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From "Peng Cheng (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (MAHOUT-1286) Memory-efficient DataModel, supporting fast online updates and element-wise iteration
Date Mon, 12 Aug 2013 15:40:48 GMT

    [ https://issues.apache.org/jira/browse/MAHOUT-1286?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13736962#comment-13736962
] 

Peng Cheng commented on MAHOUT-1286:
------------------------------------

The idea of ArrayMap has been discarded due to its impractical time consumption of insertion
(O(n) for a batch insertion) and query (O(logn)). I have moved back to HashMap. Due to the
same reason, I feel that using Sparse Row/Column matrix may have the same problem.
                
> Memory-efficient DataModel, supporting fast online updates and element-wise iteration
> -------------------------------------------------------------------------------------
>
>                 Key: MAHOUT-1286
>                 URL: https://issues.apache.org/jira/browse/MAHOUT-1286
>             Project: Mahout
>          Issue Type: Improvement
>          Components: Collaborative Filtering
>    Affects Versions: 0.9
>            Reporter: Peng Cheng
>            Assignee: Sean Owen
>   Original Estimate: 336h
>  Remaining Estimate: 336h
>
> Most DataModel implementation in current CF component use hash map to enable fast 2d
indexing and update. This is not memory-efficient for big data set. e.g. Netflix prize dataset
takes 11G heap space as a FileDataModel.
> Improved implementation of DataModel should use more compact data structure (like arrays),
this can trade a little of time complexity in 2d indexing for vast improvement in memory efficiency.
In addition, any online recommender or online-to-batch converted recommender will not be affected
by this in training process.

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