SIG Governing Board-Executive Committee Motions - FY'99

Motion: Move that SIGCAPH retain its status as a multi-service SIG and provide the SGB with a program review in October'00.

Unanimous

Motion: Move to charter the Special Interest Group on Electronic Commerce and appoint Stuart Feldman, Chair.

Lidtke, Ellis
(6,0,0)

Motion: Move to allow SIGPLAN to run only one candidate for the position of Vice Chair.

Furuta,, Notkin
(6,0,0)

Motion: Move to accept proposal presented by Furuta to endorse the SIGKDD proposal for SIGKDD Innovation Award and SIGKDD Services Award, with the following amendments:

  1. Awards will be plaque only (no cash award) until SIGKDD achieves the required fund balance reserve or until stable external funding for prize money is obtained.
  2. Initiation of the SIGKDD Services Award will be postponed to on or after January 1, 2000, or SIGKDD's leaving transitional status, whichever is later.

Soffa, Furuta
(6,0,1)

Motion:Move that the SGB-EC endorse the SIGCAS proposal (as amended) to create two awards: SIGCAS Service Award and SIGCAS Making a Difference Award.

(Furuta, Cassel)
Motion passed

Motion: Motion to extend terms of SIGCAS and SIGACT leadership through June 30, 2001.

(Cassel, Furuta)
Motion passed

Motion: Move that the SGB EC approve the SIGGRAPH EC budget request to include a $350,000 movie expense in their FY'99 budget and to include a $110,000 movie premier expense in their FY'00 budget.

(Soffa, Ellis)
unanimous

Motion: Move to thank McCarren, Riederman and Mohar for their fine work and service.

acclamation

Motion: Move to make $1,500 in travel funds available to each TC Rep from the SIG Board budget.

(Ellis, Cohoon)
motion passed

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.