Petition Announcement

In accordance with ACM Bylaw 6, the following SIGs will hold elections in 2015:

SIGACCESS, SIGACT, SIGAPP, SIGARCH, SIGBED, SIGBio, SIGCHI, SIGDA, SIGecom, SIGEVO, SIGGRAPH, SIGITE, SIGMETRICS, SIGMIS, SIGOPS, SIGPLAN, SIGSOFT, and SIGWEB

In accordance with ACM's Constitution and Bylaws, the following SIGs have requested and the SIG Governing Board has granted an extension of termsSIGAdaSIGCOMMSIGDOCSIGKDDSIGMOBILESIGMMSIGSAC, and SIGSAM.  

As a voting member, you may petition the ACM to request an election. If this is your wish, you must send a petition with signatures of at least 1% of the SIG's members to Pat Ryan, ACM Chief Operating Officer, Office of Policy and Administration, 2 Penn Plaza, Suite 701, New York, NY 10121-0701, USA no later than November 3, 2014.

The SIG will then be asked to form a nominating committee and begin the electoral process. Questions about the petition process should be directed to Pat Ryan, ACM Chief Operating Officer (ryan_p@acm.org).

SIG Elections Policy and Procedures

Volunteer with SocialCoder

You can use your technical skills for social good and offer volunteer support on software development projects to organizations who could not otherwise afford it. SocialCoder connects volunteer programmers/software developers with registered charities and helps match them to suitable projects based on their skills, experience, and the causes they care about. Learn more about ACM’s new partnership with SocialCoder, and how you can get involved.

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.