ACM DL Profile Pages
ACM DL Profile Pages
Throughout the ACM Digital Library there are a variety of aggregate views. Author Profile pages contain a normalized look at all items published by a particular user. A variety of aggregated information is produced including a set of bibliometrics, subject listings, and colleagues. Authors are encouraged to add personal information to these pages. Institutional Pages that take a similar approach to an author page but based on an aggregation of all the works from authors affiliated with a given institution.
Select a list of Institutional Profile pages here...
Conference views pull together all the published materials that have come out of a given conference over time.
Select a list of Conference Profile pages here…
- SIGGRAPH - International Conference on Computer Graphics and Interactive Techniques
- Super Computing - High Performance Computing Networking, Storage and Analysis
- ICSE - International Conference on Software Engineering
SIG views perform a similar function across all materials that a SIG has published or sponsored. These views lead to interesting collections of bibliometrics, people, subjects, affiliations, etc.
Select a list of SIG Profile pages here…
Why I Belong to ACM
Hear from Bryan Cantrill, vice president of engineering at Joyent, Ben Fried chief information officer at Google, and Theo Schlossnagle, OmniTI founder on why they are members of ACM.
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.
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.