docs/dmx/, 'http:' need 's', 'en-us/', CATS inspired. Previous PR was 8069.

MightyPen · MightyPen · commit 24b0aefd152f · 2018-11-13T00:35:28.000-08:00
diff --git a/docs/dma/dma-consolidatereports.md b/docs/dma/dma-consolidatereports.md
@@ -1,4 +1,4 @@
----
+﻿---
 title: "Assess an enterprise and consolidate assessment reports (SQL Server) | Microsoft Docs"
 description: Learn how to use DMA to assess an enterprise and consolidate assessment reports before upgrading SQL Server or migrating to Azure SQL Database.
 ms.custom: ""
@@ -26,7 +26,7 @@ The following step-by-step instructions help you use the Data Migration Assistan
 - Designate a tools computer on your network from which DMA will be initiated. Ensure that this computer has connectivity to your SQL Server targets.
 - Download and install:
     - [Data Migration Assistant](https://www.microsoft.com/download/details.aspx?id=53595) v3.6 or above.
-    - [PowerShell](http://aka.ms/wmf5download) v5.0 or above.
+    - [PowerShell](https://aka.ms/wmf5download) v5.0 or above.
     - [.NET Framework](https://www.microsoft.com/download/details.aspx?id=30653) v4.5 or above.
     - [SSMS](https://docs.microsoft.com/sql/ssms/download-sql-server-management-studio-ssms) 17.0 or above.
     - [PowerBI desktop](https://docs.microsoft.com/power-bi/desktop-get-the-desktop).
diff --git a/docs/dmx/bottomcount-dmx.md b/docs/dmx/bottomcount-dmx.md
@@ -32,7 +32,7 @@ BottomCount(<table expression>, <rank expression>, <count>)
  The value that is supplied by the \<rank expression> argument determines the increasing order of rank for the rows that are supplied in the \<table expression> argument, and the number of bottom-most rows that is specified in the \<count> argument is returned.  
   
 ## Examples  
- The following example creates a prediction query against the Association model that you build by using the [Basic Data Mining Tutorial](http://msdn.microsoft.com/library/6602edb6-d160-43fb-83c8-9df5dddfeb9c).  
+ The following example creates a prediction query against the Association model that you build by using the [Basic Data Mining Tutorial](https://msdn.microsoft.com/library/6602edb6-d160-43fb-83c8-9df5dddfeb9c).  
   
  To understand how BottomCount works, it might be helpful to first execute a prediction query that returns only the nested table.  
   
diff --git a/docs/dmx/bottompercent-dmx.md b/docs/dmx/bottompercent-dmx.md
@@ -39,7 +39,7 @@ BottomPercent(<table expression>, <rank expression>, <percent>)
  The **BottomPercent** function returns the bottom-most rows in increasing order of rank. The rank is based on the evaluated value of the \<rank expression> argument for each row, such that the sum of the \<rank expression> values is at least the given percentage that is specified by the \<percent> argument. **BottomPercent** returns the smallest number of elements possible while still meeting the specified percent value.  
   
 ## Examples  
- The following example creates a prediction query against the Association model that you built in the [Basic Data Mining Tutorial](http://msdn.microsoft.com/library/6602edb6-d160-43fb-83c8-9df5dddfeb9c).  
+ The following example creates a prediction query against the Association model that you built in the [Basic Data Mining Tutorial](https://msdn.microsoft.com/library/6602edb6-d160-43fb-83c8-9df5dddfeb9c).  
   
  To understand how BottomPercent works, it may be helpful to first execute a prediction query that returns only the nested table.  
   
diff --git a/docs/dmx/bottomsum-dmx.md b/docs/dmx/bottomsum-dmx.md
@@ -32,7 +32,7 @@ BottomSum(<table expression>, <rank expression>, <sum>)
  The **BottomSum** function returns the bottom-most rows in increasing order of rank. The rank is based on the evaluated value of the \<rank expression> argument for each row, such that the sum of the \<rank expression> values is at least the given total that is specified by the \<sum> argument. **BottomSum** returns the smallest number of elements possible while still meeting the specified sum value.  
   
 ## Examples  
- The following example creates a prediction query against the Association model that you build by using the [Basic Data Mining Tutorial](http://msdn.microsoft.com/library/6602edb6-d160-43fb-83c8-9df5dddfeb9c).  
+ The following example creates a prediction query against the Association model that you build by using the [Basic Data Mining Tutorial](https://msdn.microsoft.com/library/6602edb6-d160-43fb-83c8-9df5dddfeb9c).  
   
  To understand how BottomSum works, it might be helpful to first execute a prediction query that returns only the nested table.  
   
diff --git a/docs/dmx/create-mining-model-dmx.md b/docs/dmx/create-mining-model-dmx.md
@@ -121,7 +121,7 @@ CREATE MINING MODEL <model> FROM PMML <xml string>
 ## Remarks  
  If you want to create a model that has a built-in testing data set, you should use the statement CREATE MINING STRUCTURE followed by ALTER MINING STRUCTURE. However, not all model types support a holdout data set. For more information, see [CREATE MINING STRUCTURE &#40;DMX&#41;](../dmx/create-mining-structure-dmx.md).  
   
- For a walkthrough of how to create a mining model by using the CREATEMODEL statement, see [Time Series Prediction DMX Tutorial](http://msdn.microsoft.com/library/38ea7c03-4754-4e71-896a-f68cc2c98ce2).  
+ For a walkthrough of how to create a mining model by using the CREATEMODEL statement, see [Time Series Prediction DMX Tutorial](https://msdn.microsoft.com/library/38ea7c03-4754-4e71-896a-f68cc2c98ce2).  
   
 ## Naive Bayes Example  
  The following example uses the [!INCLUDE[msCoName](../includes/msconame-md.md)] Naive Bayes algorithm to create a new mining model. The Bike Buyer column is defined as the predictable attribute.  
diff --git a/docs/dmx/dmx-tutorials-analysis-services-data-mining.md b/docs/dmx/dmx-tutorials-analysis-services-data-mining.md
@@ -16,11 +16,11 @@ manager: kfile
   The following tutorials introduce you to the use of Data Mining Extensions (DMX) statements with data mining structures and models.  
   
 ## In This Section  
- [Bike Buyer DMX Tutorial](http://msdn.microsoft.com/library/4b634cc1-86dc-42ec-9804-a19292fe8448)  
+ [Bike Buyer DMX Tutorial](https://msdn.microsoft.com/library/4b634cc1-86dc-42ec-9804-a19292fe8448)  
   
  In this tutorial, you will learn how to create, train, and explore mining models by using the DMX query language. You will then use these mining models to create predictions about whether a customer is likely to purchase a specific product.  
   
- [Market Basket DMX Tutorial](http://msdn.microsoft.com/library/6e262a1d-c89e-4033-8368-46cf25168ef5)  
+ [Market Basket DMX Tutorial](https://msdn.microsoft.com/library/6e262a1d-c89e-4033-8368-46cf25168ef5)  
   
  In this tutorial, you will learn how to create a mining model that predicts which products tend to be purchased at the same time. This tutorial also demonstrates the use of nested tables in data mining.  
   
@@ -38,6 +38,6 @@ manager: kfile
   
 ## See Also  
  [Structure and Usage of DMX Prediction Queries](../dmx/structure-and-usage-of-dmx-prediction-queries.md)   
- [Basic Data Mining Tutorial](http://msdn.microsoft.com/library/6602edb6-d160-43fb-83c8-9df5dddfeb9c)  
+ [Basic Data Mining Tutorial](https://msdn.microsoft.com/library/6602edb6-d160-43fb-83c8-9df5dddfeb9c)  
   
   
diff --git a/docs/dmx/exists-dmx.md b/docs/dmx/exists-dmx.md
@@ -37,7 +37,7 @@ EXISTS(<subquery>)
 ## Examples  
  You can use EXISTS and NOT EXISTS to check for conditions in a nested table. This is useful when creating a filter that controls the data used to train or test a data mining model. For more information, see [Filters for Mining Models &#40;Analysis Services - Data Mining&#41;](../analysis-services/data-mining/filters-for-mining-models-analysis-services-data-mining.md).  
   
- The following example is based on the `[Association]` mining structure and mining model that you created in the [Basic Data Mining Tutorial](http://msdn.microsoft.com/library/6602edb6-d160-43fb-83c8-9df5dddfeb9c). The query returns only those cases where the customer purchased at least one patch kit.  
+ The following example is based on the `[Association]` mining structure and mining model that you created in the [Basic Data Mining Tutorial](https://msdn.microsoft.com/library/6602edb6-d160-43fb-83c8-9df5dddfeb9c). The query returns only those cases where the customer purchased at least one patch kit.  
   
 ```  
 SELECT * FROM [Association].CASES  
diff --git a/docs/dmx/istestcase-dmx.md b/docs/dmx/istestcase-dmx.md
@@ -36,7 +36,7 @@ IsTestCase()
  To return cases that are part of the training data set, use the function [IsTrainingCase &#40;DMX&#41;](../dmx/istrainingcase-dmx.md).  
   
 ## Examples  
- The following example uses the `Targeted Mailing` mining structure that is created in the [Basic Data Mining Tutorial](http://msdn.microsoft.com/library/6602edb6-d160-43fb-83c8-9df5dddfeb9c). The query returns all the cases in the structure that are used for testing.  
+ The following example uses the `Targeted Mailing` mining structure that is created in the [Basic Data Mining Tutorial](https://msdn.microsoft.com/library/6602edb6-d160-43fb-83c8-9df5dddfeb9c). The query returns all the cases in the structure that are used for testing.  
   
 ```  
 SELECT *  
diff --git a/docs/dmx/istrainingcase-dmx.md b/docs/dmx/istrainingcase-dmx.md
@@ -36,7 +36,7 @@ IsTrainingCase()
  To return cases that are part of the test data set, use the function [IsTestCase &#40;DMX&#41;](../dmx/istestcase-dmx.md).  
   
 ## Examples  
- The following example uses the clustering data mining model from the targeted mailing scenario in the [Basic Data Mining Tutorial](http://msdn.microsoft.com/library/6602edb6-d160-43fb-83c8-9df5dddfeb9c). The query returns only those cases that were used for training the mining model. Moreover, the training cases are restricted to customers younger than 40.  
+ The following example uses the clustering data mining model from the targeted mailing scenario in the [Basic Data Mining Tutorial](https://msdn.microsoft.com/library/6602edb6-d160-43fb-83c8-9df5dddfeb9c). The query returns only those cases that were used for training the mining model. Moreover, the training cases are restricted to customers younger than 40.  
   
 ```  
 SELECT *  
diff --git a/docs/dmx/predicttimeseries-dmx.md b/docs/dmx/predicttimeseries-dmx.md
@@ -79,7 +79,7 @@ PredictTimeSeries(<scalar column reference>, n-start, n-end, REPLACE_MODEL_CASES
   
 -   The third example shows how to use the EXTEND_MODEL_CASES parameter to update a mining model with fresh data.  
   
- To learn more about working with time series models, see the data mining tutorial, [Lesson 2: Building a Forecasting Scenario &#40;Intermediate Data Mining Tutorial&#41;](http://msdn.microsoft.com/library/9a988156-c900-4c22-97fa-f6b0c1aea9e2) and [Time Series Prediction DMX Tutorial](http://msdn.microsoft.com/library/38ea7c03-4754-4e71-896a-f68cc2c98ce2).  
+ To learn more about working with time series models, see the data mining tutorial, [Lesson 2: Building a Forecasting Scenario &#40;Intermediate Data Mining Tutorial&#41;](https://msdn.microsoft.com/library/9a988156-c900-4c22-97fa-f6b0c1aea9e2) and [Time Series Prediction DMX Tutorial](https://msdn.microsoft.com/library/38ea7c03-4754-4e71-896a-f68cc2c98ce2).  
   
 > [!NOTE]  
 >  You might obtain different results from your model; the results of the examples below are provided only to illustrate the result format.  
@@ -113,7 +113,7 @@ OR [Model Region] = 'M200 Pacific'
 ### Example 2: Adding New Data and Using REPLACE_MODEL_CASES  
  Suppose you find that the data was incorrect for a particular region, and want to use the patterns in the model, but to adjust the predictions to match the new data. Or, you might find that another region has more reliable trends and you want to apply the most reliable model to data from a different region.  
   
- In such scenarios, you can use the REPLACE_MODEL_CASES parameter and specify a new set of data to use as historical data. That way, the projections will be based on the patterns in the specified model, but will continue smoothly from the end of the new data points. For a complete walkthrough of this scenario, see [Advanced Time Series Predictions &#40;Intermediate Data Mining Tutorial&#41;](http://msdn.microsoft.com/library/b614ebdb-07ca-44af-a0ff-893364bd4b71).  
+ In such scenarios, you can use the REPLACE_MODEL_CASES parameter and specify a new set of data to use as historical data. That way, the projections will be based on the patterns in the specified model, but will continue smoothly from the end of the new data points. For a complete walkthrough of this scenario, see [Advanced Time Series Predictions &#40;Intermediate Data Mining Tutorial&#41;](https://msdn.microsoft.com/library/b614ebdb-07ca-44af-a0ff-893364bd4b71).  
   
  The following PREDICTION JOIN query illustrates the syntax for replacing data and making new predictions. For the replacement data, the example retrieves the value of the Amount and Quantity columns and multiplies each by two:  
   
@@ -188,7 +188,7 @@ WHERE ([Model Region] = 'M200 Europe'
   
 -   Returns new predictions for the remaining three time slices based on the newly expanded model.  
   
- The following table lists the results of the Example 2 query. Notice that the first two values returned for M200 Europe are exactly the same as the new values that you provided. This behavior is by design; if you want to start predictions after the end of the new data, you must specify a starting and ending time step. For an example of how to do this, see [Lesson 5: Extending the Time Series Model](http://msdn.microsoft.com/library/7aad4946-c903-4e25-88b9-b087c20cb67d).  
+ The following table lists the results of the Example 2 query. Notice that the first two values returned for M200 Europe are exactly the same as the new values that you provided. This behavior is by design; if you want to start predictions after the end of the new data, you must specify a starting and ending time step. For an example of how to do this, see [Lesson 5: Extending the Time Series Model](https://msdn.microsoft.com/library/7aad4946-c903-4e25-88b9-b087c20cb67d).  
   
  Also, notice that you did not supply new data for the Pacific region. Therefore, [!INCLUDE[ssASnoversion](../includes/ssasnoversion-md.md)] returns new predictions for all five time slices.  
   
diff --git a/docs/dmx/select-distinct-from-model-dmx.md b/docs/dmx/select-distinct-from-model-dmx.md
@@ -57,7 +57,7 @@ SELECT [FLATTENED] DISTINCT [TOP <n>] <expression list> FROM <model>
 |Continuous|The midpoint for the values in the column.|  
   
 ## Discrete Column Example  
- The following code sample is based on the `[TM Decision Tree]` model that you create in the [Basic Data Mining Tutorial](http://msdn.microsoft.com/library/6602edb6-d160-43fb-83c8-9df5dddfeb9c). The query returns the unique values that exist in the discrete column, `Gender`.  
+ The following code sample is based on the `[TM Decision Tree]` model that you create in the [Basic Data Mining Tutorial](https://msdn.microsoft.com/library/6602edb6-d160-43fb-83c8-9df5dddfeb9c). The query returns the unique values that exist in the discrete column, `Gender`.  
   
 ```  
 SELECT DISTINCT [Gender]  
diff --git a/docs/dmx/select-from-model-cases-dmx.md b/docs/dmx/select-from-model-cases-dmx.md
@@ -56,7 +56,7 @@ SELECT [FLATTENED] [TOP <n>] <expression list> FROM <model>.CASES
  Using the [IsInNode &#40;DMX&#41;](../dmx/isinnode-dmx.md) function in the **WHERE** clause returns only cases that are associated with the node that is specified by the NODE_UNIQUE_NAME column of the schema rowset.  
   
 ## Examples  
- The following examples are based on the mining structure Targeted Mailing, which is based on the [!INCLUDE[ssSampleDBDWobject](../includes/sssampledbdwobject-md.md)]database and its associated mining models. For more information, see [Basic Data Mining Tutorial](http://msdn.microsoft.com/library/6602edb6-d160-43fb-83c8-9df5dddfeb9c).  
+ The following examples are based on the mining structure Targeted Mailing, which is based on the [!INCLUDE[ssSampleDBDWobject](../includes/sssampledbdwobject-md.md)]database and its associated mining models. For more information, see [Basic Data Mining Tutorial](https://msdn.microsoft.com/library/6602edb6-d160-43fb-83c8-9df5dddfeb9c).  
   
 ### Example 1: Drillthrough to Model Cases and Structure Columns  
  The following example returns the columns for all the cases that were used to test the Targeted Mailing model. If the mining structure on which the model is built does not have a holdout test data set, this query would return 0 cases. You can use the expression list to return only the columns that you need.  
diff --git a/docs/dmx/select-from-structure-cases.md b/docs/dmx/select-from-structure-cases.md
@@ -60,7 +60,7 @@ SELECT StructureColumn('<column name>') FROM <model>.CASES
  Therefore, to protect sensitive data or personal information, you should construct your data source view to mask personal information, and grant **AllowDrillthrough** permission on a mining structure or mining model only when necessary.  
   
 ## Examples  
- The following examples are based on the mining structure, Targeted Mailing, which is based on the [!INCLUDE[ssAWDWsp](../includes/ssawdwsp-md.md)] database, and the associated mining models. For more information, see [Basic Data Mining Tutorial](http://msdn.microsoft.com/library/6602edb6-d160-43fb-83c8-9df5dddfeb9c).  
+ The following examples are based on the mining structure, Targeted Mailing, which is based on the [!INCLUDE[ssAWDWsp](../includes/ssawdwsp-md.md)] database, and the associated mining models. For more information, see [Basic Data Mining Tutorial](https://msdn.microsoft.com/library/6602edb6-d160-43fb-83c8-9df5dddfeb9c).  
   
 ### Example 1: Drill through to Structure Cases  
  The following example returns a list of the 500 oldest customers in the mining structure, Targeted Mailing. The query returns all the columns in the mining model, but restricts the rows to those who purchased a bike, and orders them by age. You can also edit the expression list to return only the columns that you need.  
diff --git a/docs/dmx/select-into-dmx.md b/docs/dmx/select-into-dmx.md
@@ -48,7 +48,7 @@ FROM <existing model>
  The **WITH DRILLTHROUGH** clause enables drillthrough on the new mining model. Drillthrough can only be enabled when you create the model.  
   
 ## Example 1: Altering the Parameters of the Model  
- The following example creates a new mining model based on an existing mining model, `TM_Clustering`, which you create in the [Basic Data Mining Tutorial](http://msdn.microsoft.com/library/6602edb6-d160-43fb-83c8-9df5dddfeb9c). In the new model, the CLUSTER_COUNT parameter is modified so that a maximum of five clusters will exist in the new model. In contrast, the existing model uses the default value, which is 10.  
+ The following example creates a new mining model based on an existing mining model, `TM_Clustering`, which you create in the [Basic Data Mining Tutorial](https://msdn.microsoft.com/library/6602edb6-d160-43fb-83c8-9df5dddfeb9c). In the new model, the CLUSTER_COUNT parameter is modified so that a maximum of five clusters will exist in the new model. In contrast, the existing model uses the default value, which is 10.  
   
 ```  
 SELECT * INTO [New_Clustering]  
diff --git a/docs/dmx/topcount-dmx.md b/docs/dmx/topcount-dmx.md
@@ -36,7 +36,7 @@ TopCount(<table expression>, <rank expression>, <count>)
  However, there are situations where you might still need to use TopCount. For example, DMX does not support the **TOP** qualifier in a sub-select statement. The [PredictHistogram &#40;DMX&#41;](../dmx/predicthistogram-dmx.md) function also does not support the addition of **TOP**.  
   
 ## Examples  
- The following examples are prediction queries against the Association model that you build by using the [Basic Data Mining Tutorial](http://msdn.microsoft.com/library/6602edb6-d160-43fb-83c8-9df5dddfeb9c). The queries return the same results, but the first example uses TopCount, and the second example uses the Predict function.  
+ The following examples are prediction queries against the Association model that you build by using the [Basic Data Mining Tutorial](https://msdn.microsoft.com/library/6602edb6-d160-43fb-83c8-9df5dddfeb9c). The queries return the same results, but the first example uses TopCount, and the second example uses the Predict function.  
   
  To understand how TopCount works, it may be helpful to first execute a prediction query that returns only the nested table.  
   
diff --git a/docs/dmx/toppercent-dmx.md b/docs/dmx/toppercent-dmx.md
diff --git a/docs/dmx/topsum-dmx.md b/docs/dmx/topsum-dmx.md
