The vertex-centric programming model is an established computational paradigm recently
incorporated into distributed processing frameworks to address challenges in large-scale …

The world is becoming a more conjunct place and the number of data sources such as
social networks, online transactions, web search engines, and mobile devices is increasing …

This paper studies incremental graph partitioning. Given a (vertex-cut or edge-cut) partition
C (G) of a graph G and updates ΔG to G, it is to compute changes ΔO to C (G), yielding a …

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 …

Graphs are a tremendously suitable data representations that model the relationships of
entities in many application domains, such as recommendation systems, machine learning …

In recent years, the graph partitioning problem gained importance as a mandatory
preprocessing step for distributed graph processing on very large graphs. Existing graph …

Billions of devices will compose the IoT system in the next few years, generating a huge
amount of data. We can use fog computing to process these data, considering that there is …

This paper proposes a class of temporal association rules, denoted by TACOs, for event
prediction. As opposed to previous graph rules, TACOs monitor updates to graphs, and can …

One of the concepts that attracts attention since entering of big data era is the graph-
structured data. Suitable frameworks to handle such data would face several constraints …

Balanced graph partitioning is an NP-complete problem with a wide range of applications.
These applications include many large-scale distributed problems, including the optimal …