Research on big data analytics is entering in the new phase called fast data where multiple
gigabytes of data arrive in the big data systems every second. Modern big data systems …

We are witnessing the emergence of new big data processing architectures due to the
convergence of the Internet of Things (IoTs), edge computing and cloud computing. Existing …

Big data processing tools have evolved rapidly in recent years. MapReduce has proven very
successful but is not optimized for many important analytics, especially those involving …

The evolution of the Internet and personal devices has changed our modem world to a new
age of data. The data now is not only big in volume and size but also huge in varieties and …

In increasing numbers, universities are providing wireless access to the Internet in
classrooms and are requiring entering freshmen to have laptop computers. Moreover …

In this paper, we apply a previously proposed data conversion system that leverages the α-
lightweight coreset for efficient and accurate validation in the context of big data. The system …

We categorize parallel machine learning applications into four types of computation models
and propose a new set of model-centric computation abstractions. This work sets up parallel …

This work investigates the problem of a non-linear divisible load distribution on a
homogeneous linear network. A novel computational model of non-linear loads that includes …

This work considers the divisible load distribution problem on heterogeneous linear
networks. A divisible load distribution determines optimal fractions of the load and assigns …

Large-scale iterative computations are common in many important data mining and machine
learning algorithms. Most of these applications can be specified as iterations of MapReduce …