emp_mgr_id, emp_sex)
  values (101, 'Noakes', 'Nicholas', '12-MAR-48', '2356, Melrose Street', '30190', 'San Jose', '12-00-00-01', '01-JAN-91', NULL, 'M');
  
  insert into employee (emp_id, emp_lastname, emp_firstname, emp_dob, emp_address, emp_area_code, emp_town, emp_phone, emp_start, emp_mgr_id, emp_sex)
  values (102, 'Ferrerez', 'Ferdinand', '10-FEB-64', '25 Arcadia Avenue', '75897', 'Los Angeles', '22-55-56-32', '30-MAR-96', 101, 'M');
  
  insert into employee (emp_id, emp_lastname, emp_firstname, emp_dob, emp_address, emp_area_code, emp_town, emp_phone, emp_start, emp_mgr_id, emp_sex)
  values (103, 'Field', 'Felicity', '15-DEC-60', '12 Brasilia Street', '12014', 'Santa Barabara', '14-46-54-22', '26-MAR-95', 102, 'F');
  
  insert into employee (emp_id, emp_lastname, emp_firstname, emp_dob, emp_address, emp_area_code, emp_town, emp_phone, emp_start, emp_mgr_id, emp_sex)
  values (104, 'Fraser', 'Frank', '13-MAR-67', '45 Seaside Avenue', '75016', 'Los Angeles', '22-55-18-33', '13-DEC-91', 101, 'M');
  
  insert into employee (emp_id, emp_lastname, emp_firstname, emp_dob, emp_address, emp_area_code, emp_town, emp_phone, emp_start, emp_mgr_id, emp_sex)
  values (105, 'Snow', 'Sara', '03-OCT-65', 'Square Woodstock', '18000', 'San Jose', '14-34-34-30', '01-MAY-93', 101, 'F');
  
  insert into employee (emp_id, emp_lastname, emp_firstname, emp_dob, emp_address, emp_area_code, emp_town, emp_phone, emp_start, emp_mgr_id, emp_sex)
  values (106, 'Speed', 'Sonya', '03-DEC-70', '5, The Vale', '22000', 'San Jose', '14-32-39-43', '04-JUL-96', 105, 'F');
  
  insert into employee (emp_id, emp_lastname, emp_firstname, emp_dob, emp_address, emp_area_code, emp_town, emp_phone, emp_start, emp_mgr_id, emp_sex)
  values (107, 'Spencer', 'Steve', '01-NOV-64', 'Square Osaka', '33010', 'Los Angeles', '22-24-25-89', '16-APR-91', 105, 'M');
  
  insert into employee (emp_id, emp_lastname, emp_firstname, emp_dob, emp_address, emp_area_code, emp_town, emp_phone, emp_start, emp_mgr_id, emp_sex)
  values (108, 'Helen', 'Harrison', '01-AUG-66', 'Via Firenze', '38200', 'Los Angeles', '22-34-31-11', '13-MAY-94', 101, 'F');
  
  insert into employee (emp_id, emp_lastname, emp_firstname, emp_dob, emp_address, emp_area_code, emp_town, emp_phone, emp_start, emp_mgr_id, emp_sex)
  values (109, 'Thomas', 'Tom', '01-DEC-68', '11 Over Way', '24000', 'San Jose', '22-45-67-45', '20-DEC-95', 101, 'M');
  
  insert into employee (emp_id, emp_lastname, emp_firstname, emp_dob, emp_address, emp_area_code, emp_town, emp_phone, emp_start, emp_mgr_id, emp_sex)
  values (110, 'Thatcher', 'Terry', '03-OCT-50', 'Stars Parkway', '21000', 'San Jose', '12-11-11-09', '06-DEC-92', 109, 'M');
  
  insert into employee (emp_id, emp_lastname, emp_firstname, emp_dob, emp_address, emp_area_code, emp_town, emp_phone, emp_start, emp_mgr_id, emp_sex)
  values (111, 'Davis', 'Diana', '12-AUG-64', 'Rue Opera Sauvage', '92100', 'Los Angeles', '14-54-11-10', '22-SEP-92', 101, 'F');
  
  insert into employee (emp_id, emp_lastname, emp_firstname, emp_dob, emp_address, emp_area_code, emp_town, emp_phone, emp_start, emp_mgr_id, emp_sex)
  values (201, 'Pickworth', 'Paul', '12-FEB-51', '23 Las palmas road', '00316', 'New York', '12-24-26-44', '12-JAN-93', 101, 'M');
  
  insert into employee (emp_id, emp_lastname, emp_firstname, emp_dob, emp_address, emp_area_code, emp_town, emp_phone, emp_start, emp_mgr_id, emp_sex)
  values (202, 'Forest', 'Florence', '10-OCT-32', 'Rue des Lombards', '75100', 'New York', '22-54-11-10', '23-DEC-94', 201, 'F');
  
  insert into employee (emp_id, emp_lastname, emp_firstname, emp_dob, emp_address, emp_area_code, emp_town, emp_phone, emp_start, emp_mgr_id, emp_sex)
  values (203, 'Brown', 'Bella', '12-APR-59', 'Hollywood Blv', '36020', 'New York', '22-36-25-50', '03-FEB-92', 202, 'F');
  
  insert into employee (emp_id, emp_lastname, emp_firstname, emp_dob, emp_address, emp_area_code, emp_town, emp_phone, emp_start, emp_mgr_id, emp_sex)
  values (204, 'Porter', 'Pete', '15-NOV-57', 'Avd Torre De Embarra', '34100', 'New York', '14-44-11-66', '13-APR-92', 201, 'M');
  
  insert into employee (emp_id, emp_lastname, emp_firstname, emp_dob, emp_address, emp_area_code, emp_town, emp_phone, emp_start, emp_mgr_id, emp_sex)
  values (205, 'Irving', 'Ira', '12-FEB-64', '44 Beach avenue', '13000', 'New York', '12-56-55-20', '18-JUN-95', 204, 'M');
  
  insert into employee (emp_id, emp_lastname, emp_firstname, emp_dob, emp_address, emp_area_code, emp_town, emp_phone, emp_start, emp_mgr_id, emp_sex)
  values (206, 'Bailey', 'Ben', '12-JUN-57', '4 Palisades Drive', '75090', 'Long Island', '12-33-51-29', '01-DEC-90', 204, 'M');
  
  insert into employee (emp_id, emp_lastname, emp_firstname, emp_dob, emp_address, emp_area_code, emp_town, emp_phone, emp_start, emp_mgr_id, emp_sex)
  values (207, 'Duckworth', 'Dave', '09-SEP-66', 'Rue du grand temps', '75018', 'New York', '12-85-01-61', '04-NOV-93', 201, 'M');
  
  insert into employee (emp_id, emp_lastname, emp_firstname, emp_dob, emp_address, emp_area_code, emp_town, emp_phone, emp_start, emp_mgr_id, emp_sex)
  values (208, 'Ince', 'Ian', '10-AUG-53', 'Sunset Blvd', '31061', 'New York', '22-52-22-00', '04-DEC-95', 207, 'M');
  
  insert into employee (emp_id, emp_lastname, emp_firstname, emp_dob, emp_address, emp_area_code, emp_town, emp_phone, emp_start, emp_mgr_id, emp_sex)
  values (209, 'Hilary', 'Hibbs', '01-FEB-60', 'Sand Hill Road', '92800', 'New York', '12-54-11-10', '08-JUN-95', 202, 'F');
  
  insert into employee (emp_id, emp_lastname, emp_firstname, emp_dob, emp_address, emp_area_code, emp_town, emp_phone, emp_start, emp_mgr_id, emp_sex)
  values (301, 'Dagmar', 'Davinda', '12-APR-58', '12, The Crescent', 'SL1 1HG', 'Slough', '01628-764234', '24-JUN-95', 101, 'F');
  
  insert into employee (emp_id, emp_lastname, emp_firstname, emp_dob, emp_address, emp_area_code, emp_town, emp_phone, emp_start, emp_mgr_id, emp_sex)
  values (302, 'Presley', 'Percy', '30-OCT-62', '1 Jubilee Close', 'SL5 23F', 'Maidenhead', '01628-834582', '15-JUL-95', 301, 'M');
  
  insert into employee (emp_id, emp_lastname, emp_firstname, emp_dob, emp_address, emp_area_code, emp_town, emp_phone, emp_start, emp_mgr_id, emp_sex)
  values (303, 'Perry', 'Philippa', '24-FEB-71', '23 Rice Hill', 'SL3 12S', 'Maidenhead', '01628-567231', '28-SEP-96', 302, 'F');
  
  insert into employee (emp_id, emp_lastname, emp_firstname, emp_dob, emp_address, emp_area_code, emp_town, emp_phone, emp_start, emp_mgr_id, emp_sex)
  values (304, 'Hubert', 'Henri', '13-DEC-69', '5 Grand Lane', 'SL3 12S', 'Maidenhead', '01628-243535', '17-APR-96', 302, 'M');
  
  insert into employee (emp_id, emp_lastname, emp_firstname, emp_dob, emp_address, emp_area_code, emp_town, emp_phone, emp_start, emp_mgr_id, emp_sex)
  values (305, 'Adamson', 'Anita', '12-OCT-69', '24 Loose Lane', 'SL4 23D', 'Cookham', '01628-782364', '15-FEB-96', 301, 'F');
  
  insert into employee (emp_id, emp_lastname, emp_firstname, emp_dob, emp_address, emp_area_code, emp_town, emp_phone, emp_start, emp_mgr_id, emp_sex)
  values (306, 'Beaver', 'Bertie', '12-MAR-72', '223 Grange Hill', 'SL2 67E', 'Windsor', '01628-187632', '13-JAN-96', 305, 'M');
  
  insert into employee (emp_id, emp_lastname, emp_firstname, emp_dob, emp_address, emp_area_code, emp_town, emp_phone, emp_start, emp_mgr_id, emp_sex)
  values (307, 'Motson', 'Mervin', '22-DEC-74', '67 Blows Down', 'SL5 45G', 'Cookham', '01628-198371', '17-JUN-96', 305, 'M');
  

Scripts to create and populate the flattened version of the HR table, Oracle syntax

• Create flattened table

  create table emp_flat
  (emp_lvl_1 varchar2(20)
  ,emp_lvl_2 varchar2(20)
  ,emp_lvl_3 varchar2(20)
  ,emp_lvl_4 varchar2(20)
  );

• Populate flattened table.
  Only four levels are supported.
  Starting point (Noakes) is hard-coded.

  insert into emp_flag (emp_lvl_1, emp_lvl_2, emp_lvl_3, emp_lvl_4)
  select a.emp_lastname
  ,      b.emp_lastname
  ,      c.emp_lastname
  ,      d.emp_lastname
  from employee a
  ,    employee b
  ,    employee c
  ,    employee d
  where a.emp_id = b.emp_mgr_id(+)
  and b.emp_id = c.emp_mgr_id(+)
  and c.emp_id = d.emp_mgr_id(+)
  and a.emp_id = 101;

• Create Snowflake Tables

  create table emp_level_01
  (emp_id number(5)
  ,emp_lvl_1 varchar2(20));
  
  create table emp_level_02
  (emp_id number(5)
  ,emp_mgr_id number(5)
  ,emp_lvl_2 varchar2(20));
  
  create table emp_level_03
  (emp_id number(5)
  ,emp_mgr_id number(5)
  ,emp_lvl_3 varchar2(20));
  
  create table emp_level_04
  (emp_id number(5)
  ,emp_mgr_id number(5)
  ,emp_lvl_4 varchar2(20));

• Populate snowflake tables
  Only four levels are built, each starting from the prior table.
  Starting point (Noakes) is hard-coded.

  insert into emp_level_01 (emp_id, emp_lvl_1)
  select emp_id, emp_lastname
  from employee
  where emp_id = 101;
  
  insert into emp_level_02 (emp_id, emp_mgr_id, emp_lvl_2)
  select e.emp_id, e.emp_mgr_id, e.emp_lastname
  from employee e, emp_level_01 e1
  where e.emp_mgr_id = e1.emp_id;
  
  insert into emp_level_03 (emp_id, emp_mgr_id, emp_lvl_3)
  select e.emp_id, e.emp_mgr_id, e.emp_lastname
  from employee e, emp_level_02 e2
  where e.emp_mgr_id = e2.emp_id;
  
  insert into emp_level_04 (emp_id, emp_mgr_id, emp_lvl_4)
  select e.emp_id, e.emp_mgr_id, e.emp_lastname
  from employee e, emp_level_03 e3
  where e.emp_mgr_id = e3.emp_id;
  

]]> http://www.dagira.com/2010/07/02/universe-models-for-recursive-data-part-iii-alias-versus-flattened/feed/ 9 http://www.dagira.com/2010/06/25/universe-models-for-recursive-data-part-ii-design-challenges/ http://www.dagira.com/2010/06/25/universe-models-for-recursive-data-part-ii-design-challenges/#comments Sat, 26 Jun 2010 00:38:35 +0000 Dave Rathbun http://www.dagira.com/?p=283 This is the second of several posts that will review my presentation “Universe Models For Recursive Data” which was originally presented at the 2009 GBN conference, then at the North Texas / Oklahoma ASUG chapter meeting, and finally at the Mastering BusinessObjects conference in Melbourne. As with my other presentations there is a PDF file that can be downloaded from my conference presentations page. The first post introduced the concepts of recursive (as opposed to hierarchical) data and provided a couple of examples. In this post I will review some of the different design challenges that I have seen in working with recursive data.

I decided to identify and cover four different examples of recursive data configurations. These included Clean, Unbalanced, Ragged, and Lateral. As I mentioned in the first post, I am going to use some basic human resources (HR) data for my examples. For this post, in order to show samples of each of the four challenges, I am going to represent my recursive data using a tree. The branches of the tree show the relationships between people. The nodes of the tree contain the information about each person. The data might include their name, hire date, and position (title) within the company. In order to properly interact with my recursive data I have to be able to work with both types of information: relationships and node data as well. If you are not sure what I mean, please continue reading, this will make more sense later on.

This post will cover slides 14 through 21 from the presentation and will describe each of the different recursive challenges that I identified.

Clean Hierarchy

In my first example everything is very clean. Each branch of the tree has the same depth. Each branch follows the same path. There are no real challenges encountered in this hierarchy, pictured below.



Imagine that the top of the tree is the company president. The second level (the “B” nodes) represent vice presidents, and the third level (”C” nodes) represents divisional directors. When a hierarchy definition is very rigorous this is the type of tree I expect. For a very simple example let me suggest a product hierarchy instead of an HR chart for the moment. A product hierarchy for a food company might include a Brand Owner, the Brand, the Size, and finally the Flavor. The brand owner could be Beverages-R-Us, the brand could be Super Sports Drinks, the size is two liter bottle, and finally the flavor is Orange. Every product in the system is guaranteed to have all four of these attributes assigned, and they will all be in that exact order.

On the other hand, a human resources hierarchy is rarely as clean. Let me move on to some more interesting examples.

Unbalanced Hierarchy

An unbalanced hierarchy is one where the nodes are at inconsistent depths. Please review the tree shown below.



In the example shown above, there is one node (B1 in this case) that does not have any children while the rest of the nodes at that level (B2) do. If the A node is the company president, and the B nodes are vice presidents, it is entirely possible to have a position (perhaps “VP of Special Projects”) that does not have any additional people that report up to him or her. In that case the tree stops at the VP level and does not go down to the Divisional Director position.

Why is this a challenge? As will be seen later, one of the possible solutions to a recursive data scenario is to pivot the data (flatten) it into different columns. What happens to the missing nodes in this case?

Ragged Hierarchy

In the last example I suggested that there could be a VP of the company that does not have any direct employees. In the case of a Ragged hierarchy it’s slightly different. In this case I might see a Divisional Director who is reporting straight up to the company president without going through a VP.



Note that in the image above I am showing both an unbalanced node (B1) and a ragged node (C2). Let me focus on C2 for a moment. As I already mentioned, there is a relationship from that director position straight up to the president. It does not go through a vice president position. Why is this a challenge? Remember that earlier I mentioned there are two parts that I need to account for: the relationship and the position or node type. In this case the relationship only goes one step, but descends two levels (from president to director). I need to be able to represent both parts properly in whatever data model I come up with.

Lateral Hierarchy

If you have spent any time reviewing company organization charts you may have seen this type of relationship before: I am calling it a lateral (sideways) relationship.



It’s not uncommon to see a lateral relationship from one director to another director (C2 reporting to C1 in this example). This is one of the biggest challenges to most of the design ideas I will be sharing in my next post, because there are two things (people) occupying the same space (node type) on the tree).

Merge / Diverge

As I mentioned toward the beginning of the post that some scenarios are inherently cleaner than others because the relationships all have to exist. Unfortunately, it is quite likely to see a combination of issues. I have even seen challenges where a hierarchy does a merge / diverge relationship such as this:



SAP and other ERP vendors generally allow this sort of hierarchy to be built in order to provide the maximum flexibility to the client company. I have never tried to implement this in BusinessObjects because it simply does not work. There is no clear drill path. Suppose I drill from node B2 to C3, and then from C3 to D2. Now when I drill up, which path do I take? I can drill from D2 up to C3, and then from C3 I can drill up to either node A1 or B2. It’s ambiguous, and therefore our project team decided that we would not attempt to handle this at all. We instituted a business rule (an exception) that would kick out any hierarchy that included this sort of path.

This particular example was dropped from the presentation in the interest of time but I wanted to mention it here.

Combinations

Even without the merge / diverge issue, there are plenty of still challenges. For our project, a typical tree was both ragged and unbalanced. That meant that the solutions we discussed had to be able to handle both. We also had a number of lateral relationships that we needed to address. Our users wanted to be able to enter the tree by node type and drill by level. They wanted to see the entire tree presented as part of a prompt. And they wanted to be able to multi-select from those prompts… for any node at any level.

Next Time

Which solutions work the best? Do any solutions work for all of these different scenarios? My next post in this series will review each of the four solutions I outlined in my presentation and present a scorecard for each.

Related Links

• Universe Models for Recursive Data Part I: Introduction
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