Skip to main content

Internationalization Design Technics

Microstrategy Internationalization Design Technics

MicroStrategy supports data internationalization through two different techniques. You can either provide translated data through the use of extra tables and columns, or you can provide separate databases to store your translated data. These techniques are described below:

You can support data internationalization in your database by using separate tables and columns to store your translated data. You can use various combinations of tables and columns to support and identify the translated data in your database.


To support displaying the name of each month in multiple languages, you can include the translated names in a separate column, one for each required language, within the same table. Each column can use a suffix to identify that the column contains translated data for a certain language. The same LU_MONTH_OF_YEAR table with translated data for the Spanish and German languages is shown below:
The data for Spanish is included in a MONTH_OF_YEAR_NAME column with the suffix _ES, and the data for German is included in a MONTH_OF_YEAR_NAME column with the suffix _DE.
As an alternative to supplying translations by using separate columns in the same table, you can create separate tables for your translations. Each table can share the same column name for the same data in different languages. In the tables below, the Spanish and German data is provided in separate Spanish and German tables:
The data for Spanish is included in a LU_MONTH_OF_YEAR table with the suffix _ES, but the MONTH_OF_YEAR column shares the same column name as in the English LU_MONTH_OF_YEAR table. The data for German uses the same technique and is stored in a LU_MONTH_OF_YEAR table with the suffix _DE.
You can also use both techniques (separate tables and extra columns in one table) to store and identify your translated data. This can be helpful to distinguish the language used for each table and column. It can also be helpful if you have a primary language stored in one table, and you store all internationalizations in an internationalization table. For example, you can store the Spanish and German data in the same internationalization table, as shown below:
In this scenario, the LU_MONTH_OF_YEAR_LANG table includes all translations in all languages other than the primary language, for the MONTH_OF_YEAR_NAME column. Each column is assigned a suffix to identify the language of the translated data.
• In the examples above, suffixes on tables and columns are used to identify the language of the translated data. While it is not a requirement to use suffixes for these identification purposes, it is the easiest method to define and support in MicroStrategy. Using prefixes or other naming conventions requires you to use some functions to recognize the location of the translated data.
• If your project supports data internationalization, you cannot use logical views as lookup tables for attributes that use translated data. 

Using separate databases for internationalization

You can support data internationalization in your database by using separate databases for each supported language. A user can then be granted access, through connection mappings, to the database that contains their preferred language.
For example, the MicroStrategy Tutorial project includes a Month of Year attribute which retrieves its primary information in English from the LU_MONTH_OF_YEAR table shown below:
For the purposes of this example, you can assume this data is stored in a database named Tutorial (English). You also provide your projects in Spanish and German, which means you must have a database for Spanish and a database for German. Each database contains the same table structure, column structure, and naming conventions, but includes translated data, as shown below:

This method of data internationalization requires that the same data is available in each internationalized database. 

Comments

Popular posts from this blog

Level metrics in Microstartegy/MSTR

Level Metrics Level metrics allows the users to choose the right combination of level target, filtering, and grouping (referred to as elements) to achieve your desired results.  The elements of a metric level are described below: • Target : The target is the attribute level at which the metric calculation groups. For a more detailed description, see  Target: The context of a calculation . • Grouping : Grouping determines how the metric aggregates. For a more detailed description, see  Grouping: How aggregation is performed . • Filtering : Filtering governs how the report filter interacts with the metric calculation. For a more detailed description, see  Filtering: Interaction with report filters . The level is indicated between the curly braces ({ }) in the metric definition shown below: Sum(Revenue) {~, Product} The tilde (~) represents the report level with standard filtering, denoted by the plus sign (+). If you add item as a level, th...

Tableau Dataset Connector to Microstrategy REST API URL

Tableau Dataset Connector to Microstrategy REST API URL The MicroStrategy Dataset Connector is an out‑of‑the box feature that allows you to fetch data from reports or cubes that reside in MicroStrategy projects into Tableau Public (also known as Tableau Desktop). You can import datasets with up to four million rows and 30+ columns. Tableau Public 10.4 or later is needed. Open Tableau Desktop Public Edition. Under Connect, click  Web Data Connector . If it is not there, click  More > Web Data Connector . Enter the following URL: https:// <MSTR Domain> /CommunityConnectors/mstr/ If the connector does not automatically detect Tableau, add the query  ?mode=tableau  to the end of the URL and refresh your browser. Enter your API Server URL and environment credentials. Your REST API URL is your environment URL with  /MicroStrategyLibrary/  added at the end. Click  Connect Select the dataset you want to import. Select the...

Microstrategy Dossiers explained

Microstrategy  Dossiers With the release of MicroStrategy 10.9, we’ve taken a leap forward in our dashboarding capabilities by simplifying the user experience, adding storytelling, and collaboration.MSTR has  evolved dashboards to the point that they are more than dashboards - they are  interactive, collaborative analytic stories . Ultimately, it was time to go beyond dashboards, both in concept and in name, and so  the've  renamed VI dashboards to  ‘ dossiers ’.  Dossiers can be created by using the new Desktop product or Workstation or simply from the Web interface which replaces Visual Insights. All the existing visual Insights dashboards will be converted to Dossiers   With MicroStrategy 10.9, there was an active focus on making it easier to build dashboards for the widest audience of end users. To achieve this, some key new capabilities were added that make it easier to author, read, interact and collaborate on dashboards ...

Apply or Pass-through functions in Microstrategy

Ap ply (Pass-Through) functions MSTR Apply functions provide access to functions or syntactic constructs that are not standard in MicroStrategy but are provided by various RDBMS systems.. Syntax common to Apply functions Apply Function Name   ("expression with placeholders", Arg1, Arg2, Arg3, …ArgN) where: Apply Function Name  – is a generic name used for the predefined pass-through functions described above expression with placeholders  – is the string describing the actual expression or syntax that the engine uses while generating the SQL and which is sent to the RDBMS. The placeholders are represented by #0, #1, and so on. "#" is a reserved character for MicroStrategy. Arg  – is an argument that replaces the parameter markers in the pattern. Arg1 replaces #0, Arg2 replaces #1, and so on. There are   five  pre-defined Apply functions to replace regular, predefined functions of the same type. For more details, cli...

Microstrategy Report Execution Process

The Report Execution Process  Report execution process at a high level: The report execution process is a three-step process:  1. Query Stage : Retrieve data from the warehouse  2. Populate and Evaluate : Fill report data required for display  3. Cross-tab : Pivot and display sorting and page-by Each of the engines plays an important part in the report execution process.  As you can imagine, the SQL Engine performs its role during the Query stage while the Query Engine and Analytical Engine can be involved in all three stages.

Types of filters in Microstrategy

Types of filters in Microstrategy Below are the types of filters: 1. Attribute qualification filter These types of qualifications restrict data related to attributes on the report. a) Attribute form qualification Filters data related to a business attribute’s form(s), such as ID or description. •  For example, the attribute Customer has the forms ID, First Name, Last Name, Address, and Birth Date. An attribute form qualification might filter on the form Last Name, the operator Begins With, and the letter H. The results show a list of customers whose last names start with the letter H. b) Attribute element list qualification Filters data related to a business attribute’s elements, such as New York, Washington, and San Francisco, which are elements of the attribute City. • For example, the attribute Customer has the elements John Smith, Jane Doe, William Hill, and so on. An attribute element list qualification can filter data to display only those customer...

Case functions Microstrategy

Ca se functions Microstrategy Case functions return specified data in a SQL query based on the evaluation of user-defined conditions. In general, a user specifies a list of conditions and corresponding return values. Case This function evaluates multiple expressions until a condition is determined to be true, then returns a corresponding value. If all conditions are false, a default value is returned.  Case  can be used for categorizing data based on multiple conditions. This is a single-value function. Syntax Case ( Condition1 ,  ReturnValue1 ,  Condition2 , ReturnValue2 ,...,  DefaultValue ) Example Case(([Total Revenue] < 300000), 0, ([Total Revenue] < 600000), 1, 2) sum(Case (Day@DESC in (“Sat”,”Sun”), Sales, 0) {~+} Sum(Case(Category@DESC In("Books","Electronics"),Revenue,0)){~+} CaseV (case vector) CaseV  evaluates a single metric and returns different values according to the results. It can be used to perfo...

Microstrategy Custom number formatting symbols

Custom number formatting symbols If none of the built-in number formats meet your needs, you can create your own custom format in the Number tab of the Format Cells dialog box. Select  Custom  as the Category and create the format using the number format symbols listed in the table below. Each custom format can have up to four optional sections, one each for: Positive numbers Negative numbers Zeros Text Each section is optional. Separate the sections by semicolons, as shown in the example below: #,###;(#,###);0;"Error: Entry must be numeric" For more examples, see  Custom number formatting examples . To jump to a section of the formatting symbol table, click one of the following: Numeric symbols Character/text symbols Date and time symbols Text color symbols Currency symbols Conditional symbols Numeric symbols For details on how numeric symbols apply to the Big Decimal data type, refer to the  Project Design Guide . ...

Custom Tooltips in Microstrategy developer and Web

Custom Tooltips in Microstrategy developer and Web The following table describes the macros you can use to customize graph tooltips in both MicroStrategy Developer and MicroStrategy Web: Macro Information Displayed {&TOOLTIP} All relevant labels and values associated with a graph item. {&GROUPLABEL} Name of the graph item's category. This value is often the graph item's attribute element information, as attributes are commonly used as the categories of graph reports. {&SERIESLABEL} Name of the graph item’s series. This value is often the graph item's metric name information, as metrics are commonly used as the series of graph reports. {&VALUE} The value of a given data point. {&XVALUE} The X-value of a data point. Only applicable to Bubble charts and Scatter plots. {&YVALUE} The Y-value of a data point. Only applicable to Bubble charts and Scatter plots. {&ZVALUE} The Z-value of a data point. Only applicable to Bubble charts and Scatter plots. {...

Settings for Outer Join between metrics in MicroStrategy

Settings for Outer Join between metrics in MicroStrategy MicroStrategy adopts multi-pass logic to determine the execution plan for a report. This means that every metric is evaluated in separate SQL passes. Outer Joins come into play when MicroStrategy Engine merges the results from all SQL passes into one report. For a multi-pass report, different Outer Join behaviors can give the user completely different results. In addition, report metrics can be of different types which can, in some cases, influence the result of the outer join. In MicroStrategy, there are two settings that users can access to control Outer Join behavior : Formula Join Type and Metric Join Type . Metric Join Type: VLDB Setting at Database Instance Level Report and Template Levels Report Editor > Data > Report Data Options Metric Level   Metric editor > Tools > Metric Join Type Control Join between Metrics Formula Join Type: Only at Compound/Split...