ACM SGB Meeting Materials Agenda October 5, 2012

SIG Governing Board
Friday, October 5, 2012
8:30 am – 4:00 pm
Chicago, Hilton O'Hare
 

Wireless information:

Network name: oharemeeting5
Password: greatday5
 
8:30 am – 9:00 am Continental Breakfast
9:00 am – 9:15 am
1.0 Welcome
1.1 Welcome, Introductions (Altman, Madden)
9:15 am – 10:00 am
2.0 Report from the ACM CEO (White)
10:00 am – 10:15 am
Break
10:15 am – 11:45 am
3.0 Publications Board Report (White, Konstan) Slides Back-up Material
11:45 am – 12:00 pm
4.0 SIGHPC Report 
12:00 pm – 1:00 pm Lunch
1:00 pm – 2:00 pm
5.0 Viability Reviews
5.1 SIGART (Gil) SlidesViabilityReachAwards
5.2 SIGecom (Parkes) SlidesViabilityReach, Awards
5.3 SIGKDD (Teredesai) SlidesViabilityReachAwards
2:00 pm – 2:20 pm
6.0 SGB EC Administrative Reports
6.1 SGB EC Update (Altman)
6.2 Task Force Updates (Madden)
6.3 ACM/AMIA Task Force Report (Konstan)
6.4 SIG Proposals (Wood)
6.5 Professional Development Committee Report (Tracz) Back-up Material          
2:20 pm –   2:35 pm
Break 
2:35 pm – 4:00 pm 7.0 Best Practices Session (All)

Why I Belong to ACM

Hear from Bryan Cantrill, vice president of engineering at Joyent, Ben Fried chief information officer at Google, and Theo Schlossnagle, OmniTI founder on why they are members of ACM.

ACM Case Studies

Written by leading domain experts for software engineers, ACM Case Studies provide an in-depth look at how software teams overcome specific challenges by implementing new technologies, adopting new practices, or a combination of both. Often through first-hand accounts, these pieces explore what the challenges were, the tools and techniques that were used to combat them, and the solution that was achieved.

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.