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Tag Archives: Dataset

#PowerBI – Change the data source in your composite model with direct query to AS/ Power BI Dataset

December 29, 2020   Self-Service BI

I have been playing around with the new awesome (preview) feature in the December Power BI Desktop release where we can use DirectQuery for Power BI datasets and Azure Analysis services (link to blogpost)

In my case I combined data from a Power BI dataset, Azure Analysis Services, and a local Excel sheet. The DirectQuery sources was in a test environment.

I then wanted to try this on the actual production datasets and wanted to change the datasources – and was a bit lost on how to do that but luckily found a way that I want to share with you.

Change the source

First you click on Data source settings under Transform data

 #PowerBI – Change the data source in your composite model with direct query to AS/ Power BI Dataset

This will open the dialog for Data source settings and show you the list of Data sources in the current file.

 #PowerBI – Change the data source in your composite model with direct query to AS/ Power BI Dataset

Now you can either right click the data source you want to change

 #PowerBI – Change the data source in your composite model with direct query to AS/ Power BI Dataset

Or click the button “Change Source…”

 #PowerBI – Change the data source in your composite model with direct query to AS/ Power BI Dataset

Depending on your data source different dialogs will appear

This one for my Azure Analysis Services Connection

 #PowerBI – Change the data source in your composite model with direct query to AS/ Power BI Dataset

And this one for Power BI Dataset

 #PowerBI – Change the data source in your composite model with direct query to AS/ Power BI Dataset

And this one for the Local Excel workbook

 #PowerBI – Change the data source in your composite model with direct query to AS/ Power BI Dataset

Hope this can help you to.

Happy new year to you all.

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Use hidden measures and members from #PowerBI dataset in an Excel Pivot table

December 3, 2020   Self-Service BI

When you connect to a Power BI Dataset from Power BI desktop you might have noticed that you can see and use hidden measures and columns in the dataset.

113020 2035 usehiddenme1 Use hidden measures and members from #PowerBI dataset in an Excel Pivot table

But the hidden fields cannot be seen if you browse the dataset in Excel.

113020 2035 usehiddenme2 Use hidden measures and members from #PowerBI dataset in an Excel Pivot table

But that does not mean that you cannot use the fields in Excel – and here is how you can do it.

Using VBA

You can use VBA by creating a macro

113020 2035 usehiddenme3 Use hidden measures and members from #PowerBI dataset in an Excel Pivot table

The code will add the field AddressLine1 from the DImReseller dimension as a Rowfield if the active cell contains a pivotable.

113020 2035 usehiddenme4 Use hidden measures and members from #PowerBI dataset in an Excel Pivot table
Sub AddField()
    Dim pv As PivotTable
        Set pv = ActiveCell.PivotTable
        pv.CubeFields("[DimReseller].[AddressLine1]").Orientation = xlRowField
End Sub

If you want to add a measure/value to the pivotable you need to set change the Orientation property to xlDataFields

113020 2035 usehiddenme5 Use hidden measures and members from #PowerBI dataset in an Excel Pivot table

This means that we now have added two hidden fields from the dataset

113020 2035 usehiddenme6 Use hidden measures and members from #PowerBI dataset in an Excel Pivot table

Add hidden measures using OLAP Tools

You can also add hidden measures using the OLAP Tools and MDX Calculated Measure

113020 2035 usehiddenme7 Use hidden measures and members from #PowerBI dataset in an Excel Pivot table

Simply create a new calculated measure by referencing the hidden measure in the MDX

113020 2035 usehiddenme8 Use hidden measures and members from #PowerBI dataset in an Excel Pivot table

This will add a calculated Measure to the measure group you selected

113020 2035 usehiddenme9 Use hidden measures and members from #PowerBI dataset in an Excel Pivot table

And you can add that to your pivotable

113020 2035 usehiddenme10 Use hidden measures and members from #PowerBI dataset in an Excel Pivot table

Referencing hidden items using CUBE functions

Notice that you can also reference the hidden measures using CUBE functions

113020 2035 usehiddenme11 Use hidden measures and members from #PowerBI dataset in an Excel Pivot table

Simply specify the name of the measure as the member expression in this case as “[Measures].[Sales Profit]”

You can also refer to members from hidden fields using the CUBEMEMBER functions

113020 2035 usehiddenme12 Use hidden measures and members from #PowerBI dataset in an Excel Pivot table

Hope this can help you too.

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Researchers create dataset to advance U.S. Supreme Court gender bias analysis

September 22, 2020   Big Data
 Researchers create dataset to advance U.S. Supreme Court gender bias analysis

Automation and Jobs

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University of Washington language researchers and legal professionals recently created a labeled dataset for detection of interruptions and competitive turn-taking in U.S. Supreme Court oral arguments. They then used the corpus of “turn changes” to train AI models to experiment with ways to automatically classify turn changes as competitive or cooperative as a way to analyze gender bias.

“In-depth studies of gender bias and inequality are critical to the oversight of an institution as influential as the Supreme Court,” reads the paper University of Washington researchers Haley Lepp and Gina-Anne Levow published on preprint repository arXiv one week ago. “We find that as the first person in an exchange, female speakers and attorneys are spoken to more competitively than are male speakers and justices. We also find that female speakers and attorneys speak more cooperatively as the second person in an exchange than do male speakers and justices.”

Attorneys who speak before the Supreme Court are allotted 30 minutes of oral argument and are expected to stop talking when a justice speaks. Linguists have observed men interrupting women routinely in professional environments and other settings.

Turn changes are defined as instances when one person stops speaking and another person starts speaking. Short audio clips of each turn change were annotated as competitive or cooperative by 77 members of the U.S. legal community who identify as an attorney, judge, legal scholar, or law student in their second year or higher. Lepp and Levow’s work focuses on measuring whether the turn change was cooperative or competitive, based on oral argument audio the Supreme Court made available, in part because previous work by Deborah Tannen found that interruptions in speech can be part of regular discourse and that the context of the conversation can be a factor.

The paper devoted to gender bias analysis was published days before the death of Supreme Court Justice Ruth Bader Ginsburg at the age of 87. Ginsburg was the second woman ever appointed to the U.S. Supreme Court. As a litigator for the American Civil Liberties Union (ACLU), Ginsburg successfully argued cases before the Supreme Court that greatly extended women’s rights in the United States. On Wednesday and Thursday, she will be the first woman and the first Jewish person in U.S. history to lie in state at the U.S. Capital building for members of the public to say goodbye. She was the longest-serving female justice in U.S. history.

Although voting has already begun in some parts of the country and Ginsburg pleaded in her final days to let the winner of the presidential election fill her vacancy, President Trump is expected to nominate a pick to fill her seat Friday or Saturday. Two Republican Senators pledged not to vote until the presidential election is decided, but Senate Majority Leader Mitch McConnell said just hours after her death that the president’s nominee will get a vote.

Details of the turn changes corpus dataset follow a 2017 study that used automation to identify the number of interruptions that occurred from 2004-2015. The study “Justice, Interrupted: The Effect of Gender, Ideology and Seniority at Supreme Court Oral Arguments” by Tonja Jacobi and Dylan Schweers found that women are interrupted three times as often as male Supreme Court justices are. Female Supreme Court justices were interrupted by attorneys as well as other Supreme Court justices, led by Anthony Kennedy, Antonin Scalia, and William Rehnquist. Scalia and Stephen Breyer also interrupted each other a lot.

A producer of the podcast More Perfect noticed people repeatedly interrupting Ginsburg, which led to an episode on the subject. Jacobi spoke on the podcast and said Ginsburg developed tactics to adapt to frequent interruptions, first by asking to ask a question, then pivoting to ask questions more like male justices who interrupt.

The episode also highlighted that Justice Sonia Sotomayor was found to speak as often as men in the Jacobi study, but has still drawn criticism from media commentators at times for being aggressive. Gender is pervasive in coverage of Supreme Courts, according to a 2016 analysis of media coverage in five democratic countries. The analysis found that generally women who ask questions like male justices are labeled abrasive, militant, or mean by critics.

Last year, the U.S. Supreme Court introduced a rule that justices will try to give attorneys two minutes to speak without interruption at the start of oral arguments.

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Dataset changes layout depending on value types

August 19, 2020   BI News and Info

I’ve been looking to using datasets for the sometimes nice layout and scrolling possibilities.
However I’ve been trying to find a way to “force” the layout when using nested tables.
I can’t find any and it also seems to depend on the type of the values.
Are there any ways described to force a layout?

As a simple example.

planets = ExampleData[{"Dataset", "Planets"}]

zCiqJ Dataset changes layout depending on value types

ReplacePart[planets, {{"Earth", "Moons", "Moon", "Mass"} ->2, {"Earth", "Moons", "Moon", "Radius"} ->2}]

cKKdu Dataset changes layout depending on value types

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Internal error using part specification with Dataset

April 11, 2020   BI News and Info

While trying to answer this question I encountered an internal error using a Part specification to extract data for specific columns from a dataset.

dsMeetings2 = 
 Dataset@{<|"id" -> 1, "date" -> "1/03/20", "name" -> "subject-1", 
    "100" -> 1, "106" -> 1, "101" -> 1, "105" -> 1, "102" -> 1, 
    "104" -> 0, "108" -> 0, "103" -> 0, "109" -> 0, "111" -> 0|>, <|
    "id" -> 2, "date" -> "8/03/20", "name" -> "subject-2", "100" -> 1,
     "106" -> 1, "101" -> 1, "105" -> 1, "102" -> 0, "104" -> 0, 
    "108" -> 0, "103" -> 1, "109" -> 0, "111" -> 0|>, <|"id" -> 3, 
    "date" -> "15/03/20", "name" -> "subject-3", "100" -> 1, 
    "106" -> 1, "101" -> 0, "105" -> 1, "102" -> 1, "104" -> 1, 
    "108" -> 0, "103" -> 0, "109" -> 0, "111" -> 0|>, <|"id" -> 4, 
    "date" -> "22/03/20", "name" -> "subject-4", "100" -> 1, 
    "106" -> 0, "101" -> 0, "105" -> 0, "102" -> 0, "104" -> 0, 
    "108" -> 1, "103" -> 1, "109" -> 1, "111" -> 0|>, <|"id" -> 5, 
    "date" -> "29/03/20", "name" -> "subject-5", "100" -> 1, 
    "106" -> 0, "101" -> 1, "105" -> 1, "102" -> 1, "104" -> 1, 
    "108" -> 0, "103" -> 0, "109" -> 1, "111" -> 0|>, <|"id" -> 6, 
    "date" -> "5/04/20", "name" -> "subject-6", "100" -> 1, 
    "106" -> 0, "101" -> 0, "105" -> 1, "102" -> 0, "104" -> 0, 
    "108" -> 0, "103" -> 1, "109" -> 1, "111" -> 1|>}

dsMeetings2[All, {1, 3 ;;}]

General::interr2: An unknown internal error occurred. Consult
Internal`$ LastInternalFailure for potential information.

kKCqe Internal error using part specification with Dataset

I can post the long results from Internal`$ LastInternalFailure if that would help.

This unexpectedly works fine

dsMeetings2[All, {1, 3 ;;}] // Normal

so does this

dsMeetings2[All, {1, 4 ;;}]

Any ideas for workarounds without converting to Normal?

I will report this to WRI.

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Find all queries to Azure AS from a Power BI report or dataset

November 14, 2019   Self-Service BI

I recently found out the SSAS team added new trace information to Azure Log analytics. The new information allows you to find out the report and dataset in Power BI that generate each query.

To show you how this works. I captured the queries in AAS using Azure Log analytics as I described here. 

I then make sure I add the column ” ApplicationContext_s” using the query below. This will give me the context the query was running under.

AzureDiagnostics
| where OperationName == "QueryBegin"
| project TimeGenerated, TextData_s, DatabaseName_s, ApplicationContext_s
| where TimeGenerated > ago(2h)

In this case it is the generated by the Power BI service and the column contains the DatasetId and ReportId as we know it in Power BI.

This is very useful if we want to figure out who is generating a particular load on the service.

But there is also another use of this, we can use the DatasetId or ReportId as filter to the query. This allows us to only see the data for the dataset or report we want.

AzureDiagnostics
| where OperationName == "QueryBegin"
| project TimeGenerated, TextData_s, DatabaseName_s, ApplicationContext_s
| where TimeGenerated > ago(2h)
| where ApplicationContext_s contains "b9d53eb4-01e7-4eb5-989b-a416769b9001"

A very useful addition to the logs that might come in handy when doing some debugging.

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Refresh a Power BI dataset that feeds from SharePoint list automatically

October 30, 2019   Self-Service BI

The Power BI team recently shipped some cool new flow options and one of them is really cool. Many times I hear of reports and dataset that fully rely on SharePoint lists. These lists are used by business groups that don’t have the rights to spin up and Azure SQL database or just want to keep a simple list to store their dimensions. SharePoint lists are great for that.

The new flow option allows you to automatically update the dataset whenever a new item is added to the list, that is pretty cool.

Here is how it works. Start at your list in SharePoint and click automate and select create new flow:

A new pane opens to the side, the flow we want is not part of the default list so we select “see your flows”

This opens flow where you can start a new automated flow which we need.

This opens a new flow, give it a name and choose “When an item is created” as trigger for the flow:

Nest pick the list you want to trigger on:

Then finally configure what happens when the trigger is fired. In this case we want to select “Refresh a dataset”.

Next you can select the dataset you want to refresh:

Now every time an item gets added to the SharePoint list the dataset gets updated with the new information. Very cool!

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Refresh your Power BI dataset using Microsoft Flow

October 18, 2019   Self-Service BI

When speaking with customers about how they’ve used Power BI to improve collaborative business processes in their organization, we often hear that Power BI is used to summarize and visualize data that many end users are entering into tools like Excel files, SharePoint lists, or the Common Data Service.  Business processes like managing a team’s budget requests, planning hiring activities, and evaluating marketing campaigns can all fit this pattern.

With these sorts of processes, users often expect Power BI reports to be updated as soon as they enter data in underlying systems. Power BI’s existing fixed refresh schedules are not sufficient to accomplish this and manually refreshing the Power BI dataset each time your access a report adds extra steps and creates confusion.

Today, we are making scheduling of refreshes much more flexible to improve how Power BI works in processes like the ones described above. Specifically, we have added a new Refresh a dataset action to the Power BI connector for Microsoft Flow. Now, you will be able to trigger dataset refreshes based on hundreds of Flow triggers. Whether your trigger is based on changes to items in your SharePoint list or updates to an Excel file in OneDrive or SharePoint Online or a complex day and time schedule, there are dozens of use cases for this action.

Excited? Read on for a full tutorial of using the new refresh a dataset action to automate refreshing a Power BI report based on changes to a SharePoint list. Or, head on over to  Flow to try it out for yourself.

Tutorial: Trigger dataset refresh for SharePoint lists or OneDrive Excel files with Flow and Power BI

In this tutorial, we will create a Flow that triggers a dataset refresh whenever items in a SharePoint list are updated.

Consider an example where you are the office administrator for Northwind Traders and you have been given the responsibility of ensuring the office is well stocked with office supplies, by monitoring inventory, placing new orders, and maintaining the overall budget for the team. You might have a report that looks something like this:

Furthermore, imagine that various employees in the company have access to a SharePoint list to report supplies that are getting low in their parts of the office:

To ensure you have an accurate picture of your supply requests and budget levels, any requests made on this SharePoint list should immediately reflect in the report. Instead of having to manually refresh your dataset each time you or someone else views the report or wait until the next scheduled refresh, you can easily automate this process using the new dataset refresh action in Flow.

To get started, navigate to Flow. Sign in, and then go to My Flows, then choose + New, and select + Automated – from blank from the drop-down. You should see the following:

Go ahead and give your Flow a name and select a SharePoint trigger depending on your use case. In this example, since we want the Flow to trigger when there’s a new row added to the list, choose When an item is created or modified.

Next, click on the + New step button, then enter ‘power bi’ in the search box. You should see a list of actions like below:

Then choose the new Refresh a dataset action.

Now, we’re at the last step of the Flow: Select the name of the workspace, then the name of the of the dataset that you want to trigger the refresh for. In our case, we have chosen Northwind Traders workspace and Northwind Budget Tracker dataset.

And that’s it! Select Save and ensure your Flow is turned on. Now, whenever there is a new supply request made in the SharePoint list, your budget tracker dataset should automatically refresh.

Going back to our budget tracking example for Northwind Traders, if there is a new request made to the Supplies Request SharePoint list that exceeds the budget:

Your flow will trigger and the dataset will automatically update. Considering this individual, placed an order for 500 4K monitors, you will certainly know when you’re out of budget next time you visit the Northwind Budget & Supplies tracker report:

Next steps

  • Try out the feature! Head on over to Flow and use the new refresh action to automate your refreshes
  • Existing limits on refreshes apply when running the refresh dataset action in Microsoft Flow.  For datasets in shared capacity used by Power BI Pro, your refresh action is limited to eight refreshes per day (including refreshes executed via Scheduled Refresh). In Premium capacities, there is not limitation on the number of refreshes per day, although you are limited by the available resources in your capacity.  If there are not sufficient resources, the refresh execution may be throttled until the load is reduced. If this throttling exceeds 1 hour, the refresh will fail.
  • For more ideas on Flows, check out some of our existing Flow templates that allow you to add data from Flow into a Power BI dataset or use data alerts to trigger a Flow whenever the data changes. In the coming weeks, we’ll also be adding several Flow templates with the new refresh a dataset action, including the one featured in the tutorial above – so stay tuned for that.
  • Have any feedback? Have an idea for another way that Power BI can connect to Flow? We’d love to hear it! Please leave comments below or in the Power BI community forums

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Power BI Dataset data source support for Paginated Reports is now available

April 29, 2019   Self-Service BI
social default image Power BI Dataset data source support for Paginated Reports is now available

As discussed in our blog post announcing support for Azure Analysis Services,  we promised to unlock the critical capability of allowing Paginated Reports to be built on the same enterprise semantic model as your interactive reports.  This week for “New Feature Friday”, we’re pleased to announce that Power BI Datasets in premium workspaces are now supported as data sources for your Paginated Reports.  While we announced the ability for authors to create reports using Power BI datasets (via the XMLA endpoint) in Power BI Report Builder three weeks ago, we’ve now unlocked the capability to publish these reports to the service and be shared widely across your organization either via apps or e-mail subscriptions.

Please note the XMLA endpoint will be enabled again in production by April 28th.

Create paginated reports with Power BI Datasets

To create a paginated report using Power BI datasets with Power BI Report Builder, you can select SQL Server Analysis Services as your source and connect to your Power BI Dataset using the instructions discussed in the announcement for XMLA support.  With paginated reports, you may choose one or more Power BI datasets to connect to in the same report, as there are no restrictions on the number of live data sources a report author may use.  Both Power BI datasets using imported data or Direct Query can be used with your paginated reports.  Be sure to read the documentation about creating reports with Report Builder to learn more about authoring paginated reports.

Upload to Power BI

After you have saved your paginated report file, you can then go to the Get Data screen within the service and upload your report to a Premium workspace.  You’re not limited to the same workspace as your Power BI datasets being used for your paginated reports, so you may choose any workspace in your Premium capacity to publish your reports to.  As always, when you consume the semantic model(s), the role-based security (RLS) defined in the model is honored for each user regardless of tool, and this is the same for Paginated reports.

Distribute your paginated reports across the organization

You can easily share your paginated reports you’ve created now against your Power BI datasets either via individual report sharing, apps, or e-mail subscriptions.  You may subscribe yourself or other users to these reports with a full PDF of the report attached.  Make sure you read the documentation to learn more about the specific paginated report capabilities around e-mail subscriptions.

This has been a top feature ask since we first launched paginated reports, and we’re excited to hear your feedback.  We’ll have another post next week announcing more new features for Paginated Reports, including additional data source support.  Have a wonderful weekend!

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Google releases dataset to help AI systems spot fake audio recordings

February 1, 2019   Big Data
 Google releases dataset to help AI systems spot fake audio recordings

When Google announced the Google News Initiative in March 2018, it pledged to release datasets that would help “advance state-of-the-art research” on fake audio detection — that is, clips generated by AI intended to mislead or fool voice authentication systems. Today, it’s making good on that promise.

The Google News team and Google’s AI research division, Google AI, have teamed up to produce a corpus of speech containing “thousands” of phrases spoken by the Mountain View company’s text-to-speech models. Phrases drawn from English newspaper articles are spoken by 68 different synthetic voices, which cover a variety of regional accents.

“Over the last few years, there’s been an explosion of new research using neural networks to simulate a human voice. These models, including many developed at Google, can generate increasingly realistic, human-like speech,” Daisy Stanton, a software engineer at Google AI, wrote in a blog post. “While the progress is exciting, we’re keenly aware of the risks this technology can pose if used with the intent to cause harm. … [That’s why] we’re taking action.”

The dataset is available to all participants of ASVspoof 2019, a competition that aims to foster the development of protections for and countermeasures against spoofed speech — specifically, systems that can distinguish between real and computer-generated speech.

“As we published in our AI Principles last year, we take seriously our responsibility both to engage with the external research community, and to apply strong safety practices to avoid unintended results that create risks of harm,” Stanton wrote. “We’re also firmly committed to Google News Initiative’s charter to help journalism thrive in the digital age, and our support for the ASVspoof challenge is an important step along the way.”

AI systems that can be used to generate misleading media have come under increased scrutiny recently. In September, members of Congress sent a letter to National Intelligence director Dan Coats requesting a report from intelligence agencies about the potential impact of deepfakes — videos made using AI that digitally grafts faces onto other people’s bodies — on democracy and national security. Members of Congress speaking with Facebook COO Sheryl Sandberg and Twitter CEO Jack Dorsey also expressed concern about the potential impact of manipulative deepfake videos in a Congressional hearing in late 2018.

Fortunately, the fight against them appears to be ramping up. Last summer, members of DARPA’s Media Forensics program tested a prototypical system that could automatically detect deepfakes, or manipulated images or videos, in part by looking for cues like unnatural blinking in videos. And startups like Truepic, which raised an $ 8 million funding round in July, are experimenting with deepfakes detection as a service.

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