Graph database models can be defined as those in which data structures for the schema
and instances are modeled as graphs or generalizations of them, and data manipulation is …

With the prevalence of graph data in a variety of domains, there is an increasing need for a
language to query and manipulate graphs with heterogeneous attributes and structures. We …

We study fundamental aspects related to the efficient processing of the SPARQL query
language for RDF, proposed by the W3C to encode machine-readable information in the …

Recent scientific and technological advances have witnessed an abundance of structural
patterns modeled as graphs. As a result, it is of special interest to process graph …

GraphGrep is an application-independent method for querying graphs, finding all the
occurrences of a subgraph in a database of graphs. The interface to GraphGrep is a regular …

The value of graph-based big data can be unlocked by exploring the topology and metrics of
the networks they represent, and the computational approaches to this exploration take on …

With the rise of social networks, large-scale graph analysis becomes increasingly important.
Because SQL lacks the expressiveness and performance needed for graph algorithms …

There is an increasing need to ingest, manage, and query large volumes of graph-structured
data arising in applications like social networks, communication networks, biological …

Bidirectional transformations provide a novel mechanism for synchronizing and maintaining
the consistency of information between input and output. Despite many promising results on …

Motivation: Many areas of modern biology are concerned with the management, storage,
visualization, comparison and analysis of networks, but no appropriate query language for …