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WRM: PIGFARM: Spring 2017 Senior Design Project – “Combining Apache Pig Scripts for Performance and Collaboration”

Mar 29, 2017 @ 1:00 pm - 2:30 pm

Carson Cumbee
Laboratory for Analytic Sciences

Abstract 

PIGFARM is an LAS sponsored project for NCSU’s Computer Science Senior Design Class for Spring 2017.  PIGFARM is an effort to create a multi-query optimization system for Apache Pig scripts that run on Hadoop Map/Reduce.  The core technique being investigated is fusing the map portions of multiple scripts running over the same data and writing out explicit temporary files that are then loaded into the remaining portions of the various scripts for parallel execution.  This presentation will give motivation for the project, the approaches taken by the project, and preliminary test results of the project on standard Benchmark datasets for Apache Pig.

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WEB/CONFERENCE CALL INFO:
We are going to use the NC State WebEx for the web conference. Please note that this
WebEx belongs to NC State and can not be downloaded directly from Cisco. Also, it
should work on iPhones and iPads via the WebEx App.
A good internet connection is recommended.
For better audio, please join via computer and then have the meeting call your number or call in directly using one of the numbers below. When not speaking, please mute your
phone to avoid background noise.
When it’s time, join the WebEx meeting from here:
https://wolfmeeting.ncsu.edu/orion/meeting/meetingInfo?MeetingKey=BgAAAGusDDQG2cXwNzZr7g3-C1DuXqwI-k9G1nScameJbApZfT6PxNmhSDBvtncaiBl3dD17gQ49YDt9CgMQid1-M5AF&frm=page&siteurl=wolfmeeting
Meeting Number: 997 750 516
Audio Connection: 919-513-9329 (WolfMeeting)
Access Code: 997 750 516

Sponsored by the Laboratory for Analytic Sciences

Details

Date:
Mar 29, 2017
Time:
1:00 pm - 2:30 pm
Event Category:
WRM
Event Tags:
meeting, research, wednesday, WRM

Organizer

Mindy Huffman
Phone:
919-515-6084

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LAS aims to bring together a multi-disciplinary group of academic, industry, and government researchers, analysts and managers together to re-engineer the intelligence analysis process to promote predictive analysis. LAS will do this by conducting both classified and unclassified research in a variety of areas of research. The research done in this area will serve as the foundation for mission effects and integrated back into the enterprise.

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