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https://issues.apache.org/jira/browse/SOLR-13105?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17266042#comment-17266042
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Joel Bernstein commented on SOLR-13105:
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Thanks for working on this [~ctargett]! And thanks for comments [~epugh]!
We can think about datasets going forward. I like the iris dataset because it's
so small and so well known and Solr logs would be a great data set. For Solr
logs we could write a program that does some randomized searches on our example
data sets to general some logs. Then either ship the program or ship the logs.
> A visual guide to Solr Math Expressions and Streaming Expressions
> -----------------------------------------------------------------
>
> Key: SOLR-13105
> URL: https://issues.apache.org/jira/browse/SOLR-13105
> Project: Solr
> Issue Type: New Feature
> Reporter: Joel Bernstein
> Assignee: Joel Bernstein
> Priority: Major
> Attachments: Screen Shot 2019-01-14 at 10.56.32 AM.png, Screen Shot
> 2019-02-21 at 2.14.43 PM.png, Screen Shot 2019-03-03 at 2.28.35 PM.png,
> Screen Shot 2019-03-04 at 7.47.57 PM.png, Screen Shot 2019-03-13 at 10.47.47
> AM.png, Screen Shot 2019-03-30 at 6.17.04 PM.png
>
>
> Visualization is now a fundamental element of Solr Streaming Expressions and
> Math Expressions. This ticket will create a visual guide to Solr Math
> Expressions and Solr Streaming Expressions that includes *Apache Zeppelin*
> visualization examples.
> It will also cover using the JDBC expression to *analyze* and *visualize*
> results from any JDBC compliant data source.
> Intro from the guide:
> {code:java}
> Streaming Expressions exposes the capabilities of Solr Cloud as composable
> functions. These functions provide a system for searching, transforming,
> analyzing and visualizing data stored in Solr Cloud collections.
> At a high level there are four main capabilities that will be explored in the
> documentation:
> * Searching, sampling and aggregating results from Solr.
> * Transforming result sets after they are retrieved from Solr.
> * Analyzing and modeling result sets using probability and statistics and
> machine learning libraries.
> * Visualizing result sets, aggregations and statistical models of the data.
> {code}
>
> A few sample visualizations are attached to the ticket.
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