The ACM Guide to Computing Literature
The ACM Guide to Computing Literature is the most comprehensive bibliographic database in existence today focused exclusively on the field of computing, making this A&I service—which seamlessly integrates with ACM’s full-text articles—a true starting point for anyone looking to search and access computing’s rapidly growing archive.
- 2,237,215 Bibliographic Records
- 1,406,570 Abstracts
- 231,000 Distinct Titles
- 6,000+ Publishers' Content
- 1,037,013 Conference Proceedings records
- 882,391 Journals & Magazines
- 183,538 Books
- 73,689 Theses
- 25,398 Technical Reports
- 3,771 RFC Documents
More than an index, the Guide to Computing Literature serves as the engine that drives the most exciting functionality of the ACM Digital Library, including features such as ACM Author Profile Pages, which includes bibliographic and bibliometric data for over 1,500,000 authors in the field of computer science, and the ACM Institutional Profile Pages, which includes bibliographic and bibliometric data for every academic, government, and industry organization publishing articles in the field.
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.