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Date: Sun, 23 May 2010 06:31:21 -0400 (EDT)
From: "Doron Cohen (JIRA)" <jira@apache.org>
To: dev@lucene.apache.org
Subject: [jira] Commented: (LUCENE-1812) Static index pruning by in-document
 term frequency (Carmel pruning)
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    [ https://issues.apache.org/jira/browse/LUCENE-1812?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12870383#action_12870383 ] 

Doron Cohen commented on LUCENE-1812:
-------------------------------------

Hi Andrzej, Robert, please note that IBM holds a [patent on Lossy index compression|http://patft.uspto.gov/netacgi/nph-Parser?Sect2=PTO1&Sect2=HITOFF&p=1&u=%2Fnetahtml%2FPTO%2Fsearch-bool.html&r=1&f=G&l=50&d=PALL&RefSrch=yes&Query=PN%2F7356527].
I am checking with the IP department at IBM about committing an implementation of the patent in Lucene, and will update here as soon as I know where it stands - could you hold committing this until then?


> Static index pruning by in-document term frequency (Carmel pruning)
> -------------------------------------------------------------------
>
>                 Key: LUCENE-1812
>                 URL: https://issues.apache.org/jira/browse/LUCENE-1812
>             Project: Lucene - Java
>          Issue Type: New Feature
>          Components: contrib/*
>    Affects Versions: 2.9, 3.1
>            Reporter: Andrzej Bialecki 
>         Attachments: pruning.patch, pruning.patch, pruning.patch
>
>
> This module provides tools to produce a subset of input indexes by removing postings data for those terms where their in-document frequency is below a specified threshold. The net effect of this processing is a much smaller index that for common types of queries returns nearly identical top-N results as compared with the original index, but with increased performance. 
> Optionally, stored values and term vectors can also be removed. This functionality is largely independent, so it can be used without term pruning (when term freq. threshold is set to 1).
> As the threshold value increases, the total size of the index decreases, search performance increases, and recall decreases (i.e. search quality deteriorates). NOTE: especially phrase recall deteriorates significantly at higher threshold values. 
> Primary purpose of this class is to produce small first-tier indexes that fit completely in RAM, and store these indexes using IndexWriter.addIndexes(IndexReader[]). Usually the performance of this class will not be sufficient to use the resulting index view for on-the-fly pruning and searching. 
> NOTE: If the input index is optimized (i.e. doesn't contain deletions) then the index produced via IndexWriter.addIndexes(IndexReader[]) will preserve internal document id-s so that they are in sync with the original index. This means that all other auxiliary information not necessary for first-tier processing, such as some stored fields, can also be removed, to be quickly retrieved on-demand from the original index using the same internal document id. 
> Threshold values can be specified globally (for terms in all fields) using defaultThreshold parameter, and can be overriden using per-field or per-term values supplied in a thresholds map. Keys in this map are either field names, or terms in field:text format. The precedence of these values is the following: first a per-term threshold is used if present, then per-field threshold if present, and finally the default threshold.
> A command-line tool (PruningTool) is provided for convenience. At this moment it doesn't support all functionality available through API.

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