In graph-based learning models, entities are often represented as vertices in an undirected
graph with weighted edges describing the relationships between entities. In many real-world …

MapReduce is becoming the state-of-the-art computing paradigm for processing large-scale
datasets on a large cluster with tens or thousands of nodes. It has been widely used in …

The trend towards multi-core/many-core architectures is well underway. It is therefore
becoming very important to develop software in ways that take advantage of such parallel …

In this paper we explore a new model of mining general temporal association rules from
large databases where the exhibition periods of the items are allowed to be different from …

In this paper, we explore the challenges and needs of current cloud infrastructures, to better
support cloud-based data-intensive applications that are not only latency-sensitive but also …

With the widespread adoption of multi-core architectures, it is becoming more important to
develop software in ways that takes advantage of such parallel architectures. This …

It is widely assumed that scheduling real-time tasks becomes more difficult as their
deadlines get shorter. With deadlines shorter, however, tasks potentially compete less with …

It has been widely studied how to schedule real-time tasks on multiprocessor platforms.
Several studies have developed optimal scheduling policies for implicit deadline task …

With increasing popularity and deployment of multi-core chips in embedded systems, a
number of real-time multiprocessor scheduling algorithms have been proposed along with …

Unlike uniprocessor scheduling, EDF (categorized into job-level fixed-priority (JFP)
scheduling) shows relatively poor performance on global multiprocessor scheduling. As no …