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From "Mark Miller (JIRA)" <j...@apache.org>
Subject [jira] Updated: (LUCENE-2089) explore using automaton for fuzzyquery
Date Sun, 22 Nov 2009 21:45:39 GMT

     [ https://issues.apache.org/jira/browse/LUCENE-2089?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Mark Miller updated LUCENE-2089:
--------------------------------

    Attachment: Moman-0.1.tar.gz

>From Moman author:

Absolutely. Sorry for the missing links. I had some problems with my
provider which backed up an old version of my website.
The library is MIT licensed, so feel free to take anything you want. I
didn't made the subversion available since I was working
on Daciuk's "Incremental construction of minimal acyclic finite-state
automata", improving the memory footprint of the algorithm.
Since I want to be sure the new algorithm is right before publishing
it, the repository isn't available. Anyway, here's the source
code (attached). I must warn you that there's no comments at all,
which is unfortunate, since I was contacted by many people
lately, that had the same kind of request than yours.

If you need anything, just don't hesitate to ask.


> 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: Moman-0.1.tar.gz
>
>
> 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 AutomatonTermEnum, here is my idea
> * up front, calculate the maximum required K edits needed to match the users supplied
float threshold.
> * for at least common K (1,2,3, etc) we should use automatontermenum. if its outside
of that, maybe use the existing slow logic. At high K, it will seek too much to be helpful
anyway.
> 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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