Temporal property graphs are graphs whose structure and properties change over time.
Temporal graph datasets tend to be large due to stored historical information, asking for …

Contextual information is a crucial factor in interdisciplinary research on social networks. At
the same time, missing linked data and missing methods for large-scale knowledge …

Today's systems are capable of storing large amounts of data in main memory. Particularly,
in-memory DBMSs benefit from this development. However, the processing of data from the …

IoT applications can be naturally modeled as a graph where the edges represent the
interactions between devices, sensors, and their environment. Thing'in 1 1 https://www …

Graph database systems are increasingly adapted for storing and processing
heterogeneous network-like datasets. However, due to the novelty of such systems, no …

Graph pattern matching is an important and challenging operation on graph data. Typical
use cases are related to graph analytics. Since analysts are often non-programmers, a …

In the current era of big data, high volumes of valuable data can be easily collected and
generated. Social networks are examples of generating sources of these big data. Users in …

Graph data management has revealed beneficial characteristics in terms of flexibility and
scalability by differently balancing between query expressivity and schema flexibility. This …

Graph databases are used for different applications like analyzing large networks,
representing and querying knowledge graphs, and managing master data and complex data …

Graph Database Management Systems (GDBMs) provide an effective and efficient solution
to data storage in current scenarios where data are more and more connected, graph …