COGNOS – Transformer.

Cognos - Transformer

Transformer Basics. Customizing Dimensions.

Time Dimension. Drill through in Transformer.

Relative time Dimension. Customizing in powercubes.

Multiple Queries. Optimizing Powercubes.

Working with Measures.

Alternative Drill Downs.

Click on the Links to Traverse

Transformer Basics

Data Types

Transformer uses three types of data:– date

– text– numeric.

Date datato define the time periods

Text datato define thecategories

Primarily numeric data to define the measures

Dimensions

45

Quantity Revenue8524

1700480

900

Bill GibbonsJean Proulx10/03/95 France

DallasSale Date Country Branch Sales Rep

09/28/95 U.S.A.Paris

Japan Tokyo Akiko Odi10/16/95

Measures

Basic Components of the Transformer Model

DimensionMap

PowerCubeslist

Measureslist

Querieslist

Components of a Model: Dimensions and Levels

Dimensions:– Broad groupings of descriptive data about a major aspect

of a business.

Levels:– Objects that represent the logical hierarchy of a

dimension.

Dimension

Level

Dimensions and Levels

SaleDate Products Locations

Years

Quarters

Months

Product Line

Product Type

Product

Region

Country

Branch

Dim 1 Dim 2 Dim 3 Dim 4 Dim 5

Sales Rep

Question tobe answered When What Where Who How

Dimensions

Levels

• dimensions represents the highest level of structural data

• levels represent a logical hierarchy.

Additional Dimensions

Exception Dimension

SaleDate

Locations Customer Types

Margin Ranges

Products

Dim 1

Dim 2

Dim 3 Dim 4 Dim 5

DimensionName

Years

Quarters

Months

Product Line

Product Type

Product

Region

Country

Branch

Customer Type

Margin Range

Sales Rep

Levels

2

1

3

4

Manchester, U.K.Manchester, U.K.

The Locations DimensionThe Locations Dimension

EuropeEurope United KingdomUnited KingdomLondon, U.K.London, U.K.

Categories:Categories:

SwedenSweden

SpainSpain

GermanyGermany

BelgiumBelgium

FranceFrance

Level:Level:RegionRegion

Categories:Categories:

Level:Level:CountryCountry

Level:Level:BranchBranch

Category:Category:

Categories

• Categories are the individual data elements that populate a level in a dimension.

Components of a Model: Categories

Categories:– Describe details about an organization and can be

different levels of information within a dimension.

Regular Category:– Based on a source column.

Calculated Category:– Based on a calculation. It becomes part of the cube and

can be applied to any measure in PowerPlay.

Special Category:– Groups a set of regular categories from any level in the

same dimension.

Components of a Model: Measures

Measures:– the numbers that gauge the performance of your

organization.– each measure adds perspective to your data.

Regular Measure:– based on a source column in your data.

Calculated Measure:– based on values calculated in an arithmetic equation.

Category Count Measure:– counts the number of categories and not the number of

rows.

Components of a Model: QueriesComponents of a Model: Queries

Queries: – contain information that describes and locates a source

table or file.

Structure Query:– contains columns that map to levels and categories to

build dimensions– defines the structure of a model– does not include measure values.

Transaction Query:– contains records that provide measure values for

PowerCubes.

What is a Query File?

• Query files:• are created from your production data• supply Transformer with all of the source data required to support the Transformer model.

• Transformer works with data from one or more queries.• Transformer accepts query data in several formats.

The Query File…

During Creating a Query File,– collect and organize the source data based on the model

plan.– data should support the measures and dimension

structures described in your model plan.

Query File

Supports

SourceSourceDataData

Transformer Model Plan

Measures:

RevenueQtyCost

Average CostProfit Margin

Products Locations

Year

Quarter

Month

Product Line

Product Type

Product

Region

Country

Branch

Sales Rep

CustomerTypes

MarginRanges

CustomerType

MarginRange

Time

The Query File…

Transformer Uses the Query File

• to populate the dimensions, levels and categories

• to transform two dimensional data into multidimensional data.

Advantages of Using .IQD Queries

An .IQD query:• improves performance• can be run and updated from Transformer• contains column properties defined in the query and

recognized by Transformer• supports Drill Through to Impromptu.

What is a Model?

• A model is the combination of dimensions, levels, measures and Powercubes list.

• Every cube that you access in PowerPlay is based on a model.

• Transformer can save models in two formats:– .MDL - a model stored as an ASCII file, which is compatible

between versions of Transformer– .PY? - a model stored as a binary file, which is version

specific.

• When a model is loaded into memory, Transformer creates a checkpoint file, .QY?.

Modifying Columns

If structural changes are made to the query, Transformer recognizes that the columns in the query no longer match those currently available in the model.

The Dimension Diagram

• A tool for modifying or customizing a dimension• The only place category information is available.

RootCategory

DrillCategory

Level

CategoryDimensionPane

Creating Model Structures Automatically

AutoDesign automatically creates a preliminary model based on the available query data.

Creating Model Structures Manually

Create model structures by moving columns from the Queries list to the Dimension Map and Measures list.

Generating Categories

Generating categories processes the query data and populates the model with categories for each level in a dimension. After generating categories, count values in a category to assess ratios.

Categorycountvalues

Time Dimension

What is a Time Dimension?

A time dimension contains categories that represent

periods of time.

Types of Time DimensionsTypes of Time Dimensions

There are two types of time dimensions:Regular

– created from a single column– most aspects pre-defined by Transformer– customizable.

Non-Standard– many columns can be used to define non-standard time

dimensions– relationships between the categories are completely defined

by you.

Calendars

Gregorian Calendar

– the default within Transformer

– Year begins property controls the date on which the year starts

– divides data into Year, Quarter, Month.

Lunar Calendar

– comprises 52 weeks

– has Year begins and Week begins on properties that must coincide

– divides data into Lunar Year, Lunar Quarters, Lunar Months, and some common reporting periods (4-4-5, 4-5-4, or 5-4-4 week months).

Date Wizard

Y e a rs?

C a le n d a r? L u n a r?

T yp e o f Y e a rs?

Q u a rte r Y e a rs?

M o nth s?

W e e ks?

D a ys?

F irs t D a y o f Y e a r?

G e n e ra te C a te g o rie s?

S o u rce C o lu m n

N a m e T im e D im e n s ion

Default Path

• An interactive set of dialog boxes prompting you to define a date dimension.

Non-Standard Time Dimensions

• Non-standard time dimensions monitor measure values over time periods that Transformer does not generate by default.

• A non-standard time dimension may be:

– irregular time periods, such as bi-weekly pay periods that do not coincide with the months of the year.

– A time period that does not follow typical intervals, requiring manually constructed time dimensions.

• Non-standard time dimensions are created manually by inserting a new item in the dimension map.

Various Functions with Time Dimension..

• Modifying Date Display Formats.• Regular Time Dimension.• Limiting the Range of Dates.• Setting the Year Begins Date.• Setting up the Current Period.

Modifying Date Display Formats

Regular Time Dimension

• By default, Transformer defines a regular time dimension with the levels Year, Quarter, and Month.

Limiting the Range of Dates

• Limit the range of dates to focus data exploration to a specific time period.

Setting the Year Begins Date

•Sets the month and day in which every year begins.

•The Year begins date can be modified to correspond to the varying start dates of your business.

For the Great Outdoors, the fiscal year begins in

March, and a working weekbegins on Monday.

Setting the Current Period

Transformer reads all of the model date values and assigns the latest one as the current period.

Relative Time Categories

Relative Time Categories

• Relative time categories are special categories that can apply to either Regular time or Non-standard time.

• In Regular time, relative time categories are generated by default.

• In Non-standard time, relative time categories must be created.

Types of Relative Time Categories

There are three types of relative time categories: Single Category

Month, Quarter, Shift

Period To-Date Week To-Date, Month To-Date, Quarter To-Date, Year To-Date, Life To-

Date, Year To-Date Grouped, Quarter To-Date Grouped

N-Period Running Total N-Period Running Total (Grouped), 6 Months running total from last

year, 1 Quarter running total from 6 months ago.Q2

Single

Q1 Q4Q3Q2Q1Q4Q3Q2 Q1

Running Total

Period To-Date

A

Today

`

Grouped Relative Time

A group of relative time periods consisting of:• prior period to-date• period to-date• calculation showing change• calculation showing growth.

Customizing Relative Time Categories

• A custom relative time category is a special category used to track measures over time periods that Transformer does not directly support.

• You can create a custom category using one of the basic approaches:– Single category– Period To-Date Total or Period To-Date Total Grouped – N-Period Running Total or N-Period Running Total Grouped.

Single Category

• A single time period, such as last week.

• Set using:

– Target period - the type of period

– Context period - based on the Target period

– associated offsets.

• For example, for the single category of last week:

– Target period = Week

– Target offset = -1

– Context period = Year

– Context offset = 0.

Period To-Date

• A sequential set of periods, starting at the beginning of a period and ending at another specific period, such as Month To-Date.

• Set using:– To-Date period - specifies the period for to-date totals– Target period - the type of period– Context period - based on the Target Period– associated offsets.

• Period To-Date (Grouped) - creates a range of to-date categories.

N-Period Running Total

• Consists of a number of time periods ending at a specific period

• Set using:– Target period - the type of period– Context period - based on the Target period– associated offsets.

• N-Period Running Total (Grouped) - creates a range of N-period categories.

Future Relative Time Categories

• Create future relative time categories to display future projections in PowerPlay.

• Your data source must contain future time periods and values to create future relative time categories.

It has been projected that, by the year 2000, the Great Outdoors will generate a 4 year total of over $10 million in revenue sales.

Current Revenue

Projected Revenue

Multiple Queries

Multiple Queries in a Transformer Model

Why use Multiple Queries?

• Data from different sources types used in the same model

• Improves performance in the model.• Administration of the data is easier.

Different Types of Queries

Query types are categorized based on the data type in the columns of a query:

Transactional Query

contains transactional data (usually numeric data) representing the measures.

Structural Query

contains structural data (usually text data) used to create dimensions and levels.

Transactional Queries for Measures– create one or more queries to provide measures – place enough structure in the transactional queries to roll

up measures in each dimension.

Structural Queries for Dimensions– create one query for each dimension– place the queries and their columns in the order in which

they will appear on the Dimension Map– placing structural queries first in the Queries list is good

design, but not absolutely necessary.

Multiple Query Guidelines

Planning the Queries

Transaction Query

Product Name

Branch

Cust Site No

Revenue

Cost

Quantity

Branch

Product Details Location

Product Line Region

Product Type Country Customer Name

Product Name Cust Site No

Customer Type

Structure Query

Date

Product Line

Product Type

Product Name

Structure Query

Date

Region

Country

Branch

Structure Query

Date

Customer Type

Customer Name

Cust Site No

Customer Details

There are two steps to associate multiple query columns with dimension levels:

Associate Multiple Query Columns with Levels

Name like columns in different queries to be identical.

Resolve uniqueness issues.

Query 4

Query 1

Query 2

Query 3

By validating a model, you can uncover the following model issues:

– column renaming – level uniqueness issues– context issues– dimensions, levels, and measure relationships.

Validate Multiple Query Models

To identify various dimension and level relationships within a model, use the Query Scope Dimension Map.

Validate Dimension Relationships

Direct(yellow)

Conflicts in the model(red)

Indirect(light yellow)

Unrelated(white)

Declare Levels Unique

• Uniqueness is set from the level property sheet.• Transformer, verifies category uniqueness during

PowerCube generation.

Uniqueness required

Uniqueness declared

Uniqueness applied

1

3

2

– insufficient context / levels that cannot be reached in current drill-down path

– uniqueness issues– an unbalanced category

tree.

Identify Model Issues

The Check Model feature helps you identify:

Unique Move

• Permits a parent category tied to a unique level to change without having to manually restructure the categories in a dimension.

• Use a Unique Move when the level being moved can have its data history move with it.

Working with Measures

Measures

Measures:– the numbers that gauge the performance of your

organization.

Regular Measure:– based on a source column in your data.

Calculated Measure:– based on values calculated in an arithmetic equation.

Category Count Measure:– counts the number of categories and not the number of

rows.

Regular Measures

Regular measures take data directly from a source column available in the query.

Calculated Measures

Calculated measures derive new numeric data for measures when no direct source column is available.

Profit Margin = Revenue - Cost

Category Count Measure

Question:

How did the number of sales reps for specific product lines change over a two year period?

Alternative Drilldowns.

• an alternate drill path through the data in the dimension• new perspectives to explore the data.

What is an Alternate Drill-down?

Primary drill-down path Alternate drill-down path

An alternate drill-down is used to:

– provide more direct navigation to the detailed data in a dimension

– offer more intuitive options for analyzing data on an ad hoc basis

– present different relationships between parts of the data in the source file.

Why Do We Use an Alternate Drill-down?Why Do We Use an Alternate Drill-down?

In PowerPlay

In Transformer

Alternate Drill-down Types

There are three alternate drill path types:

Direct Access

Reorganize a level

Introduce a new level

Direct Access

This type of alternate drill path:• provides direct access to the lower levels of detail in a

dimension• allows users to bypass higher levels to view a broader scope

of data.

Alternatedrill-down path

Primarydrill-down path

Reorganize the Order of the Levels

This type of alternate drill path:• provides a different approach to the primary drill-down path

using the current levels

• is used to reorganize the order of levels in a dimension.

Alternatedrill-down path

Primarydrill-down path

Introduce a New Level

This type of alternate drill path:– provides an additional perspective for the dimension– introduces a level not currently used in the dimension.

Alternatedrill-down path

Primarydrill-down path

In an alternate drill-down path:– the convergence level is the level at which an alternate

drill-down path connects to the primary path– each convergence level must have unique categories.

Convergence Levels in an Alternate Drill-down

Dimension Map

Convergence level

• The Level property sheet is used to define a level as unique.

• Levels may already be defined as unique because of previous relationships analyzed using the Query Scope.

• Transformer warns you if the convergence level had not

previously been declared unique.

Defining the Convergence Level as Unique

An Alternate Drill-Down in the Time Dimension

• Alternate drill-down paths create additional opportunities to view summarized data in PowerPlay.

• Create an alternate drill-down to see time data in different ways to avoid having multiple time dimensions in a model.

• For example, you can view data for the calendar or fiscal year in the same model.

Customizing Dimensions

What are Manual Levels?

Manual Levels:– provide further definition to dimension structures when

source levels are not available– allow you to introduce intermediary levels to add more

detail to the dimension– provide you with the means to group and organize large

numbers of descendant categories.

Creating Manual Levels

Create manual levels in the Dimension Map or Dimension Diagram using the Level property sheet.

Source column remains empty

Populating Manual Levels

• Manual levels contain manual categories you create within the Dimension Diagram.

• Each manual category must have parent-child relationships defined.

Manual/Parent Category

ChildCategory

Manual Level

Special Categories:

– highlight a specific view of the data

– are used to compare trends

– provide a direct path from a root category to a detailed level category

– are viewed in PowerPlay as part of the dimension hierarchy, but are not part of the main dimension rollup.

What are Special Categories?

Special Category

Transformer PowerPlay

Creating a Special Category

• Special categories are created outside the main Dimension Diagram.

• The required categories are connected to the special category to create new links.

Drill Through in transformer

What is Drill Through?

Drill through is the ability to access additional data from an active PowerPlay report.

Drill Through

From a PowerPlay report you can drill through to:– PowerCubes (.MDC)– PowerPlay reports (.PPR)– Impromptu Web Query (.IWQ)– Impromptu reports (.IMR)– Associated files (.DOC, .XLS, .PPT).

Drill Through to Impromptu

Drill Through to Impromptu allows you to access detailed transaction-level data which is not visible in the PowerPlay report.

Impromptu reportDrill ThroughPowerPlay report

How Does Drill Through to Impromptu Work?

The PowerPlay data cell acts as a drill-through filter.

PowerPlay report

Drill Through

Impromptu report

Drill Through Using an .IQD

By default, measures supported by an .IQD query have drill through enabled.

Enable or disabledrill through for Revenue

Lists associated files for drill through

Customizing Powercubes

Omit Dimensions and Exclude Measures

You can customize the PowerCube as a whole or for specific users by:

– omitting dimensions using the Dimensions tab– excluding measures using the Measures tab.

What are PowerCube Passwords?

PowerCube passwords:– restrict user access to PowerCubes or PowerCube groups– are set in the PowerCube property sheet.

What are Dimension Views?

Dimension Views:

– are a subset of a Dimension

– are used to create PowerCubes that contain only selected aspects of the dimension

– allow users to see only the data most relevant to them

– can be customized using the following actions:

– Apex

– Filter

– Cloak

– Summarize

– Suppress.

Suppressed Category

In this Dimension View:

– the category is hidden from a PowerPlay user

– descendants appear as lower-level categories

– rollup of measure values is maintained in the parent category.

Suppressed category

Summarized Category

In this Dimension View:

– the descendants of the selected category are eliminated in PowerPlay

– measure values are rolled up to the parent category.

Summarized category

Cloaked Category

In this Dimension View:

– the selected category and its descendants are eliminated from the PowerPlay view

– the cloaked category is included in the summary values.

Cloaked category

Filtered Category

In this Dimension View:– the selected category and its descendants are excluded from

PowerPlay

– categories and their descendants, as well as the associated data, are omitted from the dimension.

Filtered category

Apexed Category

In this Dimension View:

– only the selected category and its immediate descendants are displayed in PowerPlay.

In Transformer, the diagram displays only the Apex category, Environmental Line.

What is a Cube Group?

A Cube Group:

– is a group of related PowerCubes based on a specific level in a dimension

– contains separate PowerCubes for each category in a selected level.

Canada United States United Kingdom

Customer Number

City

Country Cube Group

Creating Cube Groups

Cube groups are created by defining the properties of a cube group in the PowerCube property sheet.

Optimizing Powercube

Plan for Disk Space

When creating models and PowerCubes, you must ensure that there is sufficient disk space available for:

• source data files• model files (.PYH, .MDL, and .QYH)• temporary files• PowerCubes (.MDC).

Using disk space effectively includes:• periodically saving a .PYH file as .MDL to eliminate

fragmentation• removing unwanted checkpoint files frequently.

Model Space Requirements

To open and use an existing model, the amount of disk space required (in bytes) is:

• 2 x size of current model (.PYH) file.

When creating a new model, use the following formula to provide an estimate of the disk space required (in bytes):

– 2 x (500 x number of categories in the model).

Workfile Space Requirements

When the PowerCube is built a series of work files are created.To estimate the size of the work files:

– 4 bytes per dimension

– 9 bytes per floating point measure– 5 bytes per 32-bit integer measure– 9 additional bytes for any average rollup measures– compute the total size per work file and multiply by

the number of rows of input data– if consolidating, multiply total by 2.

• Build problems can occur if the work file is too small.

I/O and Storage Devices

• In the Preferences dialog box, use the Directories property sheet to determine where the following files will be stored:

• Models• Data Source• PowerCubes• Data temporary files• Model temporary files• Log files.

• This can optimize PowerCube creation by taking advantage of physical disk caching.

Memory

Memory requirements:– Population is memory intensive

• 100,000 categories needs ~50MB of memory.

– PowerCube creation can be faster with more memory• WRITE CACHE SIZE (PPDS WRITE on the server) is set

based on expected cube size.

– Make sure enough computer memory is available to Transformer

• some operating systems are configured to restrict one process from getting all the memory.

Optimize Performance

Many factors can effect performance, such as cube size, processing time in Transformer, and access time in PowerPlay. Consider the following when building a PowerCube:

• cube processing• partitioning• incremental updates• data consolidation.

PowerCube Processing

Several settings can affect how the PowerCube is processed:• optimization method• incremental updates• cube creation and processing• compression• crosstab caching.

Optimization Methods

Auto-Partition:– Enables the Auto-Partition tab, where you can set the

parameters for Transformer to devise a partitioning scheme, and can increase access time in PowerPlay.

Categories:– Minimizes the number of categories in the cube and

can slightly increase processing time.

What is Partitioning?

Partitioning:– divides a large PowerCube into a set of nested “sub-

cubes” called partitions– each partition contains pre-summarized data for

faster access– optimizes run-time performance in PowerPlay by

reducing the number of data records searched to satisfy each information request.

Partitioning Benefits

Partitioning can improve performance for:– Queries on categories at a partitioned level

• already summarized in the previous partition

– Queries on categories in a single low level partition• indexing fewer rows to get answer

– Queries on unpartitioned dimensions• indexing fewer rows to get answer.

Partitioning - The Tradeoff

Amount of partitioning

increased build performance when building PowerCube

increased run-time performance

- +

Partitioned PowerCubes take longer to create than unpartitioned PowerCubes.

Auto-Partition

• There are two controls used by the auto-partitioning algorithm:– desired partition size– number of partitioning passes.

• The partition size determines the tradeoff between cube build time and query response time.

• The number of passes is the number of times Transformer reads the source data for the cube during partitioning.

Auto-Partition

• There are two controls used by the auto-partitioning algorithm:– desired partition size– number of partitioning passes.

• The partition size determines the tradeoff between cube build time and query response time.

• The number of passes is the number of times Transformer reads the source data for the cube during partitioning.

Manual Partitioning

You may want to use manual partitioning when you are:– trying to improve the auto-partitioning strategy– dealing with large cubes or unusually structured data– tuning for the top 10 reports used in the client

environment.

Manual Partitioning

Some tips include:

– select dimensions with the highest number of categories

– select dimensions that contain a large number of levels

– consider the end user’s requirements for exploring versus reporting

– avoid exceeding 2 levels of partitioning

– determine the drill-down ratio from one level to the next

– monitor the Partition Status

– navigate through the PowerCube in PowerPlay Client, testing the strategy.

Optimize Performance

We have looked at how cube processing and partitioning affect performance. Now we will focus on:

– incremental updates– data consolidation.

What is Incremental Update?

Incremental update:

– is the creation of a PowerCube based on data sources containing only incremental data

– adds new data to an existing PowerCube without having to recreate the entire PowerCube

– maintains older data.

Data updates

Factors that Affect PowerCube Size

Factors that affect PowerCube size include:– the number of categories– the number of measures– the number of consolidated rows– the amount of partitioning.

Number of Categories

Inclusion:– specifies the conditions under which Transformer includes

or excludes categories from the PowerCube.– determines the conditions of inclusion for each category in

the Category Viewer.

Number of Measures

You can optimize for:

– the number of measures in a PowerCube

– the storage type for measures.

Exclude Measures

By default, Transformer assigns values to missing measure values in PowerPlay.

Use the PowerCube property sheet to eliminate measures that contain no data values.

Storage Type

The larger the data storage types in your source files, the more space required in the temporary working files. PowerCubes will be larger as well.

Example: a 64-Bit floating point data storage type for a measure requires the same amount of storage space in temporary working files when the measure is processed.

What is Consolidation?

Consolidation:

– Reduces the number of rows in a PowerCube by combining identical non-measure values into a single record.

Before Consolidation:

1998-05-15 Outdoor Products 1,000

1998-05-16 Outdoor Products 1,500

1998-05-16 Outdoor Products 2,000

1998-05-15 GO Sports Line 1,600

1998-05-15 GO Sports Line 1,700

After Consolidation:

1998-05-15 Outdoor Products 1,000

1998-05-15 GO Sports Line 3,300

1998-05-16 Outdoor Products 3,500

Consolidating Your Data

• Consolidate your data using the PowerCube property sheet.

• The default is to consolidate when there is evidence that consolidation would be useful.

Consolidation and Rollup

• You can determine the rollup of duplicate records using the property sheet for each measure.

• By default, Regular rollup is set for duplicate records.

PowerCube Storage

Store PowerCubes in various locations:– individual report users’ computers– LAN server– UNIX server– Web server– relational database.