PoliCheck updates

Author: damabe

docs/ado/guide/multidimensional/working-with-multidimensional-data.md

@@ -23,7 +23,7 @@ A *cellset* is the result of a query on multidimensional data. It consists of a
 
 -   Salesperson  
 
--   Geography (natural hierarchy) - Continents, Countries, States, and so on  
+-   Geography (natural hierarchy) - Continents, Countries/Regions, States, and so on  
 
 -   Quarters - Quarters, Months, Days  
 

docs/data-quality-services/dqs-knowledge-bases-and-domains.md

@@ -114,13 +114,13 @@ ms.topic: conceptual
  In domain management, you can specify a term-based relation for a single domain, specifying a change to a single value.  
 
 ### Composite Domains  
- A composite domain is a structure comprised of two or more single domains that each contain knowledge about common data. Examples of data that can be addressed by composite domains are the first, middle, and family names in a name field, and the house number and street, city, state, postal code, and country in an address field. When you map a single field to a composite domain, DQS parses the data from the one field into the multiple domains that make up the composite.  
+ A composite domain is a structure comprised of two or more single domains that each contain knowledge about common data. Examples of data that can be addressed by composite domains are the first, middle, and family names in a name field, and the house number and street, city, state, postal code, and country/region in an address field. When you map a single field to a composite domain, DQS parses the data from the one field into the multiple domains that make up the composite.  
 
  Sometimes a single domain does not represent field data in full. Grouping two or more domains in a composite domain can enable you to represent the data in an efficient way. The following are advantages of using composite domains:  
 
 -   Analyzing the different single domains that make up a composite domain can be a more effective way of assessing data quality.  
 
--   When you use a composite domain, you can also create cross-domain rules that enable you to verify that the relationship between the data in multiple domains is appropriate. For example, you can verify that the string "London" in a city domain corresponds to the string "England" in a country domain. Note that cross-domain rules are taken into consideration after domain rules.  
+-   When you use a composite domain, you can also create cross-domain rules that enable you to verify that the relationship between the data in multiple domains is appropriate. For example, you can verify that the string "London" in a city domain corresponds to the string "England" in a country/region domain. Note that cross-domain rules are taken into consideration after domain rules.  
 
 -   Data in composite domains can be attached to a reference data source, in which case the composite domain will be sent to the reference data provider. This is often done with address data.  
 

docs/data-quality-services/managing-a-composite-domain.md

@@ -14,7 +14,7 @@ ms.topic: conceptual
 
   This topic describes the use of composite domains in [!INCLUDE[ssDQSnoversion](../includes/ssdqsnoversion-md.md)] (DQS). Sometimes a single domain does not represent the data in a field satisfactorily, and you can represent the data only by grouping single domains. To do so, you create a composite domain. A composite domain consists of two or more single domains, and maps to a data field that consists of multiple related terms that are not parsed, but are included in a single composite value. Each term in the value will be represented by a different single domain. Once you have included single domains into composite domains, and then mapped the composite domain to the data field, you can build knowledge in the knowledge base about the data in that field by building knowledge in the single domains. A composite domain, like a single domain, is a semantic representation of the data in a single data field.  
 
- The single domains in a composite domain must have a common area of knowledge. An example is an address field that has street, city, state, country, and postal code data. The different terms in this field could have different data types. To handle that, you map those terms to different single domains. Another example is a full name field that has first name, middle name, and last name data. To use a composite domain, you have to be able to parse the data in the field into different single domains, creating a composite domain for the field and a single domain for part of the field.  
+ The single domains in a composite domain must have a common area of knowledge. An example is an address field that has street, city, state, country/region, and postal code data. The different terms in this field could have different data types. To handle that, you map those terms to different single domains. Another example is a full name field that has first name, middle name, and last name data. To use a composite domain, you have to be able to parse the data in the field into different single domains, creating a composite domain for the field and a single domain for part of the field.  
 
  Composite domains have different capabilities than single domains. You cannot change the values in the composite domain-you must do so in a single domain. With composite domains, you can use cross-domain rules to test the values in the single domains of the composite domain. You can also view the value combinations that are found in the composite domains.  
 

docs/mdx/median-mdx.md

@@ -38,7 +38,7 @@ Median(Set_Expression [ ,Numeric_Expression ] )
 >  [!INCLUDE[ssASnoversion](../includes/ssasnoversion-md.md)] ignores nulls when calculating the median value in a set of ordered numbers.  
   
 ## Example  
- The following example returns the median monthly sales for each quarter, each subcategory, and each country in the Adventure Works cube.  
+ The following example returns the median monthly sales for each quarter, each subcategory, and each country/region in the Adventure Works cube.  
 
 ```  
 WITH MEMBER Measures.x AS Median   

docs/odbc/reference/appendixes/sql-to-c-timestamp.md

@@ -45,4 +45,4 @@ The following table shows the ODBC C data types to which timestamp SQL data can
 
  [f]   This is the size of the corresponding C data type.  
 
-When timestamp SQL data is converted to character C data, the resulting string is in the "*yyyy*-*mm*-*dd* *hh*:*mm*:*ss*[.*f...*]" format, where up to nine digits can be used for fractional seconds. This format is not affected by the Windows® country setting. (Except for the decimal point and fractional seconds, the entire format must be used, regardless of the precision of the timestamp SQL data type.)
+When timestamp SQL data is converted to character C data, the resulting string is in the "*yyyy*-*mm*-*dd* *hh*:*mm*:*ss*[.*f...*]" format, where up to nine digits can be used for fractional seconds. This format is not affected by the Windows® country/region setting. (Except for the decimal point and fractional seconds, the entire format must be used, regardless of the precision of the timestamp SQL data type.)

docs/relational-databases/indexes/columnstore-indexes-query-performance.md

@@ -62,7 +62,7 @@ monikerRange: ">=aps-pdw-2016||=azuresqldb-current||=azure-sqldw-latest||>=sql-s
 
 - Columnstore indexes compress data by columns instead of by rows, achieving high compression rates and reducing the size of the data stored on disk. Each column is compressed and stored independently.  Data within a column always has the same data type and tends to have similar values. Data compression techniques are very good at achieving higher compression rates when values are similar.
 
-- For example, if a fact table stores customer addresses and has a column for country, the total number of possible values is fewer than 200. Some of those values will be repeated many times. If the fact table has 100 million rows, the country column will compress easily and require very little storage. Row-by-row compression is not able to capitalize on the similarity of column values in this way and will use more bytes to compress the values in the country column.
+- For example, if a fact table stores customer addresses and has a column for country/region, the total number of possible values is fewer than 200. Some of those values will be repeated many times. If the fact table has 100 million rows, the country/region column will compress easily and require very little storage. Row-by-row compression is not able to capitalize on the similarity of column values in this way and will use more bytes to compress the values in the country/region column.
 
 ### Column elimination
 

docs/relational-databases/json/optimize-json-processing-with-in-memory-oltp.md

@@ -74,7 +74,7 @@ ALTER TABLE xtp.Product
 Computed columns let you expose values from JSON text and access those values without fetching the value from the JSON text again and without parsing the JSON structure again. Values exposted in this way are strongly typed and physically persisted in the computed columns. Accessing JSON values using persisted computed columns is faster than accessing values in the JSON document directly.
 
 The following example shows how to expose the following two values from the JSON `Data` column:
--   The country where a product is made.
+-   The country/region where a product is made.
 -   The product manufacturing cost.
 
 In this example, the computed columns `MadeIn` and `Cost` are updated every time the JSON document stored in the `Data` column changes.

docs/reporting-services/report-data/data-connections-data-sources-and-connection-strings-report-builder-and-ssrs.md

@@ -55,7 +55,7 @@ ms.author: maggies
  If you configure your ODBC or SQL data source to prompt for a password or to include the password in the connection string, and a user enters the password with special characters like punctuation marks, some underlying data source drivers cannot validate the special characters. When you process the report, the message "Not a valid password" may indicate this problem. If changing the password is impractical, you can work with your database administrator to store the appropriate credentials on the server as part of a system ODBC data source name (DSN). For more information, see [OdbcConnection.ConnectionString](/dotnet/api/system.data.odbc.odbcconnection.connectionstring)" in the [!INCLUDE[dnprdnshort](../../includes/dnprdnshort-md.md)] documentation.  
 
 ##  <a name="bkmk_Expressions_in_connection_strings"></a> Expression-based connection strings  
- Expression-based connection strings are evaluated at run time. For example, you can specify the data source as a parameter, include the parameter reference in the connection string, and allow the user to choose a data source for the report. For example, suppose a multinational firm has data servers in several countries/regions. With an expression-based connection string, a user who is running a sales report can select a data source for a particular country before running the report.  
+ Expression-based connection strings are evaluated at run time. For example, you can specify the data source as a parameter, include the parameter reference in the connection string, and allow the user to choose a data source for the report. For example, suppose a multinational firm has data servers in several countries/regions. With an expression-based connection string, a user who is running a sales report can select a data source for a particular country/region before running the report.  
 
  The following example illustrates the use of a data source expression in a [!INCLUDE[ssNoVersion](../../includes/ssnoversion-md.md)] connection string. The example assumes you have created a report parameter named `ServerName`:  
 

docs/t-sql/data-types/nondeterministic-convert-date-literals.md

@@ -73,7 +73,7 @@ The preceding claim of "no guarantee" might be incorrect, in the minds of the SQ
 
 #### Specific countries/regions
 
-In Japan and China, the DATEFORMAT of **ymd** is used. The format's parts are in a sensible sequence of largest unit to smallest. So, this format sorts well. This format is considered to be the _international_ format. It's international because the four digits of the year are unambiguous, and no country on Earth uses the archaic format of **ydm**.
+In Japan and China, the DATEFORMAT of **ymd** is used. The format's parts are in a sensible sequence of largest unit to smallest. So, this format sorts well. This format is considered to be the _international_ format. It's international because the four digits of the year are unambiguous, and no country/region on Earth uses the archaic format of **ydm**.
 
 In other countries/regions such as Germany and France, the DATEFORMAT is **dmy**, meaning **'dd-mm-yyyy'**. The **dmy** format doesn't sort well, but it's a sensible sequence of smallest unit to largest.