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From "Andrzej Bialecki (JIRA)" <j...@apache.org>
Subject [jira] Commented: (LUCENE-1812) Static index pruning by in-document term frequency (Carmel pruning)
Date Tue, 10 Aug 2010 21:10:17 GMT

    [ https://issues.apache.org/jira/browse/LUCENE-1812?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12897043#action_12897043
] 

Andrzej Bialecki  commented on LUCENE-1812:
-------------------------------------------

That's great news, thanks! However, now you got me thinking ... considering there is legal
aspect to the matter, do we (the Apache Lucene project) need something more substantial from
IBM (e.g. a statement from your IP dept.) than just your "go ahead" in a JIRA comment?

> 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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