ACM SGB Meeting Materials September 20-21, 2003

(1) SIGDA puts together a compendium to capture and archive research contributions that take place at their conferences. SIGDA uses leverage by stating if ACM doesn't do it, IEEE will and if IEEE doesn't do it, ACM will. SIGDA's compendium was presented to ACM and other SIGs with positive feedback.

(2) SIGMOD did a version of the compendium, but found it to be more costly than anticipated.

(3) SIGARCH is doing a compendium as well. The challenge is gathering outside copyright information. The data is more available now and if utilize ACM's data, it is already scanable and includes it accurately.

(4) SIGMICRO allows authors to rebuttal paper reviews. SIGMICRO had an incident when someone gave an obnoxious review and the Program Chair did not notice it and sent it to the author. That particular review was discarded and other reviews were counted. Software is available through Kemal Ebcioglu.

(5) SIGARCH states that we must be observant with the Program Committee because with additional activities can be difficult. Also, just as there is a page limit on papers, there should also be a page limit on rebuttals. Long rebuttal reviews makes the process harder to run.

(6) SIGCHI feels that more precise guidelines should be given to the authors and the Program Committee regarding the rebuttal reviews. This may be more work, but the outcome is better.

(7) SIGPLAN feels that depending on the percentage of papers receive; Program Chairs should not have the need to read every paper.

(8) Be cautious when entering a co-sponsored agreement with other organizers. SIGOPS recent USENIX agreement caused quite concern.

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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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