ACM SGB Meeting Materials Agenda March 14, 2013

SIG Governing Board
Thursday, March 14, 2013
8:30 am - 4:00 pm
Chicago, Hilton O'Hare
 
Room: International West & Center
WIRELESS ACCESS (case sensitive):
Network Name: Hilton Meeting Rms
Access Code: ACM2013
 
8:30 am - 9:00 am     Continental Breakfast
9:00 am - 9:15 am 1.0   Welcome
  1.1   Welcome, Introductions (Altman, Madden)
  1.2   Welcome from ACM President (Cerf)
9:15 am - 10:00 am 2.0   Report from the ACM CEO (White)
10:00 am - 10:15 am     Break
10:15 am - 10:45 am 3.0   Publications Board Report (Konstan)
10:45 am - 11:25 am 4.0   CSTA (Stephenson) Report
11:25 am - 12:00 pm 5.0   IFIP (Turner) Report
12:00 pm - 12:15 pm 6.0   ACM History Committee Report (Tao Xie)
12:15 pm - 1:00 pm     Lunch
1:00 pm - 2:45 pm  7.0   Viability Reviews
  7.1   SIGDOC (Pierce) SlidesViabilityReachAwards
  7.2   SIGMICRO (Bose) SlidesViabilityReach, Awards
  7.3   SIGMOD (Ioannidis) SlidesViabilityReachAwards
  7.4   SIGMM (Nahrstedt) SlidesViabilityReachAwards 
  7.5   SIGSAC (Bertino) SlidesViabilityReachAwards
  7.6   SIGSAM (Johnson) SlidesViabilityReachAwards
  7.7   SIGSIM (Fishwick) SlidesViabilityReachAwards
2:45 pm - 3:00 pm     Break
3:00 pm - 3:15 8.0   SGB EC Administrative Reports
  8.1   SGB EC Update (Altman)
  8.2   Distinguished Speakers Update (Bose)
  8.3   ACM/AMIA Task Force Report (Konstan)
  8.4   SIG Proposals/New Conferences (Wood, Madden) 
3:15 pm - 4:00 pm 9.0   Best Practices Session (All)

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Prediction-Serving Systems

ACM Queue’s “Research for Practice” is your number one resource for keeping up with emerging developments in the world of theory and applying them to the challenges you face on a daily basis. In this installment, Dan Crankshaw and Joey Gonzalez provide an overview of machine learning server systems. What happens when we wish to actually deploy a machine learning model to production, and how do we serve predictions with high accuracy and high computational efficiency? Dan and Joey’s curated research selection presents cutting-edge techniques spanning database-level integration, video processing, and prediction middleware. Given the explosion of interest in machine learning and its increasing impact on seemingly every application vertical, it's possible that systems such as these will become as commonplace as relational databases are today.