September 23, 2011 SGB Meeting Agenda

SIG Governing Board

Friday, September 23, 2011

Ballroom V

8:30 am – 4:00 pm

8:30 am - 9:00 am Continental Breakfast
9:00 am - 9:10 am 1.0 Welcome
  1.1 Welcome, Introductions (Hanson, Sears)
  1.2 Welcome, ACM President (Chesnais)
9:10 am - 9:55 am 2.0 Report from the ACM CEO (White)
9:55 am - 10:25 am 3.0 USACM Report (Gene Spafford)
10:25 am - 11:10 am 4.0 Program Reviews
  4.1 SIGEVO (Wolfgang Banzhaf) Slides  Viability Reach Awards
  4.2 SIGMOBILE (Roy Want) Slides Viability Reach Awards
  4.3 SIGBioinformatics (Armin Mikler) Slides Viability Reach
11:10 am - 11:25 am Break
11:25 am - 11:50 am 5.0 Publications Board Report (Joe Konstan) Report
11:50 am - 12:00 pm 6.0 Turing Award Website and Oral Histories (Brent Hailpern) History Committee Update
12:00 pm - 1:00 pm Lunch
1:00 pm - 2:15 pm 7.0 SIG Proposals (Dave Pennock)
  7.1 SIGHPC Final Proposal (SIGHPC Rep) 20 minutes 
  7.2 SIGGAME 
  7.3 SIGEnterprise Architecure
  7.4 SIGEnterprise Computing 
  7.5 SIGDEV 
  7.6 SIGBP 
2:15 pm - 2:30 pm Break
2:30 pm - 3:00 pm 8.0 Social Networking Opportunities (Cliff Lampe)
3:00 pm - 3:30 pm 9.0 Changes to Copyright Policy & Referral Linking (Bernie Rous, Wayne Graves)
3:30 pm - 4:00 pm 10.0 Best Practices Session (All)
  10.1 Are You Talking To Me? Vardi Editorial
  10.2 Contests
  10.2.1 SIGCOMM Contest Example 

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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.