Numerous irregular graph datasets, for example social networks or web graphs, may contain
even trillions of edges. Often, their structure changes over time and they have domain …

With the fast development of big data systems in recent years, a variety of open-source
benchmarks have been built to evaluate and compare the workloads on these systems, and …

Most of the current graph database systems have been designed to support property graphs.
Surprisingly, there is no standard specification of the database model behind such systems …

NoSQL and especially graph databases are constantly gaining popularity among
developers of Web 2.0 applications as they promise to deliver superior performance when …

The introduction of Google's Pregel generated much interest in the field of large-scale graph
data processing, inspiring the development of Pregel-like systems such as Apache Giraph …

In recent years, more and more companies provide services that can not be anymore
achieved efficiently using relational databases. As such, these companies are forced to use …

We show that existing mature, relational optimizers can be exploited with a novel schema to
give better performance for property graph storage and retrieval than popular noSQL graph …

Graph databases (GDBs) are crucial in academic and industry applications. The key
challenges in developing GDBs are achieving high performance, scalability …

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 …

The abundance of interconnected data has fueled the design and implementation of graph
generators reproducing real-world linking properties or gauging the effectiveness of graph …