Big data benchmark suites must include a diversity of data and workloads to be useful in
fairly evaluating big data systems and architectures. However, using truly comprehensive …

Today's Internet Services are undergoing fundamental changes and shifting to an intelligent
computing era where AI is widely employed to augment services. In this context, many …

Big data systems address the challenges of capturing, storing, managing, analyzing, and
visualizing big data. Within this context, developing benchmarks to evaluate and compare …

Spark has been increasingly employed by industries for big data analytics recently, due to its
resilience, scalability and efficient in-memory distributed programming model. Meanwhile …

In last decade, data analytics have rapidly progressed from traditional disk-based
processing to modern in-memory processing. However, little effort has been devoted at …

Several fundamental changes in technology indicate domain-specific hardware and
software co-design is the only path left. In this context, architecture, system, data …

Many research works deal with big data platforms looking forward to data science and
analytics. These are complex and usually distributed environments, composed of several …

Big data applications (eg, Extract, Transform, and Load (ETL) applications) are designed to
handle great volumes of data. However, processing such great volumes of data is time …

Abstract The field of Big Data and related technologies is rapidly evolving. Consequently,
many benchmarks are emerging, driven by academia and industry alike. As these …

Heterogeneous memory systems that comprise memory nodes based on widely-different
device technologies (eg, DRAM and nonvolatile memory (NVM)) are emerging in various …