An Analysis of Distributed Programming Models and Frameworks for Large-Scale Graph Processing [JCR]

ALEJANDRO CORBELLINI; DANIELA GODOY; CRISTIAN MATEOS; SILVIA SCHIAFFINO; ALEJANDRO ZUNINO

IETE JOURNAL OF RESEARCH

MEDKNOW PUBLICATIONS

Lugar: New Dehli; Año: 2020

0377-2063

In recent years, processing and analyzing large graphs has become a major need in many research areas. As a consequence, distributed graph processing frameworks arised as a natural solution to process linked data of large volumes, such as data originating from Social Media. These frameworks are distributed by design and help users to perform operations on the graph, sometimes reaching almost real-time performance even on huge graphs. Some of the available graph processing frameworks run on top of generic frameworks, like MapReduce, while others were specifically built for graph processing, introducing techniques such as vertex or edge partitioning and graph-oriented programming models. In this work, we analyse the properties of recent and widely popular frameworks designed to process large-scale graphs with the goal of assisting software designers in choosing the most adequate tool.</div></div></body></html></div></div></body></html>