Recent demands for storing and querying big data have revealed various shortcomings of
traditional relational database systems. This, in turn, has led to the emergence of a new kind …

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 …

Organizations face a data discovery problem when their analysts spend more time looking
for relevant data than analyzing it. This problem has become commonplace in modern …

Graph data modeling and querying arises in many practical application domains such as
social and biological networks where the primary focus is on concepts and their …

Many public knowledge bases are represented and stored as RDF graphs, where users can
issue structured queries on such graphs using SPARQL. With massive queries over large …

The specific characteristics of graph workloads make it hard to design a one-size-fits-all
graph storage system. Systems that support transactional updates use data structures with …

Recently, many applications have required the ability to perform dynamic graph analytical
processing (GAP) tasks on the datasets generated by relational OLTP in real time. To meet …

Knowledge graphs (KGs) such as DBpedia, Freebase, YAGO, Wikidata, and NELL were
constructed to store large-scale, real-world facts as (subject, predicate, object) triples-that …

Many big data applications in business and science require the management and analysis
of huge amounts of graph data. Suitable systems to manage and to analyze such graph data …

Evolving graphs in the real world are large-scale and constantly changing, as hundreds of
thousands of updates may come every second. Monotonic algorithms such as Reachability …