Sentence examples for graph processing from inspiring English sources

We focus on systems that are modeled after Google's Pregel framework for large-scale distributed graph processing.

We then introduce GraphTau, a time-evolving graph processing framework built on top of Apache Spark, a widely used distributed dataflow system.

This talk will give an overview of how Adobe is tackling this problem using graph processing techniques, going over our journey from the initial research ideas to a fully productized product that works at scale running in the cloud while respecting user privacy.

Applying such an algorithm in a cloud-based environment will yield the same benefits, with potential applications to graph processing.

Faisal et al. [6] proposed an approach of identifying the boundary nodes in a community, as they played a vital role in communication for energy-efficient graph processing.

This demonstrates that, even with an interconnect as fast as the K-Computer, network is still the bottleneck for large graphs, and as such, further hardware and algorithmic improvements are desirable for future extreme graph processing.

In recent years, many distributed graph-processing systems have been designed and developed to analyze large-scale graphs.

We begin by modeling the execution time of distributed graph-processing systems.

By using three typical large-scale graphs and three popular graph-processing algorithms, we conduct comprehensive experiments to study the performance of our and of many alternative streaming policies on a real distributed graph-processing system.

GraphX recast the essential patterns and optimizations in graph-processing systems as new distributed join strategies and new techniques for incremental materialized view maintenance.

This paper serves the dual role of describing the GPS system, and presenting techniques and experimental results for graph partitioning in distributed graph-processing systems like GPS.