IBM Cognos Analytics Version 11.0: Data Ing Guide Ca Mdl
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IBM Cognos Analytics
Version 11.0
Data Modeling Guide
IBM
©
Product Information
This document applies to IBM Cognos Analytics version 11.0.0 and may also apply to subsequent releases.
Copyright
Licensed Materials - Property of IBM
© Copyright IBM Corp. 2015, 2017.
US Government Users Restricted Rights – Use, duplication or disclosure restricted by GSA ADP Schedule Contract
with IBM Corp.
IBM, the IBM logo and ibm.com are trademarks or registered trademarks of International Business Machines Corp.,
registered in many jurisdictions worldwide. Other product and service names might be trademarks of IBM or other
companies. A current list of IBM trademarks is available on the Web at “Copyright and trademark information” at
www.ibm.com/legal/copytrade.shtml.
© Copyright IBM Corporation 2015, 2016.
US Government Users Restricted Rights – Use, duplication or disclosure restricted by GSA ADP Schedule Contract
with IBM Corp.
Contents
Chapter 1. Data modeling in Cognos Analytics . . . . . . . . . . . . . . . . . . . 1
Chapter 2. Creating a data module . . . . . . . . . . . . . . . . . . . . . . . . 3
Using
Using
Using
Using
Using
a data module source .
a data server source. .
an uploaded file source
a data set source . . .
a package source . . .
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3
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5
Chapter 3. Creating a simple data module . . . . . . . . . . . . . . . . . . . . . 7
Chapter 4. Refining a data module . . . . . . . . . . . . . . . . . . . . . . . . 9
Relationships . . . . . . . . .
Creating a relationship from scratch
Calculations . . . . . . . . .
Creating basic calculations . . .
Grouping data . . . . . . .
Cleaning data . . . . . . .
Creating custom calculations . .
Creating navigation paths . . . .
Filtering data . . . . . . . . .
Hiding tables and columns . . . .
Validating data modules . . . . .
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Appendix A. Using the expression editor . . . . . . . . . . . . . . . . . . . . . 21
Operators .
( . . .
) . . .
* . . .
/. . .
|| . .
+. . .
- . . .
<. . .
<= . .
<> . .
=. . .
>. . .
>= . .
and . .
between
case . .
contains
distinct .
else . .
end . .
ends with
if . . .
in. . .
is missing
like . .
lookup .
not . .
or . .
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© Copyright IBM Corp. 2015, 2016
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21
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iii
starts with . . . . . .
then . . . . . . . .
when . . . . . . .
Summaries . . . . . .
Statistical functions . . .
average . . . . . . .
count . . . . . . .
maximum . . . . . .
median . . . . . . .
minimum . . . . . .
percentage. . . . . .
percentile . . . . . .
quantile . . . . . .
quartile . . . . . . .
rank . . . . . . . .
tertile . . . . . . .
total . . . . . . . .
Business Date/Time Functions
_add_seconds. . . . .
_add_minutes . . . .
_add_hours . . . . .
_add_days . . . . . .
_add_months . . . . .
_add_years . . . . .
_age . . . . . . . .
current_date . . . . .
current_time . . . . .
current_timestamp . . .
_day_of_week . . . .
_day_of_year . . . . .
_days_between . . . .
_days_to_end_of_month .
_end_of_day . . . . .
_first_of_month . . . .
_from_unixtime . . . .
_hour . . . . . . .
_last_of_month . . . .
_make_timestamp . . .
_minute . . . . . .
_month . . . . . . .
_months_between . . .
_second . . . . . .
_shift_timezone . . . .
_start_of_day . . . . .
_week_of_year . . . .
_timezone_hour . . . .
_timezone_minute . . .
_unix_timestamp . . .
_year . . . . . . .
_years_between . . . .
_ymdint_between . . .
Common Functions. . . .
abs . . . . . . . .
cast . . . . . . . .
ceiling . . . . . . .
char_length . . . . .
coalesce . . . . . .
exp . . . . . . . .
floor . . . . . . . .
ln. . . . . . . . .
lower . . . . . . .
iv
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IBM Cognos Analytics Version 11.0: Data Modeling Guide
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nullif . . . . . .
position . . . . .
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power . . . . . .
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trim . . . . . . .
upper . . . . . .
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Appendix B. About this guide . . . . . . . . . . . . . . . . . . . . . . . . . . 55
Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
Contents
v
vi
IBM Cognos Analytics Version 11.0: Data Modeling Guide
Chapter 1. Data modeling in Cognos Analytics
You can use data modeling in IBM® Cognos® Analytics to fuse together many
sources of data, including relational databases, Hadoop-based technologies,
Microsoft Excel spreadsheets, text files, and so on. Using these sources, a data
module is created that can then be used in reporting and dashboarding.
Star schemas are the ideal database structure for data modules, but transactional
schemas are equally supported.
You can refine a data module by creating calculations, defining filters, referencing
additional tables, updating metadata, and more.
After you save data modules, other users can access them. Save the data module in
a folder that users, groups, and roles have appropriate permissions to access. This
procedure is the same idea as saving a report or dashboard into a folder that
controls who can access it.
Data modules can be used in both dashboards and reports. A dashboard can be
assembled from multiple data modules.
Data modeling in Cognos Analytics does not replace IBM Cognos Framework
Manager, IBM Cognos Cube Designer, or IBM Cognos Transformer, which remain
available for more complex modeling.
Intent-driven modeling
You can use intent-driven modeling to add tables into your data module.
Intent-driven modeling proposes tables to include in the module, based on matches
between the terms you supply and metadata in the underlying sources.
While you are typing in keywords for intent-driven modeling, text from column
and table names in the underlying data sources are retrieved by the Cognos
Analytics software. The intent field has a type-ahead list that suggests terms that
are found in the source metadata.
Intent-driven modeling recognizes the difference between fact tables and
dimension tables by the number of rows, data types, and distribution of values
within columns. When possible, the intent-driven modeling proposal is a star or
snowflake of tables. If an appropriate star or snowflake cannot be determined, then
intent-driven modeling proposes a single table or collection of tables.
© Copyright IBM Corp. 2015, 2016
1
2
IBM Cognos Analytics Version 11.0: Data Modeling Guide
Chapter 2. Creating a data module
You can create data modules by combining inputs from input sources such as other
data modules, data servers, uploaded files, data sets, and packages.
When you create a new data module from the home screen of IBM Cognos
Analytics, you are presented with five possible input sources in Sources. These
sources are described here.
Data modules
Data modules are source objects that contain data from data servers,
uploaded files, or other data modules, and are saved in My content or
Team content.
Data servers
Data servers are databases for which connections exist. For more
information, see Managing IBM Cognos Analytics .
Uploaded files
Uploaded files are data that are stored with the Upload files facility.
Data sets
Data sets contain extracted data from a package or a data module, and are
saved in My content or Team content.
Packages
Packages are created in IBM Cognos Framework Manager and contain
dimensions, query subjects, and other data contained in Cognos
Framework Manager projects. You can use packages as sources for a data
module.
You can combine multiple sources into one data module. After you add a source,
click Add sources (
) in Selected sources to add another source. You can use
a combination of data source types in a data module.
Each type of data source is described in the following topics.
Using a data module source
Saved data modules can be used as data sources for other data modules. When a
data module is used as a source for another data module, parts of that module are
copied into the new data module.
Procedure
1. When you select Data modules in the Sources slide-out panel, you are
presented with a list of data modules to use as input. Check one or more data
modules to use as sources.
2. Click Start or Done in Selected sources to expand the data module into its
component tables.
3. Drag tables into the new data module.
4. Continue to work with your new data module, and then save it.
© Copyright IBM Corp. 2015, 2016
3
5. If the source data module or any of its tables are deleted, then the next time
that you open the new data module, tables that are no longer available have a
red outline in the diagram and missing is listed in the Source fields of the
Properties pane of the table.
6. A table in your new data module that is linked is read-only. You cannot modify
it in the new data module in any way. You can break the link to the source data
module, and modify the table, by clicking Break link in the actions for the
table.
Using a data server source
Data servers are databases for which connections exist and can be used as source
for data modules.
Before you begin
You can use multiple data server sources for your data module.
Data server connections are created in Manage > Data servers. For more
information, see Managing IBM Cognos Analytics .
Procedure
1. When you select Data modules in the Sources slide-out panel, you are
presented with a list of data servers to use as input. Select the data server to
use as a source.
2. The available schemas in the data server are listed. Choose the schema that you
want to use. Only schemas for which metadata is preloaded are displayed. If
you want to use other schemas, click Manage schemas... to load metadata for
other schemas.
3. Click Start or Done in Selected sources to expand the data module into its
component tables.
4. To start populating your data module, type some terms into the Intent
slide-out panel and then click Go.
5. You are presented with a proposed model. Click Add this proposal to create a
data module.
6. You can also drag tables from the data server schema into the data module.
Example
For an example data module created from a data server, see Chapter 3, “Creating a
simple data module,” on page 7
What to do next
If the metadata of your data server schemas changes after you create the data
module, you can refresh the schema metadata. For more information, see the topic
on preloading metadata from a data source connection in the IBM Cognos Analytics
Managing User Guide.
Using an uploaded file source
Uploaded files are data that is stored with the Upload files facility. You can use
these files as sources for a data module.
4
IBM Cognos Analytics Version 11.0: Data Modeling Guide
Before you begin
Supported formats for uploaded files are Microsoft Excel (.xlsx and .xls)
spreadsheets, and text files that contain either comma-separated, tab-separated,
semi colon-separated, or pipe-separated values. Only the first sheet in Microsoft
Excel spreadsheets is uploaded. If you want to upload the data from multiple
sheets in a spreadsheet, save the sheets as separate spreadsheets. Uploaded files
are stored in a columnar format.
To upload a file, click Upload files on the navigation bar in the IBM Cognos
Analytics home screen.
Procedure
1. When you select Uploaded files in the Sources slide-out panel, you are
presented with a list of uploaded files to use as input. Check one or more
uploaded files to use as sources.
2. Click Start or Done in Selected sources to expand the data module into its
component tables.
3. Drag the source uploaded file into your data module to start modeling.
Using a data set source
Data sets contain data that is extracted from a package or a data module, and are
saved in My content or Team content.
About this task
Procedure
1. When you select Data sets in the Sources slide-out panel, you are presented
with a list of data sets to use as input. Check one or more data sets to use as
sources.
2. Click Start or Done in Selected sources to expand the data set into its
component tables and queries.
3. Drag tables or queries into the new data module.
4. If the data in the data sets changes, this change is reflected in your data
module.
Using a package source
Packages are created in IBM Cognos Framework Manager and contain query
subject and other data contained in Cognos Framework Manager projects. You can
use relational packages as sources for a data module.
Before you begin
The package must be relational and use Dynamic Query Mode.
Chapter 2. Creating a data module
5
Procedure
1. When you select Packages in the Sources slide-out panel, you are presented
with a list of packages to use as input. Check one or more packages to use as
sources.
2. Click Start or Done in Selected sources to select the packages.
3. Drag the source packages into your data module to start modeling.
What to do next
When you use a package as your data source, you cannot select individual tables.
You must drag the entire package into your data module. The only actions you can
take are to create relationships between query subjects in the package and query
subjects in the data module.
6
IBM Cognos Analytics Version 11.0: Data Modeling Guide
Chapter 3. Creating a simple data module
You can create a simple data module based on the Great Outdoors Warehouse sales
database that is included in IBM Cognos Analytics extended samples.
Before you begin
Install the Great Outdoors sales data warehouse database and create a connection
to the database. For more information, see Samples for IBM Cognos Analytics.
Procedure
1. In the IBM Cognos Analytics welcome screen, click New → Data module.
2. In Sources, select Data servers.
3. In Data servers, select great_outdoors_warehouse.
4. In great_outdoors_warehouse, select the GOSALESDW schema.
5. In Selected sources, click Done.
6. In the Data module panel, click the intent modeling icon
.
7. In the Intent panel, type sales revenue, and click Go. A proposed model is
displayed in the Intent panel.
8. Click Add Proposal. A basic data module is created.
In the next panel, click the module diagram icon
diagram that is automatically generated.
to see the data module
9. You can now explore the data module. For example, click an item in Data
module, and then click its properties
to view and modify the item
properties. In the diagram view, try changing the Cardinality settings to view
relationships between tables.
10. To save the data module, you have the Save or Save as options
© Copyright IBM Corp. 2015, 2016
.
7
11. To create a report from your data module, click Try It. A new tab opens in
your browser with IBM Cognos Analytics - Reporting open within it. Your
data module is shown in Source Data items.
12. Drag Product Line Code from Sls Product Dim and Quantity from Sls Sales
Fact into the report.
13. Click Run options (
) to select an output format, and then click Run
HTML to run the report and view the output as a web page.
8
IBM Cognos Analytics Version 11.0: Data Modeling Guide
Chapter 4. Refining a data module
The initial data module that you create manually or using intent modeling might
contain data that is not required for your reporting purposes. Your goal is to create
a data module that contains only the data that meets your reporting requirements
and that is properly formatted and presented.
For example, you can delete some tables from your initial data module, or add
different tables. You can also apply different data formatting, filter and group the
data, and change the metadata properties.
You can refine your data module by applying the following modifications:
v Add or delete tables.
v Edit or create new relationships between tables.
v Change column properties.
v Create basic and custom calculations.
v Create navigation paths.
v Define filters.
v Group data.
v Clean the text data.
v Hide tables and columns.
You can initiate these actions from the Data module panel or from the diagram.
When working in a data module, you can use the undo
and redo
actions
in the application bar to revert or restore changes to the data module in the current
editing session. You can undo or redo up to 20 times.
Source panel
The source panel shows the sources of data that were selected when the data
module was created. The types of sources can include other data modules, data
servers, uploaded files, data sets, and packages.
Except for packages, you can expand the specific source to view its tables and
columns. Drag tables onto the data module panel or onto the diagram to add them
to the data module.
Data module panel
The data module tree shows the tables and columns of data that is included in the
data module. This is the primary space for editing the data module.
Click the context menu icon
for the module, table, or column to view its
modeling and editing context menu options. Here you can start joining tables,
creating filters and calculations, or renaming and deleting items.
Click the intent modeling icon
in the panel toolbar to add tables to your data
module. Intent-driven modeling proposes tables to include in the module that are
© Copyright IBM Corp. 2015, 2016
9
based on matches between the terms you supply and metadata in the underlying
sources.
Diagram
The diagram is a graphical representation of table relationships in a data module.
You can use the diagram to examine the relationships, edit the data module, and
view the cardinality information for the relationships.
Right-click a table in the diagram to view the table context menu that can be your
starting point for creating joins or filters, renaming the table, viewing the table
properties, or removing it from the module.
Click any table join to see the join summary information that includes the
matching keys. When you right-click the join line, the context menu appears with
options for editing or deleting the join.
Select the Cardinality check box to show the cardinality of relationships between
different tables in your data module. Move the Degrees of separation slider.
Depending on the slider position, the diagram shows different distances of
relationships between tables.
Data view
You can use the data view to examine the actual data in table columns and rows.
Select a table in the data module tree or in the diagram, and click the grid icon
to open the data view.
Validation view
You can use the validation view to examine errors that are identified by the
validation process.
The messages are displayed after you start the Validate operation anywhere in the
modeling user interface, and the failed validation
icon is displayed for tables,
columns, expressions, or joins where errors are discovered.
Relationships
A relationship joins logically related objects that the users want to combine in a
single query. Relationships exist between two tables.
You can modify or delete relationships, or create new ones so that the data module
properly represents the logical structure of your business. Verify that the
relationships that you require exist in the data module, the cardinality is set
correctly, and referential integrity is enforced.
The diagram provides a graphical view of table relationships in a data module.
You can use the diagram to create, examine, and edit the relationships.
Creating a relationship from scratch
You need to create relationships whenever the required relationships are not
detected by the IBM Cognos software.
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About this task
Relationships can be created between tables from the same source and from
different sources.
The diagram is the most convenient place to view all data module relationships,
and quickly discover the disconnected tables.
Important: The list of possible keys in the relationship editor excludes measures.
This means that if a row in a column was misidentified as a measure, but you
want to use it as an identifier, you will not see the row in the key drop-down list.
You need to examine the data module to confirm that the usage property is correct
on each column in the table.
Procedure
1. In the data module tree or in the diagram, click the table for which you want to
create a relationship, and from the context menu, click Create relationship.
Tip: You can also start creating a relationship using the following methods:
v
In the data module tree or in the diagram, control-click the two tables that
you want to join in a relationship, and click Create relationship.
v On the Relationships tab in the table properties, click Create a relationship.
If the data module does not include the table that you need, you can drag this
table from Selected sources directly onto the diagram.
2. In the relationship editor, specify the second table to include in the relationship,
and then select the matching columns in both tables.
Depending on the method that you used to start the relationship, the second
table might already be added, and you only need to match the columns. You
can include more than one set of matching rows in both tables.
3. Find the matching columns in both tables, and select Match selected columns.
4. Specify the Realtionship Type, Cardinality, and Optimaization options for the
relationship.
5. Click OK.
Results
The new relationship appears on the Relationships tab in the properties page of
the tables that you joined, and in the diagram view.
To view or edit all relationships defined for a table, go to the Relationships tab in
the table properties. Click the relationship link, and make the modifications. To
view a relationship from the diagram, click the join line to open a small graphical
view of the relationship. To edit a relationship from the diagram, right-click the
join line, and click Edit relationship.
To delete a relationship for a table, go to the Relationships tab in the table
properties, and click the remove icon
for the required relationship. To delete
the relationship from the diagram, right-click the line joining the two tables, and
click Remove.
Chapter 4. Refining a data module
11
Calculations
Calculations allow you to answer questions that cannot be answered by the source
columns.
The following product features are based on underlying calculations:
v Basic arithmetic calculations and field concatenations.
v Custom groups.
v Cleaning text data.
v Custom calculations.
Creating basic calculations
You can create basic arithmetic calculations for columns with numeric data types,
and concatenate text values for columns with the text data type.
About this task
The expression for these calculations is predefined and you only need to select it.
For example, you can create a column Revenue by multiplying values for Quantity
and Unit price. You can create a column Name by combining two columns: First
name and Last name.
Procedure
1. To create a simple arithmetic calculation for columns with numeric data types,
use the following steps:
a. In the data module tree, right-click the column for which you want to create
a calculation. For calculations that are based on two columns, use
control-click to select the columns.
b. In the Create calculation box, type a name for the calculation.
c. If the calculation is based on one column, type the number to use in the
calculation.
Tip: The link Use calculation editor opens the expression editor.
d. Click OK.
2. To create a calculation that concatenates values for columns with the text data
type, use the following steps:
a. In the data module tree, control-click the two columns that you want to
combine into a single column. Depending on which column you select first,
its value appears at the beginning of the combined string.
b. Click Create a calculation, and select the suggested option.
c. Type a name for the calculation.
d. Click OK.
Results
In the table that you added the calculation to, you can now see a new calculated
column at the end of the list of columns.
Grouping data
You can organize the column data into custom groups so that the data is easier to
read and analyze.
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IBM Cognos Analytics Version 11.0: Data Modeling Guide
About this task
You can create two types of custom groups depending on the data type of the
column: one group type for columns with numeric data and the second group type
for columns with text data. For example, in the Employee code column you can
group employees into ranges, such as 0-100, 101-200, 200+. In the Manager column,
you can group managers according to their rank, such as First line manager,
Senior manager, and so on.
Procedure
1. In the data module tree, right-click the column that you want to group on, and
click Custom groups.
2. If you selected a numeric column, specify the grouping in the following way:
a. Specify how many groups you want to create.
b. Specify the distribution of the values to be either Equal distribution or
Custom.
c. If you chose Equal distribution, specify the values to be contained in each
group by typing the numbers or clicking the scroll bars.
d. If you chose Custom, you can enter your own range values for the group.
e. Optional: Change the group name.
f. Click Create.
3. If you selected a text column, specify the grouping in the following way:
a. Control-select the values to include in the first group.
b. In the Groups column, click the plus sign.
c. Specify the name for the group, and click OK. The values are added in the
Group members column, and the name of the group appears in the Groups
column. You can add additional values to a group after it is created, and
you can remove values from a group. You can also remove a group.
d. Optional: To add another group, repeat the steps for the first group.
e. Optional: To create a group that contains all of the values that aren't already
included in a group, select the Group remaining and future values in
check box, and specify a name for the group.
f. Click Create.
Results
The custom group column that is based on your selections appears at the end of
the list of columns in the table. A group expression is automatically created in the
expression editor. To view the expression, go to the column properties page and
click on the expression that is shown for the Expression property.
Tip: To complete the action of creating the custom group, you can click Replace
instead of Create. This option will replace the column name in the table with the
group name.
Cleaning data
Data is often messy and inconsistent. You might want to impose some formatting
order on your data so that it's clearer and easier to read.
Chapter 4. Refining a data module
13
About this task
The Clean options that are available for a column depend on the column data
type. Some options can be specified for multiple columns with the same data type,
and some for singular columns only.
The following options are available to clean your data:
Whitespace
Trim leading and trailing whitespace
Select this check box to remove leading and trailing whitespace from
strings.
Convert case to
UPPERCASE, lowercase, Do not change
Use this option to change the case of all characters in the string to either
uppercase or lowercase, or to ensure that the case of each individual
character is unchanged.
Return a substring of characters
Return a string that includes only part of the original string in each value.
For example, an employee code can be stored as CA096670, but you need
only the number 096670 so you use this option to remove the CA part. You
can specify this option for singular columns only.
For the Start value, type a number that represents the position of a
character in the string that will start the substring. Number 1 represents
the first character in the string. For the Length value, specify the number
of characters that will be included in the substring.
NULL values
Specify NULL-handling options for columns with text, numeric, date, and
time data types that allow NULL values. When Cognos Analytics detects
that a column does not allow NULL values, these options are not available
for that column.
The default value for each option depends on the column data type. For
text data, the default is an empty string. For numbers, the default is 0. For
dates, the default is 2000-01-01. For time, the default is 12:00:00. For date
and time (timestamp), the default is 2000-01-01T12:00:00.
The entry field for each option also depends on the column data type. For
text, the entry field accepts alphanumeric characters, for numbers, the
entry field accepts only numeric input. For dates, a date picker is provided
to select the date, and for time, a time picker is provided to select the time.
The following NULL-handling options are available:
Replace this value with NULL
Replaces the text, numbers, date, and time values, as you specify in the
entry field, with NULL.
For example, if you want to use an empty string instead of NULL in a
given column, but your uploaded file sometimes uses the string n/a to
indicate that the value is unknown, you can replace n/a with NULL, and
then choose to replace NULL with the empty string.
Replace NULL values with
Replaces NULL values with text, numbers, date, and time values, as you
specify in the entry field.
For example, for the Middle Name column, you can specify the following
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values to be used for cells where middle name does not exist: n/a, none, or
the default empty string. For the Discount Amount column, you can
specify 0.00 for cells where the amount is unknown.
Procedure
1. In the data module tree, click the context menu icon
click Clean.
for a column, and
Tip: To clean data in multiple columns at once, control-select the columns that
you want to clean. The Clean option is available only if the data type of each
selected column is the same.
2. Specify the options that are applicable for the selected column or columns.
3. Click Clean.
Results
After you complete the Clean operation, the expression editor automatically creates
an expression for the modified column or columns. To view the expression, open
the column properties panel, and click the expression that is shown for the
Expression property.
Creating custom calculations
To create a custom calculation, you must define your own expression using the
expression editor.
About this task
Custom calculations can be created at the data module level or at the table level.
The module-level calculations can reference columns from multiple tables.
For information about the functions that you can use to define your expressions,
see Appendix A, “Using the expression editor,” on page 21.
Procedure
1. In the data module tree, right-click the data module name or a specific table
name, and click Create custom calculation.
2. In the Expression editor panel, define the expression for your calculation, and
specify a name for it.
v To enter a function for your expression, type the first character of the
function name, and select the function from the drop-down list of suggested
functions.
v To add table columns to your expression, drag-and-drop one or more
columns from the data module tree to the expression editor panel. The
column name is added where you place the cursor in the expression editor.
Tip: You can also double-click the column in the data module tree, and the
column name appears in the expression editor.
3. Click Validate to check if the expression is valid.
4. After successful validation, click OK.
Chapter 4. Refining a data module
15
Results
If you created your calculation at the data module level, the calculation is added
after the last table in the data module tree. If you created your calculation at the
table level, the calculation is added at the end of the list of columns in the table. To
view the expression for the calculation, open the calculation properties panel, and
click on the expression that is shown for the Expression property.
Creating navigation paths
A navigation path is a collection of non-measure columns that business users
might associate for data exploration.
When a data module contains navigation paths, the dashboard users can drill
down and back to change the focus of their analysis by moving between levels of
information. The users can drill down from column to column in the navigation
path by either following the order of columns in the navigation path, or by
choosing the column to which they want to proceed.
About this task
You can create a navigation path with columns that are logically related, such as
year, month, quarter, week. You can also create a navigation path with columns
that are not logically related, such as product, customer, state, city.
Columns from different tables can be added to a navigation path. The same
column can be added to multiple navigation paths.
A data module can have multiple navigation paths.
Procedure
1. In the data module panel, start creating a navigation path by using one of the
following methods:
v From the data module context menu
, click Properties, and then click the
Navigation paths tab. Click Add a navigation path. In the Create navigation
path dialog box, drag columns from the data module panel to the navigation
path panel. Change the order of columns as needed. Click OK.
v In the data module tree, select one or more columns, and from the context
menu
of any of the selected columns, click Create navigation path. The
selected columns are listed in the Create navigation path dialog box. Click
OK.
Tip: The default name of the navigation path includes names of the first and
last column in the path. You can change this name.
2. Save the data module to preserve the navigation path.
3. To modify a navigation path, from the data module context menu
, click
Properties, and then click the Navigation paths tab. Click the Edit link for the
path that you want to modify. In the Edit navigation path dialog box, you can
make the following modifications:
v To add different columns, drag the columns from the data module to the
navigation path. You can multi-select columns and drag them all at once.
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v To remove columns, click the remove
icon for the column.
v To change the order of columns, drag them up or down.
v To change the navigation path name, overwrite the existing name.
The default name reacts to the changed order of columns. If you overwrite
the default name, it does no longer change when you modify the group
definition. The name cannot be blank.
Results
The navigation path is added to the data module and is available to users in
dashboards and stories. If you select the option Identify navigation path members
in the data module toolbar, the columns that are members of the navigation
path are underlined.
What to do next
The modeler can modify the navigation path at any time, and re-save the data
module.
To view the navigation path that a column belongs to, from the column context
menu
, click Properties > Navigation paths. Click the navigation path name to
view or modify its definition.
To view all navigation paths in a data module, from the data module context menu
, click Properties > Navigation paths. Click the navigation path name to view
or modify its definition. To delete a navigation path, click the remove
the path.
icon for
Filtering data
A filter specifies the conditions that rows must meet to be retrieved from a table.
About this task
The filter is based on a specific column in a table, but it affects the whole table.
Also, only rows that meet the filter criteria are retrieved from other tables.
You can create filters at the table level, which allows you to add multiple filters at
once, or at the column level.
Procedure
1. In the data module tree or in the diagram, locate the table for which you want
to create filters.
2. Expand the table in the data module panel, and from the column context menu,
click Filter.
Tip: You can also right-click the table in the diagram, and click Manage filters
from there.
3. Select the filter values in the following way:
a. If the column data type is integer, you have two options to specify the
values: Range and Individual items. When you choose Range, use the
Chapter 4. Refining a data module
17
slider to specify the value ranges. When you choose Individual items, select
the check boxes associated with the values.
b. For columns with numeric data types other than integer, use the slider to
specify the range values.
c. For columns with date and time (timestamp) data types, specify a range of
values before, after, or between the selected date and time, or select
individual values.
d. For columns with text data types, select the check boxes associated with the
values.
4. Optional: To select values that are outside the range that you specified, click
Invert.
5. Click OK.
Results
After you create a filter, the filter icon
data module panel and in the diagram.
is added for the table and column in the
What to do next
To view, edit, or remove the filters defined for a table, select the Manage filters
context menu option for the table, and click the Filters tab in the table properties.
To edit the filter, click its expression, make the modifications, and click OK. To
remove a filter from the table, select the remove icon
for the filter.
Tip: To edit a filter on a single column, from the column context menu in the data
module panel, click Filter to open the filter definition.
Hiding tables and columns
You can hide a table or column in a data module. The hidden tables or columns
remain visible in the modeling interface, but they are not visible in the reporting
and dashboarding interfaces. The hidden items are fully functional in the product.
About this task
Use this feature to provide an uncluttered view of metadata for the report and
dashboard users. For example, when you hide columns that are referenced in a
calculation, the metadata tree in the reporting and dashboarding interfaces shows
only the calculation column, but not the referenced columns. When you hide the
identifier columns used as keys for joins, the keys are not exposed in the
dashboarding and reporting interfaces, but the joins are functional in all interfaces.
Procedure
1. In the data module tree, click the context menu icon
and click Hide.
for a table or column,
You can also select multiple tables or columns to hide them at once.
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Tip: To un-hide the items, click the context menu icon for the hidden table or
column, and click Show.
2. Save the data module.
Results
The labels on the hidden tables and columns are grayed out in the data module
tree and in the diagram. Also, on the General tab of the table or column
properties, the check box This item is hidden from users is selected.
The hidden tables and columns are not visible in the reporting and dashboarding
interfaces.
Validating data modules
Use the validation feature to check for invalid object references within a data
module.
About this task
Validation identifies the following errors:
v A table or column that a data module is based on no longer exists in the source.
v A calculation expression is invalid.
v A filter references a column that no longer exists in the data module.
v A table or column that is referenced in a join no longer exists in the data
module.
Errors in the data module are identified by the failed validation icon
descriptions of errors are shown in the validation tray when it is open
. The
.
Procedure
1. In the data module tree, click the data module context menu icon
click Validate
, and
If errors are identified, the failed validation icon
is displayed in the data
module tree, in the diagram, and in the properties panel, next to the column or
expression where the error exists. The descriptions of errors are displayed in
the validation tray.
Tip: To open or close the validation tray, click its icon
.
2. Click the failed validation
icon for a module, column, expression, or join to
view a pop-up box that informs you of the number of errors for the selected
item. Double-click the failed validation icon
error details.
in the pop-up box to view the
Chapter 4. Refining a data module
19
Results
Using the validation messages, try to resolve the errors. You can save a data
module with validation errors in it.
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Appendix A. Using the expression editor
An expression is any combination of operators, constants, functions, and other
components that evaluates to a single value. You build expressions to create
calculation and filter definitions. A calculation is an expression that you use to
create a new value from existing values that are contained within a data item. A
filter is an expression that you use to retrieve a specific subset of records.
Operators
Operators specify what happens to the values on either side of the operator.
Operators are similar to functions, in that they manipulate data items and return a
result.
(
Identifies the beginning of an expression.
Syntax
( expression )
)
Identifies the end of an expression.
Syntax
( expression )
*
Multiplies two numeric values.
Syntax
value1 * value2
/
Divides two numeric values.
Syntax
value1 / value2
||
Concatenates, or joins, strings.
Syntax
string1 || string2
+
Adds two numeric values.
Syntax
value1 + value2
© Copyright IBM Corp. 2015, 2016
21
Subtracts two numeric values or negates a numeric value.
Syntax
value1 - value2
or
- value
<
Compares the values that are represented by "value1" against "value2" and
retrieves the values that are less than "value2".
Syntax
value1 < value2
<=
Compares the values that are represented by "value1" against "value2" and
retrieves the values that are less than or equal to "value2".
Syntax
value1 <= value2
<>
Compares the values that are represented by "value1" against "value2" and
retrieves the values that are not equal to "value2".
Syntax
value1 <> value2
=
Compares the values that are represented by "value1" against "value2" and
retrieves the values that are equal to "value2".
Syntax
value1 = value2
>
Compares the values that are represented by "value1" against "value2" and
retrieves the values that are greater than "value2".
Syntax
value1 > value2
>=
Compares the values that are represented by "value1" against "value2" and
retrieves the values that are greater than or equal to "value2".
Syntax
value1 >= value2
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and
Returns "true" if the conditions on both sides of the expression are true.
Syntax
argument1 and argument2
between
Determines if a value falls in a given range.
Syntax
expression between value1 and value2
Example
[Revenue] between 200 and 300
Result
Returns the number of results with revenues between 200 and 300.
Result data
Revenue
$332.06
$230.55
$107.94
Between
false
true
false
case
Works with when, then, else, and end. Case identifies the beginning of a specific
situation, in which when, then, and else actions are defined.
Syntax
case expression { when expression then expression } [ else
expression ] end
contains
Determines if "string1" contains "string2".
Syntax
string1 contains string2
distinct
A keyword used in an aggregate expression to include only distinct occurrences of
values. See also the function unique.
Syntax
distinct dataItem
Example
count ( distinct [OrderDetailQuantity] )
Result
Appendix A. Using the expression editor
23
1704
else
Works with the if or case constructs. If the if condition or the case expression are
not true, then the else expression is used.
Syntax
if ( condition ) then .... else ( expression ) , or case .... else (
expression ) end
end
Indicates the end of a case or when construct.
Syntax
case .... end
ends with
Determines if "string1" ends with "string2".
Syntax
string1 ends with string2
if
Works with the then and else constructs. If defines a condition; when the if
condition is true, the then expression is used. When the if condition is not true, the
else expression is used.
Syntax
if ( condition ) then ( expression ) else ( expression )
in
Determines if "expression1" exists in a given list of expressions.
Syntax
expression1 in ( expression_list )
is missing
Determines if "value" is undefined in the data.
Syntax
value is missing
like
Determines if "string1" matches the pattern of "string2", with the character "char"
optionally used to escape characters in the pattern string.
Syntax
string1 LIKE string2 [ ESCAPE char ]
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Example 1
[PRODUCT_LINE] like ’G%’
Result
All product lines that start with 'G'.
Example 2
[PRODUCT_LINE] like ’%Ga%’ escape ’a’
Result
All the product lines that end with 'G%'.
lookup
Finds and replaces data with a value you specify. It is preferable to use the case
construct.
Syntax
lookup ( name ) in ( value1 --> value2 ) default ( expression )
Example
lookup ( [Country]) in ( ’Canada’--> ( [List Price] * 0.60),
’Australia’--> ( [List Price] * 0.80 ) ) default ( [List Price] )
not
Returns TRUE if "argument" is false or returns FALSE if "argument" is true.
Syntax
NOT argument
or
Returns TRUE if either of "argument1" or "argument2" are true.
Syntax
argument1 or argument2
starts with
Determines if "string1" starts with "string2".
Syntax
string1 starts with string2
then
Works with the if or case constructs. When the if condition or the when expression
are true, the then expression is used.
Syntax
if ( condition ) then ..., or case expression when expression
then .... end
Appendix A. Using the expression editor
25
when
Works with the case construct. You can define conditions to occur when the
WHEN expression is true.
Syntax
case [expression] when ... end
Summaries
This list contains predefined functions that return either a single summary value
for a group of related values or a different summary value for each instance of a
group of related values.
Statistical functions
This list contains predefined summary functions of statistical nature.
standard-deviation
Returns the standard deviation of selected data items.
Syntax
standard-deviation ( expression [ auto ] )
standard-deviation ( expression for [ all|any ] expression { ,
expression } )
standard-deviation ( expression for report )
Example
standard-deviation ( ProductCost )
Result
Returns a value indicating the deviation between product costs and the average
product cost.
variance
Returns the variance of selected data items.
Syntax
variance ( expression [ auto ] )
variance ( expression for [ all|any ] expression { , expression } )
variance ( expression for report )
Example
variance ( Product Cost )
Result
Returns a value indicating how widely product costs vary from the average
product cost.
average
Returns the average value of selected data items. Distinct is an alternative
expression that is compatible with earlier versions of the product.
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Syntax
average ( [
average ( [
expression
average ( [
distinct ] expression [ auto ] )
distinct ] expression for [ all|any ] expression { ,
} )
distinct ] expression for report )
Example
average ( Sales )
Result
Returns the average of all Sales values.
count
Returns the number of selected data items excluding null values. Distinct is an
alternative expression that is compatible with earlier versions of the product. All is
supported in DQM mode only and it avoids the presumption of double counting a
data item of a dimension table.
Syntax
count ( [ all
count ( [ all
expression }
count ( [ all
| distinct ] expression [ auto ] )
| distinct ] expression for [ all|any ] expression { ,
)
| distinct ] expression for report )
Example
count ( Sales )
Result
Returns the total number of entries under Sales.
maximum
Returns the maximum value of selected data items. Distinct is an alternative
expression that is compatible with earlier versions of the product.
Syntax
maximum ( [
maximum ( [
expression
maximum ( [
distinct ] expression [ auto ] )
distinct ] expression for [ all|any ] expression { ,
} )
distinct ] expression for report )
Example
maximum ( Sales )
Result
Returns the maximum value out of all Sales values.
median
Returns the median value of selected data items.
Appendix A. Using the expression editor
27
Syntax
median ( expression [ auto ] )
median ( expression for [ all|any ] expression { , expression } )
median ( expression for report )
minimum
Returns the minimum value of selected data items. Distinct is an alternative
expression that is compatible with earlier versions of the product.
Syntax
minimum ( [
minimum ( [
expression
minimum ( [
distinct ] expression [ auto ] )
distinct ] expression for [ all|any ] expression { ,
} )
distinct ] expression for report )
Example
minimum ( Sales )
Result
Returns the minimum value out of all Sales values.
percentage
Returns the percent of the total value for selected data items. The ""
defines the scope of the function. The "at" option defines the level of aggregation
and can be used only in the context of relational datasources.
Syntax
percentage ( numeric_expression [ at expression { , expression } ]
[ ] [ prefilter ] )
percentage ( numeric_expression [ ] [ prefilter ] )
::= for expression { , expression }|for report|auto
Example
percentage ( Sales 98 )
Result
Returns the percentage of the total sales for 1998 that is attributed to each sales
representative.
Result data
Employee
Gibbons
Flertjan
Cornel
Sales 98
60646
62523
22396
Percentage
7.11%
7.35%
2.63%
percentile
Returns a value, on a scale of one hundred, that indicates the percent of a
distribution that is equal to or below the selected data items. The ""
defines the scope of the function. The "at" option defines the level of aggregation
and can be used only in the context of relational datasources.
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Syntax
percentile ( numeric_expression [ at expression { , expression } ]
[ ] [ prefilter ] )
percentile ( numeric_expression [ ] [ prefilter ] )
::= for expression { , expression }|for report|auto
Example
percentile ( Sales 98 )
Result
For each row, returns the percentage of rows that are equal to or less than the
quantity value of that row.
Result data
Qty
800
700
600
500
400
400
200
200
Percentile (Qty)
1
0.875
0.75
0.625
0.5
0.5
0.25
0.25
quantile
Returns the rank of a value within a range that you specify. It returns integers to
represent any range of ranks, such as 1 (highest) to 100 (lowest). The
"" defines the scope of the function. The "at" option defines the level of
aggregation and can be used only in the context of relational datasources.
Syntax
quantile ( numeric_expression , numeric_expression [ at expression { ,
expression } ] [ ] [ prefilter ] )
quantile ( numeric_expression , numeric_expression [ ]
[ prefilter ] )
::= for expression { , expression }|for report|auto
Example
quantile ( Qty , 4 )
Result
Returns the quantity, the rank of the quantity value, and the quantity values
broken down into 4 quantile groups (quartiles).
Result data
Qty
800
700
600
500
400
Rank
1
2
3
4
5
Quantile (Qty, 4)
1
1
2
2
3
Appendix A. Using the expression editor
29
Qty
400
200
200
Rank
5
7
7
Quantile (Qty, 4)
3
4
4
quartile
Returns the rank of a value, represented as integers from 1 (highest) to 4 (lowest),
relative to a group of values. The "" defines the scope of the function.
The "at" option defines the level of aggregation and can be used only in the context
of relational datasources.
Syntax
quartile ( numeric_expression [ at expression { , expression } ]
[ ] [ prefilter ] )
quartile ( numeric_expression [ ] [ prefilter ] )
::= for expression { , expression }|for report|auto
Example
quartile ( Qty )
Result
Returns the quantity and the quartile of the quantity value represented as integers
from 1 (highest) to 4 (lowest).
Result data
Qty
450
400
350
300
250
200
150
100
Quartile (Qty)
1
1
2
2
3
3
4
4
rank
Returns the rank value of selected data items. The sort order is optional;
descending order (DESC) is assumed by default. If two or more rows tie, then
there is a gap in the sequence of ranked values (also known as Olympic ranking).
The "" defines the scope of the function. The "at" option defines the
level of aggregation and can be used only in the context of relational datasources.
Distinct is an alternative expression that is compatible with earlier versions of the
product. Null values are ranked last.
Syntax
rank ( expression [ ASC|DESC ] { , expression [ ASC|DESC ] } [ at
expression { , expression } ] [ ] [ prefilter ] )
rank ( [ distinct ] expression [ ASC|DESC ] { , expression
[ ASC|DESC ] } [ ] [ prefilter ] )
::= for expression { , expression }|for report|auto
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Example
rank ( Sales 98 )
Result
For each row, returns the rank value of sales for 1998 that is attributed to each
sales representative. Some numbers are skipped when a tie between rows occurs.
Result data
Employee
Gibbons
Flertjan
Cornel
Smith
Sales 98
60000
50000
50000
48000
Rank
1
2
2
4
tertile
Returns the rank of a value as High, Middle, or Low relative to a group of values.
Syntax
tertile ( expression [ auto ] )
tertile ( expression for [ all|any ] expression { , expression } )
tertile ( expression for report )
Example
tertile ( Qty )
Result
Returns the quantity, the quantile rank value of the quantity as broken down into
tertiles, and the quantile rank label as broken down into tertiles.
Result data
Qty
800
700
500
400
200
200
Quantile (Qty, 3)
1
1
2
2
3
3
Tertile (Qty)
H
H
M
M
L
L
total
Returns the total value of selected data items. Distinct is an alternative expression
that is compatible with earlier versions of the product.
Syntax
total ( [ distinct ] expression [ auto ] )
total ( [ distinct ] expression for [ all|any ] expression { ,
expression } )
total ( [ distinct ] expression for report )
Appendix A. Using the expression editor
31
Example
total ( Sales )
Result
Returns the total value of all Sales values.
Business Date/Time Functions
This list contains business functions for performing date and time calculations.
_add_seconds
Returns the time or datetime, depending on the format of "time_expression", that
results from adding "integer_expression" seconds to "time_expression".
Syntax
_add_seconds ( time_expression, integer_expression )
Example 1
_add_seconds ( 13:04:59 , 1 )
Result
13:05:00
Example 2
_add_seconds ( 2002-04-30 12:10:10.000, 1 )
Result
2002-04-30 12:10:11.000
Example 3
_add_seconds ( 2002-04-30 00:00:00.000, 1/100 )
Note that the second
argument is not a whole number. This is supported by some database
technologies and increments the time portion.
Result
2002-04-30 00:00:00.010
_add_minutes
Returns the time or datetime, depending on the format of "time_expression", that
results from adding "integer_expression" minutes to "time_expression".
Syntax
_add_minutes ( time_expression, integer_expression )
Example 1
_add_minutes ( 13:59:00 , 1 )
Result
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14:00:00
Example 2
_add_minutes ( 2002-04-30 12:59:10.000, 1 )
Result
2002-04-30 13:00:10.000
Example 3
_add_minutes ( 2002-04-30 00:00:00.000, 1/60 )
Note that the second
argument is not a whole number. This is supported by some database
technologies and increments the time portion.
Result
2002-04-30 00:00:01.000
_add_hours
Returns the time or datetime, depending on the format of "time_expression", that
results from adding "integer_expression" hours to "time_expression".
Syntax
_add_hours ( time_expression, integer_expression )
Example 1
_add_hours ( 13:59:00 , 1 )
Result
14:59:00
Example 2
_add_hours ( 2002-04-30 12:10:10.000, 1 )
Result
2002-04-30 13:10:10.000,
Example 3
_add_hours ( 2002-04-30 00:00:00.000, 1/60 )
Note that the second
argument is not a whole number. This is supported by some database
technologies and increments the time portion.
Result
2002-04-30 00:01:00.000
_add_days
Returns the date or datetime, depending on the format of "date_expression", that
results from adding "integer_expression" days to "date_expression".
Appendix A. Using the expression editor
33
Syntax
_add_days ( date_expression, integer_expression )
Example 1
_add_days ( 2002-04-30 , 1 )
Result
2002-05-01
Example 2
_add_days ( 2002-04-30 12:10:10.000, 1 )
Result
2002-05-01 12:10:10.000
Example 3
_add_days ( 2002-04-30 00:00:00.000, 1/24 )
Note that the second
argument is not a whole number. This is supported by some database
technologies and increments the time portion.
Result
2002-04-30 01:00:00.000
_add_months
Adds "integer_expression" months to "date_expression". If the resulting month has
fewer days than the day of month component, then the last day of the resulting
month is returned. In all other cases the returned value has the same day of month
component as "date_expression".
Syntax
_add_months ( date_expression, integer_expression )
Example 1
_add_months ( 2012-04-15 , 3 )
Result
2012-07-15
Example 2
_add_months ( 2012-02-29 , 1 )
Result
2012-03-29
Example 3
_last_of_month ( _add_months ( 2012-02-29 , 1 ) )
Result
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2012-03-31
Example 4
_add_months ( 2012-01-31 , 1 )
Result
2012-02-29
Example 5
_add_months ( 2002-04-30 12:10:10.000 , 1 )
Result
2002-05-30 12:10:10.000
_add_years
Adds "integer_expression" years to "date_expression". If the "date_expression" is
February 29 and resulting year is non leap year, then the resulting day is set to
February 28. In all other cases the returned value has the same day and month as
"date_expression".
Syntax
_add_years ( date_expression, integer_expression )
Example 1
_add_years ( 2012-04-15 , 1 )
Result
2013-04-15
Example 2
_add_years ( 2012-02-29 , 1 )
Result
2013-02-28
Example 3
_add_years ( 2002-04-30 12:10:10.000 , 1 )
Result
2003-04-30 12:10:10.000
_age
Returns a number that is obtained from subtracting "date_expression" from today's
date. The returned value has the form YYYYMMDD, where YYYY represents the
number of years, MM represents the number of months, and DD represents the
number of days.
Syntax
_age ( date_expression )
Appendix A. Using the expression editor
35
Example
_age ( 1990-04-30 ) (if today’s date is 2003-02-05)
Result
120906, meaning 12 years, 9 months, and 6 days.
current_date
Returns a date value representing the current date of the computer that the
database software runs on.
Syntax
current_date
Example
current_date
Result
2003-03-04
current_time
Returns a time with time zone value, representing the current time of the computer
that runs the database software if the database supports this function. Otherwise, it
represents the current time of the IBM Cognos Analytics server.
Syntax
current_time
Example
current_time
Result
16:33:11.354+05:00
current_timestamp
Returns a datetime with time zone value, representing the current time of the
computer that runs the database software if the database supports this function.
Otherwise, it represents the current time of the server.
Syntax
current_timestamp
Example
current_timestamp
Result
2003-03-03 16:40:15.535+05:00
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_day_of_week
Returns the day of week (1 to 7), where 1 is the first day of the week as indicated
by the second parameter (1 to 7, 1 being Monday and 7 being Sunday). Note that
in ISO 8601 standard, a week begins with Monday being day 1.
Syntax
_day_of_week ( date_expression, integer )
Example
_day_of_week ( 2003-01-01 , 1 )
Result
3
_day_of_year
Returns the day of year (1 to 366) in "date_ expression". Also known as Julian day.
Syntax
_day_of_year ( date_expression )
Example
_day_of_year ( 2003-03-01 )
Result
61
_days_between
Returns a positive or negative number representing the number of days between
"date_expression1" and "date_expression2". If "date_expression1" <
"date_expression2", then the result will be a negative number.
Syntax
_days_between ( date_expression1 , date_expression2 )
Example
_days_between ( 2002-04-30 , 2002-06-21 )
Result
-52
_days_to_end_of_month
Returns a number representing the number of days remaining in the month
represented by "date_expression".
Syntax
_days_to_end_of_month ( date_expression )
Appendix A. Using the expression editor
37
Example
_days_to_end_of_month ( 2002-04-20 14:30:22.123 )
Result
10
_end_of_day
Returns the end of today as a timestamp.
Syntax
_end_of_day
Example
_end_of_day
Result
2014-11-23 23:59:59
_first_of_month
Returns a date or datetime, depending on the argument, by converting
"date_expression" to a date with the same year and month but with the day set to
1.
Syntax
_first_of_month ( date_expression )
Example 1
_first_of_month ( 2002-04-20 )
Result
2002-04-01
Example 2
_first_of_month ( 2002-04-20 12:10:10.000 )
Result
2002-04-01 12:10:10.000
_from_unixtime
Returns the unix time specified by an integer expression as a timestamp with time
zone.
Syntax
_from_unixtime ( integer_expression )
Example
_from_unixtime ( 1417807335 )
Result
2014-12-05 19:22:15+00:00
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_hour
Returns the value of the hour field in a date expression.
Syntax
_hour( date_expression )
Example
_hour ( 2002-01-31 12:10:10.254 )
Result
12
_last_of_month
Returns a date or datetime, depending on the argument, that is the last day of the
month represented by "date_expression".
Syntax
_last_of_month ( date_expression )
Example 1
_last_of_month ( 2002-01-14 )
Result
2002-01-31
Example 2
_last_of_month ( 2002-01-14 12:10:10.000 )
Result
2002-01-31 12:10:10.000
_make_timestamp
Returns a timestamp constructed from "integer_expression1" (the year),
"integer_expression2" (the month), and "integer_expression3" (the day). The time
portion defaults to 00:00:00.000 .
Syntax
_make_timestamp ( integer_expression1, integer_expression2,
integer_expression3 )
Example
_make_timestamp ( 2002 , 01 , 14 )
Result
2002-01-14 00:00:00.000
_minute
Returns the value of the minute field in a date expression.
Appendix A. Using the expression editor
39
Syntax
_minute( date_expression )
Example
_minute ( 2002-01-31 12:10:10.254 )
Result
10
_month
Returns the value of the month field in a date expression.
Syntax
_month( date_expression )
Example
_month ( 2003-03-01 )
Result
3
_months_between
Returns a positive or negative integer number representing the number of months
between "date_expression1" and "date_expression2". If "date_expression1" is earlier
than "date_expression2", then a negative number is returned.
Syntax
_months_between ( date_expression1, date_expression2 )
Example
_months_between ( 2002-04-03 , 2002-01-30 )
Result
2
_second
Returns the value of the second field in a date expression.
Syntax
_second( date_expression )
Example
_second ( 2002-01-31 12:10:10.254 )
Result
10.254
_shift_timezone
Shifts a timestamp value from one time zone to another time zone. This function
honors the Daylight Savings Time when applicable. If the first argument is of type
"timestamp", then the second and third arguments represent the "from" and
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"target" time zones, respectively. If the first argument is of type "timestamp with
time zone", then the "from" time zone is already implied in the first argument
therefore the second argument represents the "target" time zone. The data type of
the first argument will also determine the data type of the return value. The
second and third arguments are of type "string" and represent time zone
identifiers. A list of these identifiers can be found below. Note: using this function
will cause local processing.
Syntax
_shift_timezone ( timestamp_value , from_time_zone ,
target_time_zone )
_shift_timezone ( timestamp_with_time_zone_value , target_time_zone )
Example 1
_shift_timezone( 2013-06-30 12:00:00 , ’EST’ , ’GMT’ )
Result
2013-06-30 16:00:00
Example 2
_shift_timezone( 2013-11-30 12:00:00-05:00 , ’PST’ )
Result
2013-11-30 09:00:00-08:00
Example 3
Time zone abbreviations:
Result data
GMT (GMT+00:00) Greenwich Mean Time
UTC (GMT+00:00) Coordinated Universal Time
WET (GMT+00:00) Western Europe Time: Lisbon, Faeroe Islands, Canary
Islands
ECT (GMT+01:00) European Central Time: Amsterdam, Brussels, Paris,
Rome, Vienna
MET (GMT+01:00) Middle European Time
ART (GMT+02:00) Egypt Time: Cairo, Damascus, Beirut, Amman, Nicosia
CAT (GMT+02:00) Central African Time: Johannesburg, Blantyre, Harare,
Tripoli
EET (GMT+02:00) Eastern Europe Time: Athens, Kiev, Sofia, Minsk,
Bucharest, Vilnius, Tallinn
EAT (GMT+03:00) East Africa Time: Addis Ababa, Asmera, Kampala,
Nairobi, Mogadishu, Khartoum
NET (GMT+04:00) Near East Time
PLT (GMT+05:00) Pakistan Lahore Time
IST (GMT+05:30) Indian Time
BST (GMT+06:00) Bangladesh Time
VST (GMT+07:00) Vietnam Time
CTT (GMT+08:00) Asia, Hong Kong S.A.R. of China
JST (GMT+09:00) Japan Time: Tokyo
ACT (GMT+09:30) Australian Central Time: Darwin
AET (GMT+10:00) Australian Eastern Time: Sydney, Melbourne, Canberra
SST (GMT+11:00) Solomon Time
AGT (GMT-03:00) Argentina Time
BET (GMT-03:00) Brazil Eastern Time: Sao Paulo, Buenos Aires
CNT (GMT-03:30) Newfoundland Time: St. Johns
PRT (GMT-04:00) Puerto Rico and U.S. Virgin Islands Time
EST (GMT-05:00) Eastern Time: Ottawa, New York, Toronto, Montreal,
Appendix A. Using the expression editor
41
Jamaica, Porto Acre
CST (GMT-06:00) Central Time: Chicago, Cambridge Bay, Mexico City
MST (GMT-07:00) Mountain Time: Edmonton, Yellowknife, Chihuahua
PST (GMT-08:00) Pacific Time: Los Angeles, Tijuana, Vancouver
AST (GMT-09:00) Alaska Time: Anchorage, Juneau, Nome, Yakutat
HST (GMT-10:00) Hawaii Time: Honolulu, Tahiti
MIT (GMT-11:00) Midway Islands Time: Midway, Apia, Niue, Pago Pago
Example 4
A customized time zone identifier may also be used, using the format
GMT(+|-)HH:MM. For example, GMT-06:30 or GMT+02:00.
A more complete
list of time zone idenfitiers (including longer form identifiers such
as "Europe/Amsterdam") may be found in the "i18n_res.xml" file from
the product’s configuration folder.
_start_of_day
Returns the start of today as a timestamp.
Syntax
_start_of_day
Example
_start_of_day
Result
2014-11-23 00:00:00
_week_of_year
Returns the number of the week of the year of "date_expression" according to the
ISO 8601 standard. Week 1 of the year is the first week of the year to contain a
Thursday, which is equivalent to the first week containing January 4th. A week
starts on Monday (day 1) and ends on Sunday (day 7).
Syntax
_week_of_year ( date_expression )
Example
_week_of_year ( 2003-01-01 )
Result
1
_timezone_hour
Returns the value of the timezone hour field in a date expression.
Syntax
_timezone_hour( date_expression )
Example
_timezone_hour (
2002-01-31 12:10:10.254-05:30 )
Result
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IBM Cognos Analytics Version 11.0: Data Modeling Guide
5
_timezone_minute
Returns the value of the timezone minute field in a date expression.
Syntax
_timezone_minute( date_expression )
Example
_timezone_minute (
2002-01-31 12:10:10.254-05:30 )
Result
30
_unix_timestamp
Returns the unix time specified by an integer expression as a timestamp with time
zone.
Syntax
_unix_timestamp
Example
_unix_timestamp
Result
1416718800
_year
Returns the value of the year field in a date expression.
Syntax
_year( date_expression )
Example
_year ( 2003-03-01 )
Result
2003
_years_between
Returns a positive or negative integer number representing the number of years
between "date_expression1" and "date_expression2". If "date_expression1" <
"date_expression2" then a negative value is returned.
Syntax
_years_between ( date_expression1, date_expression2 )
Example
_years_between ( 2003-01-30 , 2001-04-03 )
Result
Appendix A. Using the expression editor
43
1
_ymdint_between
Returns a number representing the difference between "date_expression1" and
"date_expression2". The returned value has the form YYYYMMDD, where YYYY
represents the number of years, MM represents the number of months, and DD
represents the number of days.
Syntax
_ymdint_between ( date_expression1 , date_expression2 )
Example
_ymdint_between ( 1990-04-30 , 2003-02-05 )
Result
120906, meaning 12 years, 9 months and 6 days.
Common Functions
abs
Returns the absolute value of "numeric_expression". Negative values are returned
as positive values.
Syntax
abs ( numeric_expression )
Example 1
abs ( 15 )
Result
15
Example 2
abs ( -15 )
Result
15
cast
Converts "expression" to a specified data type. Some data types allow for a length
and precision to be specified. Make sure that the target is of the appropriate type
and size. The following can be used for "datatype_specification": character, varchar,
char, numeric, decimal, integer, bigint, smallint, real, float, date, time, timestamp,
time with time zone, timestamp with time zone, and interval. When type casting to
an interval type, one of the following interval qualifiers must be specified: year,
month, or year to month for the year-to-month interval datatype; day, hour,
minute, second, day to hour, day to minute, day to second, hour to minute, hour
to second, or minute to second for the day-to-second interval datatype. Notes:
When you convert a value of type timestamp to type date, the time portion of the
timestamp value is ignored. When you convert a value of type timestamp to type
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IBM Cognos Analytics Version 11.0: Data Modeling Guide
time, the date portion of the timestamp is ignored. When you convert a value of
type date to type timestamp, the time components of the timestamp are set to zero.
When you convert a value of type time to type timestamp, the date component is
set to the current system date. It is invalid to convert one interval datatype to the
other (for instance because the number of days in a month is variable). Note that
you can specify the number of digits for the leading qualifier only, i.e. YEAR(4) TO
MONTH, DAY(5). Errors will be reported if the target type and size are not
compatible with the source type and size.
Syntax
cast ( expression , datatype_specification )
Example 1
cast ( ’123’ , integer )
Result
123
Example 2
cast ( 12345 , varchar ( 10 ) )
Result
a string containing 12345
ceiling
Returns the smallest integer that is greater than or equal to "numeric_expression".
Syntax
ceiling ( numeric_expression )
Example 1
ceiling ( 4.22 )
Result
5
Example 2
ceiling ( -1.23 )
Result
-1
char_length
Returns the number of logical characters in "string_expression". The number of
logical characters can be distinct from the number of bytes in some East Asian
locales.
Syntax
char_length ( string_expression )
Appendix A. Using the expression editor
45
Example
char_length ( ’Canada’ )
Result
6
coalesce
Returns the first non-null argument (or null if all arguments are null). Requires
two or more arguments in "expression_list".
Syntax
coalesce ( expression_list )
Example
coalesce ( [Unit price], [Unit sale price] )
Result
Returns the unit price, or the unit sale price if the unit price is null.
exp
Returns 'e' raised to the power of "numeric_expression". The constant 'e' is the base
of the natural logarithm.
Syntax
exp ( numeric_expression )
Example
exp ( 2 )
Result
7.389056
floor
Returns the largest integer that is less than or equal to "numeric_expression".
Syntax
floor ( numeric_expression )
Example 1
floor ( 3.22 )
Result
3
Example 2
floor ( -1.23 )
Result
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-2
ln
Returns the natural logarithm of "numeric_expression".
Syntax
ln ( numeric_expression )
Example
ln ( 4 )
Result
1.38629
lower
Returns "string_expression" with all uppercase characters shifted to lowercase.
Syntax
lower ( string_expression )
Example
lower ( ’ABCDEF’ )
Result
abcdef
mod
Returns the remainder (modulus) of "integer_expression1" divided by
"integer_expression2". "Integer_expression2" must not be zero or an exception
condition is raised.
Syntax
mod ( integer_expression1, integer_expression2 )
Example
mod ( 20 , 3 )
Result
2
nullif
Returns null if "expression1" equals "expression2", otherwise returns "expression1".
Syntax
nullif ( expression1, expression2 )
position
Returns the integer value representing the starting position of "string_expression1"
in "string_expression2" or 0 when the "string_expression1" is not found.
Appendix A. Using the expression editor
47
Syntax
position ( string_expression1 , string_expression2 )
Example 1
position ( ’C’ , ’ABCDEF’ )
Result
3
Example 2
position ( ’H’ , ’ABCDEF’ )
Result
0
position_regex
Returns the integer value representing the beginning or ending position of the
substring in "string_expression" that matches the regular expression
"regex_expression". The search starts at position "integer_expression1", which has a
default value of 1. The occurrence of the pattern to search for is specified by
"integer_expression2", which has a default value of 1. The return option, specified
by the first argument, specifies what is returned in relation to the occurrence. If
you specify "start", the position of the first character of the occurrence is returned.
If you specify "after", the position of the character following the occurrence is
returned. If you don't specify a return option, "start" is implicit. Flags to set options
for the interpretation of the regular expression are specified by "flags_expression".
Individual letters are used to define the flags, with valid values being 's', 'm', 'i',
and 'x'.
Syntax
position_regex ([ start|after ] regex_expression , string_expression
[ , integer_expression1 [ , integer_expression2 [ , flags_expression ]]] )
Example 1
position_regex ( ’.er’ , ’Flicker Lantern’ )
Result
5
Example 2
position_regex ( after ’.er’ , ’Flicker Lantern’ )
Result
8
Example 3
position_regex ( ’.er’ , ’Flicker Lantern’ , 1 , 2 )
Result
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12
power
Returns "numeric_expression1" raised to the power "numeric_expression2". If
"numeric_expression1" is negative, then "numeric_expression2" must result in an
integer value.
Syntax
power ( numeric_expression1 , numeric_expression2 )
Example
power ( 3 , 2 )
Result
9
_round
Returns "numeric_expression" rounded to "integer_expression" decimal places.
Notes: "integer_expression" must be a non-negative integer. Rounding takes place
before data formatting is applied.
Syntax
_round ( numeric_expression , integer_expression )
Example
_round ( 1220.42369, 2 )
Result
1220.42
sqrt
Returns the square root of "numeric_expression". "Numeric_expression" must be
non-negative.
Syntax
sqrt ( numeric_expression )
Example
sqrt ( 9 )
Result
3
substring
Returns the substring of "string_expression" that starts at position
"integer_expression1" for "integer_expression2" characters or to the end of
"string_expression" if "integer_expression2" is omitted. The first character in
"string_expression" is at position 1.
Appendix A. Using the expression editor
49
Syntax
substring ( string_expression , integer_expression1 [ ,
integer_expression2 ] )
Example
substring ( ’abcdefg’ , 3 , 2 )
Result
cd
substring_regex
Returns a substring of "string_expression" that matches the regular expression
"regex_expression". The search starts at position "integer_expression1", which has a
default value of 1. The occurrence of the pattern to search for is specified by
"integer_expression2", which has a default value of 1. Flags to set options for the
interpretation of the regular expression are specified by "flags_expression".
Individual letters are used to define the flags, with valid values being 's', 'm', 'i',
and 'x'.
Syntax
substring_regex ( regex_expression , string_expression [ , integer_expression1
[ , integer_expression [ , flags_expression ]]] )
Example 1
substring_regex ( ’.er’ , ’Flicker Lantern’)
Result
ker
Example 2
substring_regex ( ’.er’ , ’Flicker Lantern’ , 1 , 2 )
Result
ter
trim
Returns "string_expression" trimmed of leading and trailing blanks or trimmed of a
certain character specified in "match_character_expression". "Both" is implicit when
the first argument is not stated and blank is implicit when the second argument is
not stated.
Syntax
trim ( [ [ trailing|leading|both ] [ match_character_expression ] , ]
string_expression )
Example 1
trim ( trailing ’A’ , ’ABCDEFA’ )
Result
ABCDEF
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IBM Cognos Analytics Version 11.0: Data Modeling Guide
Example 2
trim ( both , ’ ABCDEF ’ )
Result
ABCDEF
upper
Returns "string_expression" with all lowercase characters converted to uppercase.
Syntax
upper ( string_expression )
Example
upper ( ’abcdef’ )
Result
ABCDEF
Trigonometric functions
arccos
Returns the arc cosine of the argument, where the argument is in the range of -1 to
1 and the result is a value expressed in radians.
Syntax
arccos ( numeric_expression )
Example
arccos ( -1 )
Result
3.1415
arcsin
Returns the arc sine of the argument, where the argument is in the range of -1 to 1
and the result is a value expressed in radians.
Syntax
arcsin ( numeric_expression )
Example
arcsin ( 0 )
Result
3.1415
arctan
Returns the arc tangent of the argument, where the argument is in the range of -1
to 1 and the result is a value expressed in radians.
Appendix A. Using the expression editor
51
Syntax
arctan ( numeric_expression )
Example
arctan ( 0 )
Result
3.1415
cos
Returns the cosine of the argument, where the argument is expressed in radians.
Syntax
cos ( numeric_expression )
Example
cos ( 0.3333 * 3.1415 )
Result
0.5
coshyp
Returns the hyperbolic cosine of the argument, where the argument is expressed in
radians.
Syntax
coshyp ( numeric_expression )
Example
coshyp ( 0 )
Result
1
sin
Returns the sine of the argument, where the argument is expressed in radians.
Syntax
sin ( numeric_expression )
Example
sin
( 0.1667 * 3.1415 )
Result
0.5
sinhyp
Returns the hyperbolic sine of the argument, where the argument is expressed in
radians.
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IBM Cognos Analytics Version 11.0: Data Modeling Guide
Syntax
sinhyp ( numeric_expression )
Example
sinhyp ( 0 )
Result
0
tan
Returns the tangent of the argument, where the argument is expressed in radians.
Syntax
tan ( numeric_expression )
Example
tan ( 0.25 * 3.1415 )
Result
1
tanhyp
Returns the hyperbolic tangent of the argument, where the argument is expressed
in radians.
Syntax
tanhyp ( numeric_expression )
Example
tanhyp ( 0 )
Result
0
Appendix A. Using the expression editor
53
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IBM Cognos Analytics Version 11.0: Data Modeling Guide
Appendix B. About this guide
This document is intended for use with IBM Cognos Analytics. Cognos Analytics
integrates reporting, modeling, analysis, dashboards, metrics, and event
management so you can understand your organization's data, and make effective
business decisions.
To find product documentation on the web, including all translated
documentation, access IBM Knowledge Center (http://www.ibm.com/support/
knowledgecenter).
Accessibility features
Accessibility features help users who have a physical disability, such as restricted
mobility or limited vision, to use information technology products successfully. For
information on accessibility features in Cognos Analytics, see the Cognos Analytics
Accessibility Guide.
Forward-looking statements
This documentation describes the current functionality of the product. References
to items that are not currently available may be included. No implication of any
future availability should be inferred. Any such references are not a commitment,
promise, or legal obligation to deliver any material, code, or functionality. The
development, release, and timing of features or functionality remain at the sole
discretion of IBM.
Samples disclaimer
The Sample Outdoors Company, Great Outdoors Company, GO Sales, any
variation of the Sample Outdoors or Great Outdoors names, and Planning Sample
depict fictitious business operations with sample data used to develop sample
applications for IBM and IBM customers. These fictitious records include sample
data for sales transactions, product distribution, finance, and human resources.
Any resemblance to actual names, addresses, contact numbers, or transaction
values is coincidental. Other sample files may contain fictional data manually or
machine generated, factual data compiled from academic or public sources, or data
used with permission of the copyright holder, for use as sample data to develop
sample applications. Product names referenced may be the trademarks of their
respective owners. Unauthorized duplication is prohibited.
© Copyright IBM Corp. 2015, 2016
55
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IBM Cognos Analytics Version 11.0: Data Modeling Guide
Index
C
M
cleaning
columns in modules
modeling user interface 9
modules
cleaning data 14
editing 14
hiding tables and columns
validating 19
14
D
data modeling
data modules
editing 9
1
18
N
navigation path
creating 16
deleting 16
E
editing data modules 9
undo and redo actions 9
user interface 9
editing modules
validation errors 19
expression editor
Business Date/Time Functions
Common Functions 44
Statistical functions 26
Summaries 26
Trigonometric functions 51
F
R
redo
editing data modules
9
32
U
undo
editing data modules
9
V
filters
adding 17
removing 17
validating
modules
19
H
hiding
tables and columns
18
© Copyright IBM Corp. 2015, 2016
57
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