Traditionally, on-demand, rigid, and malleable applications have been scheduled and
executed on separate systems. The ever-growing workload demands and rapidly …

Stream processing applications deal with millions of data items continuously generated over
time. Often, they must be processed in real-time and scale performance, which requires the …

With an escalating arms race to adopt machine learning (ML) in diverse application
domains, there is an urgent need to support declarative machine learning over distributed …

Data‐driven applications are essential to handle the ever‐increasing volume, velocity, and
veracity of data generated by sources such as the Web and Internet of Things (IoT) devices …

Abstract In Tencent Inc., more than 80% of the data are extracted and transformed using
Spark. However, the commonly used machine learning systems are TensorFlow, XGBoost …

The increasing popularity of Apache Spark has attracted many users to put their data into its
ecosystem. On the other hand, it has been witnessed in the literature that Spark is slow …

Tensors, or N-dimensional arrays, are increasingly used to represent multi-dimensional
data. Sparse tensor decomposition algorithms are of particular interest in analyzing and …

Convergence between high-performance computing (HPC) and big data analytics (BDA) is
currently an established research area that has spawned new opportunities for unifying the …

Today's scientific applications are increasingly relying on a variety of data sources, storage
facilities, and computing infrastructures, and there is a growing demand for data analysis …

The dawn of exascale computing and its convergence with big data analytics has greatly
spurred research interests. The reasons are straightforward. Traditionally, high performance …