With the advent of the electric power big data era, semantic interoperability and
interconnection of power data have received extensive attention. Knowledge graph …

Big data task scheduling in cloud computing environments has gained considerable
attention in the past few years, due to the exponential growth in the number of businesses …

Big data-based acquisition and storage system (ASS) plays an important role in the design
of industrial data platform. Many big data frameworks have been integrated compression …

As a lightweight, flexible, and high-performance operating system virtualization, containers
are used to speed up the big data platform. However, due to the imperfection of the resource …

Today enterprises have massive stream data that require to be processed in real time due to
data explosion in recent years. Spark Streaming as an emerging system is developed to …

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 …

At present, big data is very popular, because it has proved to be much successful in many
fields such as social media, E-commerce transactions, etc. Big data describes the tools and …

In the distributed computing framework of Spark, cross-node/rack data transfer produced by
map tasks and reduce tasks are common problems resulting in performance degradation …

The concept of data locality is crucial for distributed systems (eg, Spark and Hadoop) to
process Big Data. Most of the existing research optimized the data locality from the aspect of …

Apache Spark is a popular framework for processing big data. Running Spark on Hadoop
YARN allows it to schedule Spark workloads alongside other data-processing frameworks …