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From "Joel Bernstein (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (SOLR-13047) Add facet2D Streaming Expression
Date Fri, 12 Apr 2019 15:33:00 GMT

     [ https://issues.apache.org/jira/browse/SOLR-13047?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Joel Bernstein updated SOLR-13047:
----------------------------------
    Description: 
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

 

  was:
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. 

 


> 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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