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https://issues.apache.org/jira/browse/SOLR-13047?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16818793#comment-16818793
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Nazerke Seidan edited comment on SOLR-13047 at 4/16/19 10:50 AM:
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Regarding the implementation details, are the math expressions limited to
metrics such as sum, count, max, min and avg?
I came up with the following implementation ideas:
This is a constructor how it looks like in this stream:
facet2DStream(String collection, ModifiableSolrParams params, Bucket x, Bucket
y, String dimensions, Metric metric).
The basic idea is that first I will apply a count metric on the given buckets.
Then I will internally sort the buckets in descending order. Then I will get
the tuples while the x and y values are not equal in the dimensions.
Any suggestions?
was (Author: snazerke):
Regarding the implementation details, are the math expressions limited to
metrics such as sum, count, max, min and avg?
> Add facet2D Streaming Expression
> --------------------------------
>
> Key: SOLR-13047
> URL: https://issues.apache.org/jira/browse/SOLR-13047
> Project: Solr
> Issue Type: New Feature
> Security Level: Public(Default Security Level. Issues are Public)
> Reporter: Joel Bernstein
> Assignee: Joel Bernstein
> Priority: Major
>
> The current facet expression is a generic tool for creating multi-dimension
> aggregations. The *facet2D* Streaming Expression has semantics specific for 2
> dimensional facets which are designed to be *pivoted* into a matrix and
> operated on by *Math Expressions*.
> facet2D will use the json facet API under the covers.
> Proposed syntax:
> {code:java}
> facet2D(medrecords, q=*:*, x=diseases, y=symptoms, dimensions="300, 10",
> count(*)){code}
> The example above will return tuples containing the top 300 diseases and the
> top ten symptoms for each disease.
> Using math expression the tuples can be *pivoted* into a matrix where the
> rows of the matrix are the diseases, the columns of the matrix are the
> symptoms and the cells in the matrix contain the counts. This matrix can then
> be *clustered* to find clusters of *diseases* that are correlated by
> *symptoms*.
> {code:java}
> let(a=facet2D(medrecords, q=*:*, x=diseases, y=symptoms, dimensions="300,
> 10", count(*)),
> b=pivot(a, diseases, symptoms, count(*)),
> c=kmeans(b, 10)){code}
>
> *Implementation Note:*
> The implementation plan for this ticket is to create a new stream called
> Facet2DStream. The FacetStream code is a good starting point for the new
> implementation and can be adapted for the Facet2D parameters. Similar tests
> to the FacetStream can be added to StreamExpressionTest
>
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