Digital Library Platform and Features

The ACM Digital Library Platform is ACM’s own proprietary system that is wholly developed, hosted, and maintained by ACM. The system is built on open source technology in collaboration with volunteers from the scientific community and contains many of the most powerful search features available today.

  • Powerful Search and Guided Navigation
  • Seamless Integration between DL and Guide Index
  • Most Comprehensive Database of Author Profiles, including detailed bibliometrics of nearly every author in the field
  • A Complete List of Index Terms for each article based on the ACM’s widely used 2012 Computing Classification Scheme (CCS)
  • Fully exportable citation pages in Endnote, BibTex, and ACM Ref formats
  • Extensive use of DOIs with reference linking provided through CrossRef
  • IP Authentication and Domain Name Look Up for Institutional Customers
  • Supports Athens and Shibboleth authentication
  • Open URL and SFX Compliant
  • COUNTER III and SUSHI Compliant Usage Statistics
  • Archiving and Long-Term Digital Preservation of all ACM-owned publications via Portico and CLOCKSS

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.

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.

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.