A study by Alberto Cano, Ph.D., assistant professor of computer science, is one of the top 10 most cited papers in Wiley Interdisciplinary Reviews (WIREs) Data Mining and Knowledge Discovery.
Cano’s article, published in January 2018, focuses on the use of graphics processing units (GPUs) for large-scale data mining. The GPU gained popularity starting in 1999 for providing smooth graphics for videos and games. More recently, however, these devices have been deployed to process large volumes of data moving at high speeds that overwhelm traditional machine learning and data mining methods.
“A survey on graphic processing unit computing for large‐scale data mining” analyzes trends in using GPUs for mining big data, including the practice of combining them with distributed frameworks for increased performance. The study also discusses architecture solutions for handling data volume and velocity and identifies factors that hinder scalability.
WIREs promotes cross-disciplinary research and seeks to provide scholars encyclopedic coverage of the field. Contributors are invited by the journal’s international editorial board.