Add to ORKGThere is a growing need for distributed graph processing systems that are capable of gracefully scaling to very large graph datasets. Unfortunately, this challenge has not been easily met due to the in-tense memory pressure imposed by process-centric, message pass-ing designs that many graph processing systems follow. Pregelix is a new open source distributed graph processing system that is based on an iterative dataflow design that is better tuned to han-dle both in-memory and out-of-core workloads. As such, Pregelix offers improved performance characteristics and scaling properties over current open source systems (e.g., we have seen up to 15× speedup compared to Apache Giraph and up to 35 × speedup com-pared to distributed GraphLab), and...
Graphs are very important parts of Big Data and widely used for modelling complex structured data wi...
Cloud computing frameworks today are being used to process extremely large graphs with billions of v...
For large-scale graph analytics on the GPU, the irregularity of dataaccess/control flow and the comp...
model [2] for Big Graph analytics, where application pro-grammers need no knowledge of parallel or d...
Recently, there is a growing need for distributed graph processing systems that are capable of grace...
In this age of information, data gathering has become a new growing trend. Social networking sites, ...
Existing distributed graph analytics systems are categorized into two main groups: those that focus ...
Existing distributed graph processing frameworks, e.g., Pregel, Gi-raph, GPS and GraphLab, mainly ex...
Large-scale graph analytics has gained attention during the past few years. As the world is going to...
The introduction of Google’s Pregel generated much inter-est in the field of large-scale graph data ...
From social networks to language modeling, the growing scale and importance of graph data has driven...
With the prevalence of graph data in real-world applications (e.g., social networks, mobile phone ne...
Graph algorithms are becoming increasingly important for analyzing large datasets in many fields. Re...
Large-scale graph processing, with its massive data sets, requires distributed processing. However, ...
Although using graphs to represent networks and relationship is not new; the size of network has bee...
Graphs are very important parts of Big Data and widely used for modelling complex structured data wi...
Cloud computing frameworks today are being used to process extremely large graphs with billions of v...
For large-scale graph analytics on the GPU, the irregularity of dataaccess/control flow and the comp...
model [2] for Big Graph analytics, where application pro-grammers need no knowledge of parallel or d...
Recently, there is a growing need for distributed graph processing systems that are capable of grace...
In this age of information, data gathering has become a new growing trend. Social networking sites, ...
Existing distributed graph analytics systems are categorized into two main groups: those that focus ...
Existing distributed graph processing frameworks, e.g., Pregel, Gi-raph, GPS and GraphLab, mainly ex...
Large-scale graph analytics has gained attention during the past few years. As the world is going to...
The introduction of Google’s Pregel generated much inter-est in the field of large-scale graph data ...
From social networks to language modeling, the growing scale and importance of graph data has driven...
With the prevalence of graph data in real-world applications (e.g., social networks, mobile phone ne...
Graph algorithms are becoming increasingly important for analyzing large datasets in many fields. Re...
Large-scale graph processing, with its massive data sets, requires distributed processing. However, ...
Although using graphs to represent networks and relationship is not new; the size of network has bee...
Graphs are very important parts of Big Data and widely used for modelling complex structured data wi...
Cloud computing frameworks today are being used to process extremely large graphs with billions of v...
For large-scale graph analytics on the GPU, the irregularity of dataaccess/control flow and the comp...