Search the Guide to Computing Literature vs. Searching the ACM Digital Library
The ACM Digital Library is ACM’s online platform which contains the full text of every article every published by ACM. The ACM Digital Library is fully integrated with the ACM Guide to Computing Literature, an extensive collection of bibliographic citations from major publishers in computing.
Because of the integration of the ACM Digital Library with the Guide to Computing Literature users of the ACM Digital Library will often find citations in their search results which belong to other publishers and are not included with their subscription to the ACM Digital Library.
To ensure you are searching only the full text of the ACM Digital Library, start by running a search at https://dl.acm.org. On the top left of your search results will indicate if your search results are within “Publications of the ACM and Affiliated Organizations” or within the “The ACM Guide to Computing Literature”.
You can toggle between searching the Guide and the ACM Digital Library by clicking on or Expand your search to The ACM Guide to Computing Literature or Limit your search to Publications from ACM and Affiliated Organizationsrespectively.
Also, subscribers of the ACM Digital Library can quickly identify which articles they have full-text access to as indicated by the ACM diamond on the left or each article and the availability of full-text button on the search results or citation page.
Over the coming months, additional work will be done to further highlight the users’ ability to search full-text or bibliographic records.
The ACM Digital Library is fully searchable across all of ACM’s publications along with the entire contents of the ACM Guide to Computing Literature. Search results contain “guided navigation” to enable knowledge discovery and topic refinement. Search results can be limited by fields such as publication date, content formats, and publication names.
Advanced search is also available for those users that prefer the more traditional query formulation.
An evolving set of statistics gathered through the ACM Digital Library at various levels of aggregation. The core building block is a set of statistics present on each article citation page containing accumulated statistics on publications, citations, and download statistics.
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's prestigious conferences and journals are seeking top-quality papers in all areas of computing and IT. It is now easier than ever to find the most appropriate venue for your research and publish with ACM.
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.