Distributed computing has become a common approach for large-scale computation tasks
due to benefits such as high reliability, scalability, computation speed, and cost …

With the explosive increase of big data in industry and academic fields, it is important to
apply large-scale data processing systems to analyze Big Data. Arguably, Spark is the state …

Spark is widely used due to its high performance caching mechanism and high scalability,
which still causes uneven workloads and produces useless intermediate caching results …

Multi-dimensional data anonymization approaches (eg, Mondrian) ensure more fine-grained
data privacy by providing a different anonymization strategy applied for each attribute. Many …

Distributed computing has become a common approach for large-scale computation of tasks
due to benefits such as high reliability, scalability, computation speed, and costeffectiveness …

Hybrid cloud bursting (ie, leasing temporary off-premise cloud resources to boost the overall
capacity during peak utilization) can be a cost-effective way to deal with the increasing …

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 …

Public cloud computing platforms are a cost-effective solution for individuals and
organizations to deploy various types of workloads, ranging from scientific applications …

High-performance data processing systems typically utilize numerous servers with large
amounts of memory. An essential operation in such environment is the parallel join, the …