Graph data management concerns the research and development of powerful technologies
for storing, processing and analyzing large volumes of graph data. This chapter presents an …

Research interests spanning numerous domains increasingly rely upon computational
systems which can store and process a large volume of variable data that is stored at high …

The unprecedented amount of data that needs to be processed in emerging data analytics
applications poses novel challenges to industry and academia. Scalability and high …

Graph databases are becoming a critical tool for the analysis of graph-structured data in the
context of multiple scientific and technical domains, including cybersecurity and …

We present a visual analytics paradigm and a system prototype for exploring web-scale
graphs. A web-scale graph is described as a graph with∼ one trillion edges and∼ 50 billion …

Heterogeneous disaggregated systems represent a promising solution to deliver the
performance required by next-generation High-Performance Computing (HPC) workloads …

Graphs represent an increasingly popular data model for data-analytics, since they can
naturally represent relationships and interactions between entities. Relational databases …

Irregular applications, by their very nature, suffer from poor data locality. This often results in
high miss rates for caches, and many long waits to off-chip memory. Historically, long …

The growing popularity of dynamic applications such as social networks provides a
promising way to detect valuable information in real time. These applications create high …

The RFID and sensor network based ubiquitous computing has great potential in medical
and healthcare services. Most of medical accidents around patients are depended on …