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From "Selina Chu (JIRA)" <j...@apache.org>
Subject [jira] [Created] (CTAKES-374) Scale out of cTAKES pipeline. Finding better ways to allow cTAKES to be easily run in a distributed fashion.
Date Tue, 18 Aug 2015 18:10:24 GMT
Selina Chu created CTAKES-374:
---------------------------------

             Summary: Scale out of cTAKES pipeline. Finding better ways to allow cTAKES to
be easily run in a distributed fashion.
                 Key: CTAKES-374
                 URL: https://issues.apache.org/jira/browse/CTAKES-374
             Project: cTAKES
          Issue Type: New Feature
    Affects Versions: future enhancement
            Reporter: Selina Chu
             Fix For: 3.2.1


Currently, cTAKES can't be easily deployed in an asynchronous manner. UIMA components aren't
serializable (and thus cTAKES' components as well).  Would like to come up with better ways
to allow cTAKES to be easily run in a distributed fashion.

For example, for processing a long document (e.g. 10+ pages), cTAKES would take a long time
to process.

I would like to see a feature where we can partition the input to cTAKES, in a way that won't
affect the cTAKES annotation performance, allowing us to process through a cluster running
in distributed mode (e.g. Spark streaming cTAKES).  And then recombine the results such that
the word/phrase token positions will be sequentially ordered.

We have a simple implementation of the ClinicalPipelineFactory with Spark Streaming.  Currently
our initial attempt in partitioning is by paragraphs. For example, we are doing something
like:
RDD.map(a_single_paragraph.process_in_ctakes())

I also wanted to see if there are any better ways of doing this.  





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