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nitingupta183 schrieb:
> Hi all,
>
> I am supposed to add a feature in which my app will detect the duplicate
> contacts of a user on the basis of their name, email, mobile number
> etc.(i.e. Contacts Duplicate Killer kind of feature). The simplest algo i
> can think of is find all the contacts on the basis of their name, email and
> mobile and then run the loop to determine which all contacts have similar
> entries. But i think this algo will have worst performance.
Try to prune your search space. It is reasonable to assume that there
are not too many duplicates overall.
You can use IndexReader.terms() to get a list of terms and then a
docFreq() to check the number of documents containing that term.
E.g. search for all email terms and process those, whose docFreq is >1.
Add the corresponding documents for each email term to a "possible
identical contacts" container.
Repeat the same with birth dates, phone numbers and names, preferably
with some normalization.
Then merge those "possible identical contact" containers, who share a
common document.
Example:
Container 1 Container 2 Merged Container
--->
A, B B,C A,B,C
(Implementation note: try to keep track of the list of containers a
certain number is in using a look-up table: A -> 1; B -> 1,2,3,6; C ->2
etc. )
Then compare the documents inside these container with each other and
decide, which contacts you want to merge and which not.
> I am currently using Hibernate. I got to know about Hibernate Search/Lucene.
> Can I use these solutions for this task. I am asking this on the basis that
> Lucene already implements algos such as Levenshtein_distance. May be I can
> harness the Lucene power to make this task efficient.
Try using a Soundex or Metaphone analyzer for similarity; they map
similar sounding strings to a single value and are much easier to handle
in the Lucene framework than numeric measures like Levensthein;
there are examples in Lucene contrib.
- --
Rene Wiermer
(Softwareentwickler/Systemingenieur)
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