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From "Fuad Efendi (JIRA)" <j...@apache.org>
Subject [jira] Issue Comment Edited: (LUCENE-2089) explore using automaton for fuzzyquery
Date Thu, 11 Feb 2010 03:06:27 GMT

    [ https://issues.apache.org/jira/browse/LUCENE-2089?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12832368#action_12832368
] 

Fuad Efendi edited comment on LUCENE-2089 at 2/11/10 3:05 AM:
--------------------------------------------------------------

Another idea (similar to 50-years-old "auto-recovery"), it doesn't allow me to sleep :)
What if we do all distance calculations (and other types of calculations) at indexing time
instead of at query time? For instance, we may have index structure like {Term, List[MisspelledTerm,
Distance]}, and we can query this structure by {MisspelledTerm, Distance}? We mentioned it
here already, LUCENE-1513, but our use case is very specific... and why to allow 5 spelling
mistakes in Unicode if user's input contains 3 characters only in Latin1? We should hardcode
some constraints.

Yes, memory requirements... in case of "????dogs" it can be at least few millions of additional
misspelled-terms for this specific "dogs" term only... but it doesn't grow linearly... and
we can limit such structure for distance=2, and use additional query-time processing if we
need distance=3.

It's just (naive) idea: to precalculate "similar terms" at indexing time...

      was (Author: funtick):
    Another idea (similar to 50-years-old "auto-recovery"), it doesn't allow me to sleep :)
What if we do all distance calculations (and other types of calculations) at indexing time
instead of at query time? For instance, we may have index structure like {Term, List[MisspelledTerm,
Distance]}, and we can query this structure by {MisspelledTerm, Distance}? We mentioned it
here already, LUCENE-1513, but our use case is very specific... and why to allow 5 spelling
mistakes in Unicode if user's input contains 3 characters only in Latin1? We should hardcode
some constraints.
  
> 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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