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Build failed in Jenkins: mahout-nightly ยป Mahout Integration #1395

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[jira] [Comment Edited] (MAHOUT-1286) Memory-efficient DataModel, supporting fast online updates and element-wise iteration

2013-10-26 Thread Gokhan Capan (JIRA) 

[ 
https://issues.apache.org/jira/browse/MAHOUT-1286?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13806106#comment-13806106
 ] 

Gokhan Capan edited comment on MAHOUT-1286 at 10/26/13 2:13 PM:


Peng,

I am attaching a patch (not to be committed) that includes some benchmarking 
code in case you need one, and 2 in-memory data models as a baseline.


was (Author: gokhancapan):
Peng,

I am attaching a patch --not to be committed-- that includes some benchmarking 
code in case you need one, and 2 in-memory data models as a baseline.

> 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
>  Labels: collaborative-filtering, datamodel, patch, recommender
> Fix For: 0.9
>
> Attachments: benchmark.patch, InMemoryDataModel.java, 
> InMemoryDataModelTest.java, Semifinal-implementation-added.patch
>
>   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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[jira] [Comment Edited] (MAHOUT-1286) Memory-efficient DataModel, supporting fast online updates and element-wise iteration

2013-10-26 Thread Gokhan Capan (JIRA) 

[ 
https://issues.apache.org/jira/browse/MAHOUT-1286?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13806106#comment-13806106
 ] 

Gokhan Capan edited comment on MAHOUT-1286 at 10/26/13 2:13 PM:


Peng,

I am attaching a patch --not to be committed-- that includes some benchmarking 
code in case you need one, and 2 in-memory data models as a baseline.


was (Author: gokhancapan):
Peng,

I am attaching a patch -not to be committed- that includes some benchmarking 
code in case you need one, and 2 in-memory data models as a baseline.

> 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
>  Labels: collaborative-filtering, datamodel, patch, recommender
> Fix For: 0.9
>
> Attachments: benchmark.patch, InMemoryDataModel.java, 
> InMemoryDataModelTest.java, Semifinal-implementation-added.patch
>
>   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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[jira] [Updated] (MAHOUT-1286) Memory-efficient DataModel, supporting fast online updates and element-wise iteration

2013-10-26 Thread Gokhan Capan (JIRA) 

 [ 
https://issues.apache.org/jira/browse/MAHOUT-1286?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Gokhan Capan updated MAHOUT-1286:
-

Attachment: benchmark.patch

Peng,

I am attaching a patch -not to be committed- that includes some benchmarking 
code in case you need one, and 2 in-memory data models as a baseline.

> 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
>  Labels: collaborative-filtering, datamodel, patch, recommender
> Fix For: 0.9
>
> Attachments: benchmark.patch, InMemoryDataModel.java, 
> InMemoryDataModelTest.java, Semifinal-implementation-added.patch
>
>   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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