[jira] [Commented] (SOLR-15197) Support temporal graph queries with daily windows

["ASF subversion and git services (Jira)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SOLR-15197?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17356454#comment-17356454 ] 

ASF subversion and git services commented on SOLR-15197:
--------------------------------------------------------

Commit 4c76bcc65017a1ff20dd64eedd63d4976a38a554 in solr's branch refs/heads/main from Joel Bernstein
[ https://gitbox.apache.org/repos/asf?p=solr.git;h=4c76bcc ]

SOLR-15197: Support temporal graph queries with daily windows


> Support temporal graph queries with daily windows
> -------------------------------------------------
>
>                 Key: SOLR-15197
>                 URL: https://issues.apache.org/jira/browse/SOLR-15197
>             Project: Solr
>          Issue Type: Improvement
>            Reporter: Joel Bernstein
>            Assignee: Joel Bernstein
>            Priority: Major
>             Fix For: main (9.0)
>
>         Attachments: SOLR-15197.patch, SOLR-15197.patch, SOLR-15197.patch
>
>
> The initial implementation of temporal graph queries supported ten second time windows which is useful for log analytics use cases. This ticket will allow for event correlation across daily windows, which is useful for many different fields, including event correlation in the stock market
> This ticket will add the support for daily windows and add the syntax to support other time windows as well. Follow-on tickets will add more time windows.
> Below is the sample syntax for a temporal graph query with a window of 1 DAY.
> {code:java}
> nodes(daily_stock_returns,
>       search(daily_stock_returns, 
>              q="ticker_s:jpm AND change_d:[2 TO *]", 
>              fl="day_s", 
>              sort="change_d desc",
>              rows="50"),
>       walk="day_s->day_s",
>       gather="ticker_s",
>       fq="change_d:[1 TO *]",
>       window="1DAY",
>       lag="2",      
>       count(*))
> {code}
> This query counts all tickers that are up by atleast 1, in a one day window, 2 days prior to a rise in the ticker *jpm* by atleast 2. This surfaces tickers that most frequently rise two days before jpm rises.
> This demonstrates event correlation in stock market data.
>  



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