SGB/SGB-EC Action Items

Action: Baglio will have ACM HQ staff put together a summary of exposure for each of the SIGs. This information will be forwarded to the SGB EC for review and will be included in future viability reports.(5/5/03)


Action: Frawley to work with SIGecom leadership to provide an update to the SGB EC at the Sept'05 meeting following the Ecom'05 conference.

Action: R. Walker to appoint SGB representative to PD Committee to oversee the pilot test and fully develop the operations and budget models for the PDC program.


Action: Ramdin to modify the DL revenue distribution mechanism so that no SIG will receive less than $10,000.

Action: Baglio to inform appropriate staff that SIGACCESS has been taken out of transition and placed in permanent status for a 2 year period.

Action: Frawley to modify viability schedule and schedule SIGACCESS for March'07 review.

Action: Frawley to modify viability schedule and schedule SIGMICRO for March'09 review.

Action: Frawley to modify viability schedule and schedule SIGMM for March'09 review.

Action: Frawley to modify viability schedule and schedule SIGSAC for March'09 review.

Action: Frawley to modify viability schedule and schedule SIGSAM for March'07 review.

Action: Volcheck to provide SGB EC with written update on SIGSAM activities and finances in March'06.

Action: Frawley to modify viability schedule and schedule SIGSIM for March'09 review.

Action: Ignatoff to modify viability schedule and schedule SIGSOFT for March'09 review.

Action: R. Walker to send note to Chris Stevenson informing her that the SGB allocated $100K per year for CSTA funding. Note should indicate that the funding is renewable for 3 years with the endorsement of the SGB, the concerns of the SGB and the reporting requirements of CSTA.

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. 

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