The overarching goal of FiDoop-DP is to boost the performance of parallel Frequent Itemset Mining on Hadoop clusters. At the heart of FiDoop-DP is the Voronoi diagram-based data partitioning technique ...

Traditional edge detection approaches cannot detect edges in images in a timely manner due to memory and computational time constraints. In this work, a novel parallelized ant colony optimization ...

Our largest cluster now has ~10,000 nodes, one of the largest (if not the largest) Hadoop clusters on the planet. Scaling Hadoop YARN has emerged as one of the most challenging tasks for our ...

Analysis on Hadoop Cluster Configuration The feasibility of visualized analysis model for Hadoop business data can be verified by experiments and the experiment can be further verified by establishing ...

In DataWorks, emerging support in Hadoop for libraries such as Tensorflow, MXNet, and Caffe on Hadoop clusters was highlighted.

Driverless AI really is able to create and train good machine learning models without requiring machine learning expertise from users.

Work on this integration of deep learning and Hadoop comes from the least surprising quarters—Yahoo, the home of Hadoop and MapReduce over a decade ago. Yahoo’s main internal cluster for research, ...

Much has changed since the Hadoop project was originally released. This podcast reviews the latest trends in the world of Hadoop and distributed computing.

The number of production Hadoop clusters is growing, but far too often, that means the number of dedicated clusters just for running it is expanding as well. This means a lot of extra management, ...