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https://issues.apache.org/jira/browse/LUCENE-1812?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12775266#action_12775266
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Robert Muir commented on LUCENE-1812:
-------------------------------------
Andrzej, i tested your patch. I found two places where @override was on an
interface, only problem so far.
here are some results on the hamshahri persian test collection (I used TF
method with -t 2)
||Measure||Unpruned||Pruned||
|index size|98627KB|42339KB|
|map|0.4809|0.4241|
|recip_rank|0.8368|0.8393|
|P5|0.6277|0.6369|
|P10|0.5677|0.5785|
|P15|0.5436|0.5231|
|P20|0.5185|0.4969|
|P30|0.4703|0.4385|
|P100|0.2782|0.2440|
the queries in this corpus are somewhat general, but seems to be a nice way to
reduce the index to more than half its size, still with reasonable quality.
> 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
> Reporter: Andrzej Bialecki
> Attachments: 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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