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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 ]

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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