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Anonymous
Not applicable

Filter for entire "chain" in network/sankey chart - not just source/target

I'm trying to visualise the structure of users > security groups > apps < workspaces < reports for ease of administration.

 

So far I have tried with the Network Navigator and the Sankey chart. My primary issue is that I only get Source and Target when I apply a filter, due to the datamodel (source/target). 

 

What I want is for example to select a security group on a list and then display all users in that group AND connected apps AND their connected workspaces AND their connected reports: the entire connection chain so to speak. 

 

Is that possible and any good ideas? Thanks.

 

My dataset is organized in Source and Target columns as follows:

 

Source                        Target

--------                        -------------------

User1                           Security group1

Security group 1          App1

Report1                       Workspace1

Workspace1                App1

 

 

1 ACCEPTED SOLUTION
stretcharm
Memorable Member
Memorable Member

 

Maybe the PATH and PATHCONTAINS DAX functions will help

 

here is a video explaining their use.

https://www.youtube.com/watch?v=EzfLJFEKV8I

 

View solution in original post

3 REPLIES 3
stretcharm
Memorable Member
Memorable Member

 

Maybe the PATH and PATHCONTAINS DAX functions will help

 

here is a video explaining their use.

https://www.youtube.com/watch?v=EzfLJFEKV8I

 

Anonymous
Not applicable

Thanks stretcharm. That looks  very relevant AND, sadly, complicated. 

 

This link seems to break it down well: https://www.daxpatterns.com/parent-child-hierarchies/

 

I will look into it.

Hi @Anonymous,

 

Thanks for your sharing. If you have resolved your problem through above link, would you please accept it as a valid solution so that more users can benefit from it?

 

Regards,

Yuliana Gu

Community Support Team _ Yuliana Gu
If this post helps, then please consider Accept it as the solution to help the other members find it more quickly.

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