SIG Governing Board-Executive Committee Motions - FY'02

Motion: Move that the SGB accept the proposal that the SIGs finance the development of the Guide, in exchange for making the Guide free to all and enabling SIG-specific enhancements to the Guide.

That the SGB EC constitute a group to work with HQ and the ACM EC to agree on the details. (11/20/01)

Motion: Move to endorse exception to ACM policy and allow SIGGRAPH to sole source their Web Programming Services. This is a one-time only exception.

Cohoon, Niederman
unanimous (11/20/01)

Motion: Move to endorse a waiver to current policy and allow a two-year extension to ACM SIGGRAPH's contract with Freeman Decorating. SIGGRAPH leadership is encouraged to exchange this extension for clear benefit to the conference.

Haramundanis, Cohoon
unanimous (11/20/01)

Motion: Move to endorse policy modification on the RFP process bringing the threshold to $75,000 for conferences with budgets in excess of $1M.

Berenbaum, Haramundanis
unanimous (11/20/01)

Motion: Move to endorse a change in policy on the length of contracts for 3 to 5 years for conferences with budgets in excess of $1M.

Cohoon, Haramundanis
unanimous (11/20/01)

Motion: The SGB EC approves the budgets of SIGCAPH, SIGDOC, SIGMICRO, SIGSAC and SIGecomm for FY'03. All Chairs for these SIGs will be required to submit an outline by June 1st indicating their plans to bring the SIG out of financial trouble.

Cohoon, Feldman
unanimous (04/10/02)

Motion: Move that SIGART be converted to a conference SIG as directed by the SIG Operating Rules.

Klein, Cohoon
unanimous (04/10/02)

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. 

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.