The limitations of traditional databases, in particular the relational model, to cover the
requirements of current applications has lead the development of new database …

Graph databases have gained renewed interest in the last years, due to its applications in
areas such as the Semantic Web and Social Networks Analysis. We study the problem of …

We present HyperGraphDB, a novel graph database based on generalized hypergraphs
where hyperedges can contain other hyperedges. This generalization automatically reifies …

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 …

A graph is a structure composed of a set of vertices (ie nodes, dots) connected to one
another by a set of edges (ie links, lines). The concept of a graph has been around since the …

The analysis of the relationship among data entities has lead to model them as graphs.
Since the size of the datasets has significantly grown in the recent years, it has become …

Graph traversals are a basic but fundamental ingredient for a variety of graph algorithms and
graph-oriented queries. To achieve the best possible query performance, they need to be …

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 …

Large scale graph processing represents an interesting challenge due to the lack of locality.
This paper presents Path Graph for improving iterative graph computation on graphs with …

Abstract Graph Database Management systems (GDBs) are gaining popularity. They are
used to analyze huge graph datasets that are naturally appearing in many application areas …