5 Report and Query Customization

Reporting Approaches in Oracle Airlines Data Model

There are two main approaches to creating reports from data in an Oracle Airlines Data Model warehouse:

• Relational Reporting

  With relational reporting, you create reports against the analytical layer entities using the fact tables as the center of the star with the dimension or lookup tables acting as the dimensions of the star. Typically the facts are derived (DWD_) and aggregate (DWA_) tables with DWM_ dimension tables. However in some cases, you may need to use base (DWB_) tables along with reference tables (DWR_ ) or lookup (DWL_) tables to generate more detailed reports.

  • The dimension (DWM_ ) tables typically represent dimensions which contain a business hierarchy and are present in the form of snowflake entities containing a table for each level of the hierarchy. This allows you to attach the appropriate set of reference tables for the multiple subject area and fact entities composed of differing granularity. For example, you can use the time dimension table DWM_CLNDR to query against a DAY level Passenger Name Record (PNR) data such as DWD_PNR.

  • Reference tables (DWR_ ) and lookup (DWL_) tables represent the simpler dimensions comprising single level containing a flat list of values. Typically, most reporting tools add a superficial top level to the dimension. These could be individual tables starting with DWL_ or views (also named DWL_) on DWL_CODE_MASTER that break out different code types into separate dimensions.

• OLAP Reporting

  With OLAP reporting, you access Oracle OLAP cubes using SQL against the dimension and cube (fact) views. Cubes and dimensions are represented using a star schema design. Dimension views form a constellation around the cube (or fact) view. The views of the dimension and cube views are relational views with names ending with _VIEW:

  • OLAP dimension views contain information relating to the whole dimension, including all the levels of the hierarchy logically partitioned on the basis of a level column (identified as level_name). You can think of OLAP dimensions as "flattened" views of snowflake dimension tables. Typically, in Oracle Airlines Data Model, a dimension view is named dimension_hierarchy_VIEW

  • Cube views contain the facts pertaining to the cross-combination of the levels of individual dimensions which are part of the cube definition. Also the join from the cube view and the dimension views are based on the dimension keys along with required dimension level filters. Typically, in Oracle Airlines Data Model, a cube view is named cube_VIEW.

  Although the OLAP views are also modeled as a star schema, there are certain unique features to the OLAP reporting methodology which requires special modeling techniques in Oracle Business Intelligence Suite Enterprise Edition.

  See also:

  The Oracle By Example tutorial, entitled "Using Oracle OLAP 11g With Oracle BI Enterprise Edition". To access the tutorial, open the Oracle Learning Library in your browser by following the instructions in "Oracle Technology Network"; and, then, search for the tutorials by name.

The rest of this chapter explains how to create Oracle Airlines Data Model reports. For examples of Oracle Airlines Data Model reports, see:

• "Writing As Is and As Was Queries"

• "Tutorial: Creating a New Oracle Airlines Data Model Dashboard"

• "Tutorial: Creating a New Oracle Airlines Data Model Report"

• The sample reports provided with Oracle Airlines Data Model that are documented in Oracle Airlines Data Model Reference.

Customizing Oracle Airlines Data Model Sample Reports

Sample reports and dashboards are delivered with Oracle Airlines Data Model. These sample reports illustrate the analytic capabilities provided with Oracle Airlines Data Model -- including the OLAP and data mining capabilities.

The sample reports were developed using Oracle Business Intelligence Suite Enterprise Edition which is a comprehensive suite of enterprise business intelligence products that delivers a full range of analysis and reporting capabilities. Thus, the reports also illustrate the ease with which you can use Oracle Business Intelligence Suite Enterprise Edition Answers and Dashboard presentation tools to create useful reports.

You can use Oracle Business Intelligence Suite Enterprise Edition Answers and Dashboard presentation tools to customize the predefined sample dashboard reports:

• Oracle BI Answers. Provides end user ad hoc capabilities in a pure Web architecture. Users interact with a logical view of the information -- completely hidden from data structure complexity while simultaneously preventing runaway queries. Users can easily create charts, pivot tables, reports, and visually appealing dashboards.

• Oracle BI Interactive Dashboards. Provide any knowledge worker with intuitive, interactive access to information. The end user can be working with live reports, prompts, charts, tables, pivot tables, graphics, and tickers. The user has full capability for drilling, navigating, modifying, and interacting with these results.

Writing Your Own Queries and Reports

The oadm_sys schema defines the relational tables and views in Oracle Airlines Data Model. You can use any SQL reporting tool to query and report on these tables and views.

Oracle Airlines Data Model also supports On Line Analytic Processing (OLAP) reporting using OLAP cubes defined in the oadm_sys schema. You can query and write reports on OLAP cubes by using SQL tools to query the views that are defined for the cubes or by using OLAP tools to directly query the OLAP components.

select DWM_CLNDR.CLNDR_MONTH_CD,DWM_BKG_CLS_TYP.SVC_CLS_DESC,sum(DWA_DLY_BKG_FACT.BKD) as Booking_Count
from DWA_DLY_BKG_FACT,DWM_CLNDR,DWM_BKG_CLS_TYP
where DWM_CLNDR.CLNDR_KEY=DWA_DLY_BKG_FACT.BKG_DT_KEY
and DWM_BKG_CLS_TYP.BKG_CLS_KEY=DWA_DLY_BKG_FACT.BKG_CLS_KEY
and DWM_BKG_CLS_TYP.SVC_CLS_DESC='BUSINESS'
and DWM_CLNDR.CLNDR_MONTH_KEY=20100201 
group by DWM_CLNDR.CLNDR_MONTH_CD, DWM_BKG_CLS_TYP.SVC_CLS_DESC

CLNDR_MONTH_CD            SVC_CLS_DESC            BOOKING_COUNT         
------------------------- --------------------    ----------------------
20100201                  BUSINESS                61                    

Optimizing Star Queries

A typical query in the access layer is a join between the fact table and some number of dimension tables and is often referred to as a star query. In a star query each dimension table is joined to the fact table using a primary key to foreign key join. Normally the dimension tables do not join to each other.

Typically, in this kind of query all of the WHERE clause predicates are on the dimension tables and the fact table. Optimizing this type of query is very straight forward.

In order to optimize, you simply:

• Create a bitmap index on each of the foreign key columns in the fact table or tables

• Set the initialization parameter STAR_TRANSFORMATION_ENABLED to TRUE.

This enables the optimizer feature for star queries which is off by default for backward compatibility.

If your environment meets these two criteria, your star queries should use a powerful optimization technique that rewrites or transforms your SQL called star transformation. Star transformation executes the query in two phases:

1. Retrieves the necessary rows from the fact table (row set).

2. Joins this row set to the dimension tables.

The rows from the fact table are retrieved by using bitmap joins between the bitmap indexes on all of the foreign key columns. The end user never needs to know any of the details of STAR_TRANSFORMATION, as the optimizer automatically chooses STAR_TRANSFORMATION when it is appropriate.

Example 5-2, "Star Transformation" gives the step by step process to use STAR_TRANSFORMATION to optimize a star query.

The following figure shows the typical execution plan for a star query when STAR_TRANSFORMATION has kicked in. The execution plan may not look exactly how you imagined it. You may have noticed that you can see that there is no join back to the customer table after the rows have been successfully retrieved from the Sales table. If you look closely at the select list, you can see that there is not anything actually selected from the Customers table so the optimizer knows not to bother joining back to that dimension table. You may also notice that for some queries even if STAR_TRANSFORMATION does kick in it may not use all of the bitmap indexes on the fact table. The optimizer decides how many of the bitmap indexes are required to retrieve the necessary rows from the fact table. If an additional bitmap index would not improve the selectivity the optimizer does not use it. The only time you see the dimension table that corresponds to the excluded bitmap in the execution plan is during the second phase or the join back phase.

Troubleshooting Oracle Airlines Data Model Report Performance

Take the following actions to identify problems generating a report created using Oracle Business Intelligence Suite Enterprise Edition:

1. In the (Online) Oracle BI Administrator Tool, select Manage, then Security, then Users, and then oadm.

   Ensure that the value for Logging level is 7.

2. Open the Oracle Airlines Data Model Repository, select Manage, and then Cache.

3. In the right-hand pane of the Cache Manager window, select all of the records, then right-click and select Purge.

4. Run the report or query that you want to track using the SQL log.

5. Open the query log file (NQQuery.log) under OracleBI\server\Log.

   The last query SQL is the log of the report you have just run. If an error was returned in your last accessed report, there is an error at the end of this log.

The following examples illustrate how to use these error messages:

• Example 5-3, "Troubleshooting an Oracle Airlines Data Model Report"

• Example 5-4, "Troubleshooting a Report: A Table Does Not Exist"

• Example 5-5, "Troubleshooting a Report: When the Database is Not Connected"

Query Status: Query Failed: [nQSError: 15018] Incorrectly defined logical table source (for fact table Booking Segment Departure Fact) does not contain mapping for [Booking Segment Departure Fact.Confirmed Count, Booking Segment Departure Fact.Belly Cargo Revenue].

Writing As Is and As Was Queries

Two common query techniques are "as is" and "as was" queries:

• Characteristics of an As Is Query

• Characteristics of an As Was Query

• Examples: As Is and As Was Queries

SELECT cust.cust_nm, cust.m_status, SUM(fct.tx) 
FROM taxpaid fct, customer cust, county cnty 
WHERE fct.cust_id = cust.cust_id 
AND cust.cnty_id = cnt.cnt_id
GROUP BY cust.cust_nm, cust.m_status
ORDER BY 1,2,3;

select
   cust.cust_nm, cust.m_status, sum(fct.tax)
from
   TaxPaid fct, 
   (
      select 
         cust_act.cust_id, cust_pit.cust_cd, cust_pit.cust_nm,
         cust_pit.m_status, cust_pit.gender,
         cust_pit.cnty_id, cust_pit.cnty_cd, cust_pit.cnty_nm
      from Customer cust_act
      inner join (
         select
            cust_id, cust_cd, cust_nm,
            m_status, gender,
            cnty_id, cnty_cd, cnty_nm
         from Customer cust_all
         where to_date('15-JAN-2011', 'DD-MON-YYYY') between eff_from and eff_to
      ) cust_pit                   
      on (cust_act.cust_cd = cust_pit.cust_cd)
   ) cust,
   (
      select 
         cnty_act.cnty_id, cnty_pit.cnty_cd, cnty_pit.cnty_nm
      from County cnty_act       
      inner join (
         select
            cnty_id, cnty_cd, cnty_nm
         from County cnty_all
         where to_date('15-JAN-2011', 'DD-MON-YYYY') between eff_from and eff_to
      ) cnty_pit
      on (cnty_act.cnty_cd = cnty_pit.cnty_cd)
   ) cnty                         
where fct.cust_id = cust.cust_id
and cust.cnty_id = cnty.cnty_id
GROUP BY cust.cust_nm, cust.m_status
order by 1,2,3;

SELECT cust.cust_nm, cnty.cnty_nm, SUM(fct.tax)
FROM TaxPaid fct, customer cust, county cnty
WHERE fct.cust_id = cust.cust_id
AND cust.cnty_id = cnty.cnty_ID
GROUP BY cut.cust_nm, cnty.cnty_nm
ORDER BY 1,2,3;

select
   cust.cust_nm, cnty.cnty_nm, sum(fct.tax)
from
  TaxPaid fct,
   (
      select 
         cust_act.cust_id, cust_pit.cust_cd, cust_pit.cust_nm,
         cust_pit.m_status, cust_pit.gender,
         cust_pit.cnty_id, cust_pit.cnty_cd, cust_pit.cnty_nm
      from Customer cust_act
      inner join (
         select
            cust_id, cust_cd, cust_nm,
            m_status, gender,
            cnty_id, cnty_cd, cnty_nm
         from Customer cust_all
         where to_date('15-JAN-2011', 'DD-MON-YYYY') between eff_from and eff_to
      ) cust_pit
      on (cust_act.cust_cd = cust_pit.cust_cd
   ) cust,
   (
      select 
         cnty_act.cnty_id, cnty_pit.cnty_cd, cnty_pit.cnty_nm
      from County cnty_act
      inner join (
         select
            cnty_id, cnty_cd, cnty_nm
         from County cnty_all
         where to_date('15-JAN-2011', 'DD-MON-YYYY') between eff_from and eff_to
      ) cnty_pit
      on (cnty_act.cnty_cd = cnty_pit.cnty_cd)
   ) cnty
where fct.cust_id = cust.cust_id
and cust.cnty_id = cnty.cnty_id
GROUP BY cust.cust_nm, cnty.cnty_nm
order by 1,2,3;

Tutorial: Creating a New Oracle Airlines Data Model Dashboard

This tutorial explains how to create a new dashboard based on dashboards in the Oracle Airlines Data Model webcat included with the sample Oracle Business Intelligence Suite Enterprise Edition reports delivered with Oracle Airlines Data Model.

In this example assume that you want to create a dashboard named "Agent Revenue and Booking Analysis", and put both "Agent Revenue Analysis in USD" and "Agent Performance Analysis PCT CANCEL" into this new dashboard.

To create a dashboard, take the following steps:

1. In the browser, open the login page at http://servername:9704/analytics where servername is the server on which the webcat is installed.

   Login with username of oadm, and provide the password.

2. Select New, and then select Dashboard to create an Oracle Business Intelligence Suite Enterprise Edition dashboard.

   
   

3. Enter a name and description. Then save the dashboard to the Dashboards folder. Click OK.

   
   

4. In the Catalog view, expand the Revenue Analysis folder. You can see the Agent Revenue Analysis in USD Report, drag it from catalog view into the right panel.

   
   

5. In the Catalog view, expand the Revenue Analysis folder. You can see the Agent Performance Analysis PCT CANCEL Report. Drag that report from the Catalog view into the right panel.

   
   

6. You can change the layout of this section to organize the two reports by horizontal or vertical.

   
   

   Note that the page name is still "Page1" so you must change it.

7. To change the page name:

   1. Select the Dashboard.

      
      

   2. In Dashboard Properties window, click Change Name.

      
      

   3. Change the name to "Agent Revenue and Booking Analysis", then click OK.

      
      

8. Click Save on the top of the dashboard. Now you have a new dashboard.

   
   

Tutorial: Creating a New Oracle Airlines Data Model Report

This tutorial explains how to create a new report based on the Oracle Airlines Data Model webcat included with the sample Oracle Business Intelligence Suite Enterprise Edition reports delivered with Oracle Airlines Data Model.

In this example, assume that you want to create a report named "Agent Booking and Flown Revenue" to put both booking count and flown revenue in one report.

To create a this new report, take the following steps:

1. In the browser, open the login page at http://servername:9704/analytics where servername is the server on which the webcat is installed.

   Login with username of oadm, and provide the password.

2. Select New, and then select Analysis to create an Oracle Business Intelligence Suite Enterprise Edition report.

   
   

3. Select Subject Area, then select OADM_OLAP to create a relational report.

   
   

4. Drag and put the dimension and fact columns into the Select Columns panel.

   
   

5. Select the Results tab to view the report.

   
   

6. Select New View to add a chart into report.

   
   

7. Select Save to save this report to the Network Health Analysis folder.