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From "Robert Muir (JIRA)" <j...@apache.org>
Subject [jira] Commented: (LUCENE-2089) explore using automaton for fuzzyquery
Date Wed, 10 Feb 2010 21:21:28 GMT

    [ https://issues.apache.org/jira/browse/LUCENE-2089?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12832205#action_12832205
] 

Robert Muir commented on LUCENE-2089:
-------------------------------------

fuad it does not expand the query to OR'ed terms.

here is a description from the 2002 paper that describes how automaton works (for regex, wildcard,
fuzzy, whatever the case)
I edited the description to substitute components with their implementation... and yes it
works for all Unicode (all over 1 million codepoints not just the BMP)

For more details, see AutomatonTermsEnum in the flex branch svn.

The set of all dictionary words is treated as a regular language over the alphabet of letters.
At each step, the prefix of all letters that are consumed on the path from the initial state
to the current state is maintained. A variant of the Wagner-Fisher algorithm is used to control
the -walk through the automaton- (enumeration of Lucene's term dictionary) in such a way that
only -prefixes are generated- (FilteredTermsEnums only seeks to terms) that potentially lead
to a correction candidate V where the Levenshtein distance between V and W does not exceed
a fixed bound n.

> explore using automaton for fuzzyquery
> --------------------------------------
>
>                 Key: LUCENE-2089
>                 URL: https://issues.apache.org/jira/browse/LUCENE-2089
>             Project: Lucene - Java
>          Issue Type: Wish
>          Components: Search
>            Reporter: Robert Muir
>            Assignee: Mark Miller
>            Priority: Minor
>         Attachments: LUCENE-2089.patch, Moman-0.2.1.tar.gz, TestFuzzy.java
>
>
> Mark brought this up on LUCENE-1606 (i will assign this to him, I know he is itching
to write that nasty algorithm)
> we can optimize fuzzyquery by using AutomatonTermsEnum, here is my idea
> * up front, calculate the maximum required K edits needed to match the users supplied
float threshold.
> * for at least small common E up to some max K (1,2,3, etc) we should create a DFA for
each E. 
> if the required E is above our supported max, we use "dumb mode" at first (no seeking,
no DFA, just brute force like now).
> As the pq fills, we swap progressively lower DFAs into the enum, based upon the lowest
score in the pq.
> This should work well on avg, at high E, you will typically fill the pq very quickly
since you will match many terms. 
> This not only provides a mechanism to switch to more efficient DFAs during enumeration,
but also to switch from "dumb mode" to "smart mode".
> i modified my wildcard benchmark to generate random fuzzy queries.
> * Pattern: 7N stands for NNNNNNN, etc.
> * AvgMS_DFA: this is the time spent creating the automaton (constructor)
> ||Pattern||Iter||AvgHits||AvgMS(old)||AvgMS (new,total)||AvgMS_DFA||
> |7N|10|64.0|4155.9|38.6|20.3|
> |14N|10|0.0|2511.6|46.0|37.9|	
> |28N|10|0.0|2506.3|93.0|86.6|
> |56N|10|0.0|2524.5|304.4|298.5|
> as you can see, this prototype is no good yet, because it creates the DFA in a slow way.
right now it creates an NFA, and all this wasted time is in NFA->DFA conversion.
> So, for a very long string, it just gets worse and worse. This has nothing to do with
lucene, and here you can see, the TermEnum is fast (AvgMS - AvgMS_DFA), there is no problem
there.
> instead we should just build a DFA to begin with, maybe with this paper: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.16.652
> we can precompute the tables with that algorithm up to some reasonable K, and then I
think we are ok.
> the paper references using http://portal.acm.org/citation.cfm?id=135907 for linear minimization,
if someone wants to implement this they should not worry about minimization.
> in fact, we need to at some point determine if AutomatonQuery should even minimize FSM's
at all, or if it is simply enough for them to be deterministic with no transitions to dead
states. (The only code that actually assumes minimal DFA is the "Dumb" vs "Smart" heuristic
and this can be rewritten as a summation easily). we need to benchmark really complex DFAs
(i.e. write a regex benchmark) to figure out if minimization is even helping right now.

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