Add link to queries in 3 TS and ML docs

orspod · orspod · commit 15e25c92774f · 2019-12-03T15:33:45.000+02:00
diff --git a/articles/data-explorer/anomaly-detection.md b/articles/data-explorer/anomaly-detection.md
@@ -3,7 +3,7 @@ title: Time series anomaly detection and forecasting in Azure Data Explorer
 description: Learn how to analyze time series data for anomaly detection and forecasting using Azure Data Explorer.
 author: orspod
 ms.author: orspodek
-ms.reviewer: jasonh
+ms.reviewer: adieldar
 ms.service: data-explorer
 ms.topic: conceptual
 ms.date: 04/24/2019
@@ -26,6 +26,8 @@ To create a decomposition model, use the function [`series_decompose()`](/azure/
 
 For example, you can decompose traffic of an internal web service by using the following query:
 
+**\[**[**Click to run query**](https://dataexplorer.azure.com/clusters/help/databases/Samples?query=H4sIAAAAAAAAA3WQ3WrDMAyF7/sUukvCnDXJGIOVPEULuwxqoixm/gm2+jf28JObFjbYrmyho3M+yRCD1a5jaGFAJtaW8qaqX8qqLqvnYrMySYHnvxRNWT1B07xW1U03JFEzbVYDWd9Z/KAuUtAUm9UXpLJcSnAH2+LxPZe3AO9gJ6ZbRjvDGLy9EbG/BUemOXnvLxD1AOJ1mijQtWhbyHbbOgOA9RogkqGeAaXn3g1BooVb6OiDNHpD6CjAUccDGv2JrL0TSzozuQHyPYqHdqRkDKN3aBRwkJaCQJIoQ4VsuXh2A/Xezj5SWkVBWSvI0vSoOSsWpLtEpyDwY4KTW8nnJ5ws+2+eAhSyOxjkd+HDVVcIfHplp2TYTxgYTpqnnDUbarM32gPO86PY4jjqfmGw3vGkftNlCi5xNprbWW5kYvENQQnqDh8CAAA=)**\]**
+
 ```kusto
 let min_t = datetime(2017-01-05);
 let max_t = datetime(2017-02-03 22:00);
@@ -51,6 +53,8 @@ The function [`series_decompose_anomalies()`](/azure/kusto/query/series-decompos
 
 The following query allows you to detect anomalies in internal web service traffic:
 
+**\[**[**Click to run query**](https://dataexplorer.azure.com/clusters/help/databases/Samples?query=H4sIAAAAAAAAA3WR3W7CMAyF73mKI25KpRbaTmjSUJ8CpF1WoXVptPxUifmb9vBLoGO7GFeR7ePv2I4ihpamYdToBBNLTYuqKF/zosyLdbqZqagQl/8UVV68oKreimLSdVFUDZtZR9o2WnxQ48lJ8tXsCzHM7yHMUdfidFiEN4U12AXoloUe0Turp4nYTsaeaYzs/RVedgis80CObkFdI9ltywTAagV4UtQyRKiZgyLEaTGZ9taFQqtIGHI4SX8USn4KltYEJF2YTIeFMFaHPPkMvrWOMuxFoEpDaVjujmo6aq0erafmIY+7ZCiX6wx5mSGJHb3kJA1sF8jB8q69toNwjLPkYfGTseqoja//eLNkRXXyTnuIcVyCneh72cL2YQdtDQ8ZHvIkDcsfPWH+3AvPvObx0FMXD/RLhfDYW9VhtNKwj/8U69M1b2S//AbRUQMWQQIAAA==)**\]**
+
 ```kusto
 let min_t = datetime(2017-01-05);
 let max_t = datetime(2017-02-03 22:00);
@@ -74,6 +78,8 @@ The function [`series_decompose_forecast()`](/azure/kusto/query/series-decompose
 
 The following query allows you to predict next week's web service traffic:
 
+**\[**[**Click to run query**](https://dataexplorer.azure.com/clusters/help/databases/Samples?query=H4sIAAAAAAAAA22QzW6DMBCE73mKuQFqKISqitSIW98gkXpEDl5iK9hG9uanUR++dqE99YRGO8x845EYRtuO0UIKJtaG8qbebMt6U9avxW41Joe4/+doyvoFTfNW14tPJlOjZqGc1w9n263crSQZ1xlxpi6Q1xSa1ReSLGcJezGtuJ7y+C3gLA6xZM/CTBi8MwshuxnkaUlGYJpS5/ETQUvEzJsiTz+ibZEd9psMQFUBgUbqGSLe7GkkpBVYygfn46EfSVjyuOpwEaN+CNbOxki6M1mZTNSLkAbOv3WSemcmF6j7vSX8dcTUlvOFsZJcFDHFx4wYnmp7JTzjplnlrHmkNvugI8Q0PYO9GAbdww0RyDjLav1XHLnBimAjEG5E5zQ7vRP284x36hOOTtxZ8Q3The8P2QEAAA==)**\]**
+
 ```kusto
 let min_t = datetime(2017-01-05);
 let max_t = datetime(2017-02-03 22:00);
@@ -83,7 +89,7 @@ demo_make_series2
 | make-series num=avg(num) on TimeStamp from min_t to max_t+horizon step dt by sid 
 | where sid == 'TS1'   //  select a single time series for a cleaner visualization
 | extend forecast = series_decompose_forecast(num, toint(horizon/dt))
-| render timechart with(title='Web app. traffic of a month, forecasting the next week by Time Series Decmposition')
+| render timechart with(title='Web app. traffic of a month, forecasting the next week by Time Series Decomposition')
 ```
 
 ![Time series forecasting](media/anomaly-detection/series-forecasting.png)
@@ -97,6 +103,8 @@ Azure Data Explorer query language syntax enables a single call to process multi
 
 The following query shows the processing of three time series simultaneously:
 
+**\[**[**Click to run query**](https://dataexplorer.azure.com/clusters/help/databases/Samples?query=H4sIAAAAAAAAA21Qy26DMBC85yvmFlChcUirSI34ikTqETl4KVawjfDmqX587UCaHuqLtePxPLYjhtG2YpRQkom1oaQQy3Uulrl4TzezLjLk5T9GkYsViuJDiImnIqlox6F1g745W67VZqbIuMrIA1WeBk2+mH0jjvk4wh5NKU9fSbhTOItdMNmyND2awZkpIbsxyMukDM/UR8/9FV6rIEkXJqvgmsYTl7X0lISHspzvtqt5hjdxPxkeYBHA4gGKFMBiAUilIAfWja617CY1NG4ASX/FSfuj7PRNsg4ZXANz7Fj3HSGuBmOjZ5hYbcSqIBwbZpNk+iQFcQpx4/omrqLamd55qh5v41d22nIybWChOI0qQ9Cg4e5ftyE6zprbhDV3VM4/aQ/Z96/gQTahU4wsYZzlNvs11vYL3BJsCIQz0eHed/W30jz9AUEBI0ktAgAA)**\]**
+
 ```kusto
 let min_t = datetime(2017-01-05);
 let max_t = datetime(2017-02-03 22:00);
diff --git a/articles/data-explorer/machine-learning-clustering.md b/articles/data-explorer/machine-learning-clustering.md
@@ -3,7 +3,7 @@ title: Machine learning capability in Azure Data Explorer
 description: Use machine learning clustering for Root Cause Analysis in Azure Data Explorer.
 author: orspod
 ms.author: orspodek
-ms.reviewer: jasonh
+ms.reviewer: adieldar
 ms.service: data-explorer
 ms.topic: conceptual
 ms.date: 04/29/2019
@@ -21,6 +21,8 @@ Azure Data Explorer has three Machine Learning plugins: [`autocluster`](/azure/k
 
 A common scenario includes a data set selected by a specific criteria such as time window that exhibits anomalous behavior, high temperature device readings, long duration commands, and top spending users. We would like a simple and fast way to find common patterns (segments) in the data. Patterns are a subset of the data set whose records share the same values over multiple dimensions (categorical columns). The following query builds and shows a time series of service exceptions over a week in ten-minute bins:
 
+**\[**[**Click to run query**](https://dataexplorer.azure.com/clusters/help/databases/Samples?query=H4sIAAAAAAAAA5XPsaoCQQyF4d6nCFa7oHCtZd9B0F6G8ajByWTJZHS5+PDOgpVgYRn485EkOAnno9NAriWGFKw7QfQYUy0O43zZ0JNKFQnG/5jrbmeIXHBgwd6DjH2/JVqk2QrTL1aYvlifa4tni29YlzaiUK4yRK3Zu54006dBZ1N5/+X6PqpRI23+pFGGfIKRtz5egzk92K+dsycMyz3szhGEKWJ01lxI760O9ABuq0bMcvV2hqFoqnOz7F9BdSHlSgEAAA==)**\]**
+
 ```kusto
 let min_t = toscalar(demo_clustering1 | summarize min(PreciseTimeStamp));  
 let max_t = toscalar(demo_clustering1 | summarize max(PreciseTimeStamp));  
@@ -35,6 +37,8 @@ The service exception count correlates with the overall service traffic. You can
 
 The second spike in the data occurs on Tuesday afternoon. The following query is used to further diagnose this spike. Use the query to redraw the chart around the spike in higher resolution (eight hours in one-minute bins) to verify whether it’s a sharp spike, and view its borders.
 
+**\[**[**Click to run query**](https://dataexplorer.azure.com/clusters/help/databases/Samples?query=H4sIAAAAAAAAAyXNwQrCMBAE0Hu/YvHUooWkghSl/yDoyUsJyWpCk2xJNnjx403pbeYwbzwyBBdnnoxiZBewHYS89GLshzNIeRWiuzUGA83al8yYXPzI5gdBLdjnWjFDLGHSVCK3HVCEe0LtMj4r9mAVVngnCvsLMO3hOFqo2goyVCxhNJhgu9dWJYavY9uyY4/T4UV1XVm2CEM0kFe34AnkBhXGOs7kCzuKh+4P3/XM5M8AAAA=)**\]**
+
 ```kusto
 let min_t=datetime(2016-08-23 11:00);
 demo_clustering1
@@ -46,6 +50,8 @@ demo_clustering1
 
 We see a narrow two-minute spike from 15:00 to 15:02. In the following query, count the exceptions in this two-minute window:
 
+**\[**[**Click to run query**](https://dataexplorer.azure.com/clusters/help/databases/Samples?query=H4sIAAAAAAAAA8tJLVHIzcyLL0hNzI4vsU1JLEktycxN1TAyMDTTNbDQNTJWMDS1MjDQtObKASlNrCCk1AioNCU1Nz8+Oae0uCS1KDMv3ZCrRqE8I7UoVSGgKDU5szg1BKgvuCQxt0AhKbWkPDU1TwPhBj09hCWaQI3J+aV5JQACnQoRpwAAAA==)**\]**
+
 ```kusto
 let min_peak_t=datetime(2016-08-23 15:00);
 let max_peak_t=datetime(2016-08-23 15:02);
@@ -60,6 +66,8 @@ demo_clustering1
 
 In the following query, sample 20 exceptions out of 972:
 
+**\[**[**Click to run query**](https://dataexplorer.azure.com/clusters/help/databases/Samples?query=H4sIAAAAAAAAA4XOsQrCMBSF4b1Pccd2aLmJKKL4DoLu4doeNDSJJb1SBx/eOHV0/37OCVCKPrkJMjo9DaJQH1FbNruW963dkNkemJtjFX5U3v+oLXRAfLo+vGZF9uluqg8tD2TQOaP3M66lu6jEiW7QBUj1+qHr1pGmhCojyPIX7QHvzakAAAA=)**\]**
+
 ```kusto
 let min_peak_t=datetime(2016-08-23 15:00);
 let max_peak_t=datetime(2016-08-23 15:02);
@@ -95,6 +103,8 @@ demo_clustering1
 
 Even though there are less than a thousand exceptions, it’s still hard to find common segments, as there are multiple values in each column. You can use [`autocluster()`](/azure/kusto/query/autoclusterplugin) plugin to instantly extract a small list of common segments and find the interesting clusters within the spike's two minutes as seen in the following query:
 
+**\[**[**Click to run query**](https://dataexplorer.azure.com/clusters/help/databases/Samples?query=H4sIAAAAAAAAA4WOsQrCMBRF937FG5OhJYkoovQfBN1DbC8aTNqSvlgHP94IQkf3c+65AUzRD3aCe1hue8dgHyGM0rta7WuzIb09KCWPVfii7vUPNQXtEUfbhTwzkh9uunrTckcCnRI6P+NSvDO7ONEVvACDWD80zRqRRcTThVxa5DKPv00hP81KL1+4AAAA)**\]**
+
 ```kusto
 let min_peak_t=datetime(2016-08-23 15:00);
 let max_peak_t=datetime(2016-08-23 15:02);
@@ -119,6 +129,8 @@ Autocluster uses a proprietary algorithm for mining multiple dimensions and extr
 
 You can also use the [`basket()`](/azure/kusto/query/basketplugin) plugin as seen in the following query:
 
+**\[**[**Click to run query**](https://dataexplorer.azure.com/clusters/help/databases/Samples?query=H4sIAAAAAAAAA4WOsQ6CMBgGd57iH9sB0tZojMZ3MNG9KfBFG1og7Y84+PDWidH9LncBTNGPdoYbLF96x2AfIYzSh1oda7MjvT8pJc9V+KHu/Q81Be0RJ9uFJTOSHx+6+tD6RAJdEzqfcS/ejV2cqQWvwCi2h6bZIrKIeLmwlBa1Lg9gIb9KJv2TswAAAA==)**\]**
+
 ```kusto
 let min_peak_t=datetime(2016-08-23 15:00);
 let max_peak_t=datetime(2016-08-23 15:02);
@@ -153,6 +165,8 @@ The [`diffpatterns()`](/azure/kusto/query/diffpatternsplugin) plugin overcomes t
 
 In the query below, we use `diffpatterns` to find interesting clusters within the spike's two minutes, which are different than clusters within the baseline. We define the baseline window as the eight minutes before 15:00 (when the spike started). We also need to extend by a binary column (AB) specifying whether a specific record belongs to the baseline or to the anomalous set. `Diffpatterns` implements a supervised learning algorithm, where the two class labels were generated by the anomalous versus the baseline flag (AB).
 
+**\[**[**Click to run query**](https://dataexplorer.azure.com/clusters/help/databases/Samples?query=H4sIAAAAAAAAA42QzU+DQBDF7/wVcwOi5UtrmhJM4OzBRO9kWqbtpssuYacfGv94t0CrxFTd02by5jfvPUkMtVBlQ7gtOauQiUVNXhLFD5NoNknuIJ7Oo8hPHXmS4vEvaXKWWuoCDUmh6Jr8fj79Tv6HfOanEIbwRLgnQFhjAwviA5EC3hCcCYCq6gamEVsC1oB7LfoRt6iMYKEVvGtFQXfeNFKc7mXe2MjNVzl+mARR6lRU63Ipd4apFWodOx9w2FBL4D23tBSGXi3mhbG+OPPGVQTB+ITvg24dGN7vlN5JTxhc+dYAHZls4LzIxGr1k/B4iXcLbq50jfLNtd9i8OB2jD3KnW0dKstokG08Zby8uLbyCfX/tG46AgAA)**\]**
+
 ```kusto
 let min_peak_t=datetime(2016-08-23 15:00);
 let max_peak_t=datetime(2016-08-23 15:02);
@@ -178,6 +192,8 @@ demo_clustering1
 
 The most dominant segment is the same segment that was extracted by `autocluster`, its coverage on the two-minute anomalous window is also 65.74%. But its coverage on the eight-minute baseline window is only 1.7%. The difference is 64.04%. This difference seems to be related to the anomalous spike. You can verify this assumption by splitting the original chart into the records belonging to this problematic segment versus the other segments as seen in the query below:
 
+**\[**[**Click to run query**](https://dataexplorer.azure.com/clusters/help/databases/Samples?query=H4sIAAAAAAAAA5WRsWrDMBCG9zzF4cmGGuJUjh2Ktw7tUkLTzuEsnRNRnRQkuSQlD185yRTo0EWIO913/J8MRWBttxE6iC5INOhzRey20owhktd2V8EZwsiMXv/Q9Dpfe5I60Idm2kTkQ1E8AczMxMLjf1h4/IN1PzY7Ax0jWQWBdomvhyF/p512FroOMsIxA0zdTdpKn1bHSzmMzbX8TAfjTkw2vqpLp69VpYQaatEogXOBsqrbtl5WDake6yabXWjkv7WkFxeuPGqG5VzWqhQrIUqx6B/L1WKB6aBViy01imT2ANnau94QT9c35xlNVqQAjF9UhpSHAtiRO+lGG/MCUoZ7CTB4x7ePie5mNbk4QDVn6E+ThUT0SQh5iGlM7tHHX4WFgLHOAQAA)**\]**
+
 ```kusto
 let min_t = toscalar(demo_clustering1 | summarize min(PreciseTimeStamp));  
 let max_t = toscalar(demo_clustering1 | summarize max(PreciseTimeStamp));  
diff --git a/articles/data-explorer/time-series-analysis.md b/articles/data-explorer/time-series-analysis.md
@@ -3,7 +3,7 @@ title: Analyze time series data using Azure Data Explorer
 description: Learn how to analyze time series data in the cloud using Azure Data Explorer.
 author: orspod
 ms.author: orspodek
-ms.reviewer: mblythe
+ms.reviewer: adieldar
 ms.service: data-explorer
 ms.topic: conceptual
 ms.date: 04/07/2019
@@ -22,6 +22,8 @@ The first step in time series analysis is to partition and transform the origina
 
 The input table *demo_make_series1* contains 600K records of arbitrary web service traffic. Use the command below to sample 10 records:
 
+**\[**[**Click to run query**](https://dataexplorer.azure.com/clusters/help/databases/Samples?query=H4sIAAAAAAAAA0tJzc2Pz03MTo0vTi3KTC02VKhRKAFyFQwNADOyzKUbAAAA)**\]**
+
 ```kusto
 demo_make_series1 | take 10 
 ```
@@ -44,6 +46,8 @@ The resulting table contains a timestamp column, three contextual dimensions col
 
 Since there are no metrics, we can only build a set of time series representing the traffic count itself, partitioned by OS using the following query:
 
+**\[**[**Click to run query**](https://dataexplorer.azure.com/clusters/help/databases/Samples?query=H4sIAAAAAAAAA5XPwQrCMBAE0Hu/Yo4NVLBn6Td4ULyWtV1tMJtIsoEq/XhbC4J48jgw+5h1rBDrW0UDDakjR7HsWUIrdOM2cbScakxIWYSiffJSL49W+KAkd2N2hVsMGv8yaPw2furFhCVu1gifpelC9loa9Hyh7LTZInh8FFiPSP7K5fufap1UoR4Mzg/s04njjEb2PUfofNYNFPUFtJiguAEBAAA=)**\]**
+
 ```kusto
 let min_t = toscalar(demo_make_series1 | summarize min(TimeStamp));
 let max_t = toscalar(demo_make_series1 | summarize max(TimeStamp));
@@ -76,6 +80,8 @@ Filtering is a common practice in signal processing and useful for time series p
     - [`series_iir()`](/azure/kusto/query/series-iirfunction): Applying IIR filter. Used for exponential smoothing and cumulative sum.
 - `Extend` the time series set by adding a new moving average series of size 5 bins (named *ma_num*) to the query:
 
+**\[**[**Click to run query**](https://dataexplorer.azure.com/clusters/help/databases/Samples?query=H4sIAAAAAAAAA5WPQavCMBCE7/6KOSYQ4fXgSfobPDx517C2q4bXpLLZQBV/vKkFQTx5WRh25tvZgRUxJK9ooWPuaCAxPcfRR/pnn1kC5wZ35BIjSbjxbDf7EPlXKV6s3a6GmUHTVwya3hkf9tUds1wvEqnEthtLUmPR85HKoO0PxoQXBSFBKJ3YPP9xSyWH5mxxuGKX/1gqlCfl1Neln5EL3R+DmCodhC9MahqHjXVQKbxMW5NScyzQerA7k+gDa1tswzsBAAA=)**\]**
+
 ```kusto
 let min_t = toscalar(demo_make_series1 | summarize min(TimeStamp));
 let max_t = toscalar(demo_make_series1 | summarize max(TimeStamp));
@@ -95,6 +101,8 @@ ADX supports segmented linear regression analysis to estimate the trend of the t
 
 Example of `series_fit_line()` and  `series_fit_2lines()` functions in a time series query:
 
+**\[**[**Click to run query**](https://dataexplorer.azure.com/clusters/help/databases/Samples?query=H4sIAAAAAAAAA0tJzc2PL04tykwtNuKqUUitKEnNS1GACMSnZZbEG+Vk5qUWa1Rq6iCLggSBYkAdRUD1qUUKIIHkjMSiEoXyzJIMjYrk/JzS3DzbCk0AUIIJ02EAAAA=)**\]**
+
 ```kusto
 demo_series2
 | extend series_fit_2lines(y), series_fit_line(y)
@@ -116,6 +124,8 @@ Many metrics follow seasonal (periodic) patterns. User traffic of cloud services
 
 The following example applies seasonality detection on one month traffic of a web service (2-hour bins):
 
+**\[**[**Click to run query**](https://dataexplorer.azure.com/clusters/help/databases/Samples?query=H4sIAAAAAAAAA0tJzc2PL04tykwtNuaqUShKzUtJLVIoycxNTc5ILCoBAHrjE80fAAAA)**\]**
+
 ```kusto
 demo_series3
 | render timechart 
@@ -129,6 +139,8 @@ demo_series3
 > [!NOTE]
 > It's an anomaly if specific distinct periods don't exist
 
+**\[**[**Click to run query**](https://dataexplorer.azure.com/clusters/help/databases/Samples?query=H4sIAAAAAAAAA12OwQ6CMBBE737FHKmpVtAr39IguwkYyzZ0IZj48TZSLx533szOEAfxieeR0/XwRpzlwb2iilkSShapl5mTQYvd5QvxxJqd1bQEi8vZor6RawaLxsA5FewcOjBKBOP0PXUMXL7lyrCeeIvdRPjrzIw35Qyoe6W2GY4qJMv9yb91xtX0AS7N323BAAAA)**\]**
+
 ```kusto
 demo_series3
 | project (periods, scores) = series_periods_detect(num, 0., 14d/2h, 2) //to detect the periods in the time series
@@ -148,6 +160,8 @@ The function detects daily and weekly seasonality. The daily scores less than th
 
 Arithmetic and logical operations can be done on a time series. Using [series_subtract()](/azure/kusto/query/series-subtractfunction) we can calculate a residual time series, that is, the difference between original raw metric and a smoothed one, and look for anomalies in the residual signal:
 
+**\[**[**Click to run query**](https://dataexplorer.azure.com/clusters/help/databases/Samples?query=H4sIAAAAAAAAA5WQQU/DMAyF7/sVT5waqWjrgRPqb+AAgmPltR6LSNLJcdhA+/G4izRAnLhEerbfl2cHVkSfBkUPnfNIgaSZOM5DpDceMovn3OGMXGIk8Z+8jDdPPvKjUjw4d78KC4NO/2LQ6Tfjz/jqjEXeVolUYj/OJWnjMPGOStB+gznhSoFPEEqv3Fz2aWukFt3eYfuBh/zMYlA+KafJmsOCrPRh56Ux2UL4wKRN1+LOtVApXF/37RTOfioUfvpz2arQqBVS2Q7rtc6wa4wlkPLVCLXIqE7DHvcsXOOh73Hz4tM0HzO6zQ1gDOx8UOvZrtayst0Y7z4babkkYQxMyQbGPYnCiGIxTS/fXGpfwk+n7uQBAAA=)**\]**
+
 ```kusto
 let min_t = toscalar(demo_make_series1 | summarize min(TimeStamp));
 let max_t = toscalar(demo_make_series1 | summarize max(TimeStamp));
@@ -169,6 +183,8 @@ demo_make_series1
 
 The example below shows how these functions can run at scale on thousands of time series in seconds for anomaly detection. To see a few sample telemetry records of a DB service's read count metric over four days run the following query:
 
+**\[**[**Click to run query**](https://dataexplorer.azure.com/clusters/help/databases/Samples?query=H4sIAAAAAAAAA0tJzc2Pz03Mq4wvTi3KTC025KpRKEnMTlUwAQArfAiiGgAAAA==)**\]**
+
 ```kusto
 demo_many_series1
 | take 4 
@@ -184,6 +200,8 @@ demo_many_series1
 
 And simple statistics:
 
+**\[**[**Click to run query**](https://dataexplorer.azure.com/clusters/help/databases/Samples?query=H4sIAAAAAAAAA0tJzc2Pz03Mq4wvTi3KTC025KpRKC7NzU0syqxKVcgrzbVNzi/NK9HQ1FHIzcyLL7EFkhohnr6uwSGOvgEg0cQKkGhiBZIoAEq2dK9VAAAA)**\]**
+
 ```kusto
 demo_many_series1
 | summarize num=count(), min_t=min(TIMESTAMP), max_t=max(TIMESTAMP) 
@@ -196,6 +214,8 @@ demo_many_series1
 
 Building a time series in 1-hour bins of the read metric (total four days * 24 hours = 96 points), results in normal pattern fluctuation:
 
+**\[**[**Click to run query**](https://dataexplorer.azure.com/clusters/help/databases/Samples?query=H4sIAAAAAAAAA5WPMQvCMBSE9/6KGxOoYGfpIOjgUBDtXh7twwabFF6ittIfb2rBQSfHg+8+7joOsMZVATlC72vqSFTDtq8subHyLIZ9hgn+Zi2JefKMq/JQ7M/ltjhqvQGSbrbQ8JeFhm/LTyGZInbl1RIhTI3P6X5ROwp0ikmjd/hYYByE3IXV+1G6TEqRtTqahF3DgmAs1y1JwMOEVo0Rzdf6BbBH5FAHAQAA)**\]**
+
 ```kusto
 let min_t = toscalar(demo_many_series1 | summarize min(TIMESTAMP));  
 let max_t = toscalar(demo_many_series1 | summarize max(TIMESTAMP));  
@@ -210,6 +230,8 @@ The above behavior is misleading, since the single normal time series is aggrega
 
 How many time series can we create?
 
+**\[**[**Click to run query**](https://dataexplorer.azure.com/clusters/help/databases/Samples?query=H4sIAAAAAAAAA0tJzc2Pz03Mq4wvTi3KTC025KpRKC7NzU0syqxKVUiqVPDJT9ZR8C/QUXBxAkol55fmlQAAWEsFxjQAAAA=)**\]**
+
 ```kusto
 demo_many_series1
 | summarize by Loc, Op, DB
@@ -223,6 +245,8 @@ demo_many_series1
 
 Now, we're going to create a set of 18339 time series of the read count metric. We add the `by` clause to the make-series statement, apply linear regression, and select the top two time series that had the most significant decreasing trend:
 
+**\[**[**Click to run query**](https://dataexplorer.azure.com/clusters/help/databases/Samples?query=H4sIAAAAAAAAA5WPsU7DQBBE+3zFdLmTTGHSgFAKUCiQiIKIe2u5rJ0T9l3YWwcH5eO5JBIFVJSzmnmz07Gi96FWzKExOepIzIb7WPcUDnVi8ZxKHJGGvifxX3yym+pp+biu7pcv1t4Bk+5EofFfFBp/U/4EJsdse+eri4QwbdKc9q1ZkNJrVhYx4IcCHyAUWjbnRcXlpQLl1uLtgOfoCqx2BRYPGcyjctjASPoYSLhA6uKObR5waasbr3XnA5tzrc0RjTtcn0hnKyg55KtkDAvU9+y2JIpPr1ujXjueT9cse+8YlVDTeIfVoNQymiiZ5ENSCi4vM3FQxAblzWx2a6f2G2UcBRyWAQAA)**\]**
+
 ```kusto
 let min_t = toscalar(demo_many_series1 | summarize min(TIMESTAMP));  
 let max_t = toscalar(demo_many_series1 | summarize max(TIMESTAMP));  
@@ -237,6 +261,8 @@ demo_many_series1
 
 Display the instances:
 
+**\[**[**Click to run query**](https://dataexplorer.azure.com/clusters/help/databases/Samples?query=H4sIAAAAAAAAA5WPvW4CMRCEe55iSlsyBWkjChApIoESAb21udsQg38O26AD8fDx3SEUJVXKWc18s2M5wxmvM6bIIVVkKYqaXdCO/EUnjobTBDekk3MUzZU7u9i+rl4229nqXcpnYGQ7CrX/olD7m/InMLoV24HHg0RkqtOUzjuxoEzroiSCx4MC4xHJ71j0i9TwksLkS+LjgmWoFN4ahcW8gLnN7GuImI4niqyQbGhYlgFDm/40WVvjWfS1skRyaPDUkXorKFXl2MSw5yr/pN9Z31SyxuhbAQAA)**\]**
+
 ```kusto
 let min_t = toscalar(demo_many_series1 | summarize min(TIMESTAMP));  
 let max_t = toscalar(demo_many_series1 | summarize max(TIMESTAMP));  
