%0 Journal Article %1 corbellini2020:analysis %A Corbellini, Alejandro %A Godoy, Daniela %A Mateos, Cristian %A Schiaffino, Silvia %A Zunino, Alejandro %D 2020 %I Taylor & Francis %J IETE Journal of Research %K Graph Processing bigdata distributed myown web %N 0 %P 1-9 %R 10.1080/03772063.2020.1754139 %T An Analysis of Distributed Programming Models and Frameworks for Large-scale Graph Processing %U https://doi.org/10.1080/03772063.2020.1754139 %V 0 %X In recent years, processing and analyzing large graphs has become a major need in many research areas. Distributed graph processing programming models and frameworks arised as a natural solution to process linked data of large volumes, such as data originating from social media. These solutions are distributed by design and help developers to perform operations on the graph, sometimes reaching almost real-time performance even on huge graphs. Some of the available graph processing frameworks exploit generic data processing models, 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 – from the perspective of the adopted programming model – designed to process large-scale graphs with the goal of assisting software developers/designers in choosing the most adequate tool.

@article{corbellini2020:analysis, abstract = {In recent years, processing and analyzing large graphs has become a major need in many research areas. Distributed graph processing programming models and frameworks arised as a natural solution to process linked data of large volumes, such as data originating from social media. These solutions are distributed by design and help developers to perform operations on the graph, sometimes reaching almost real-time performance even on huge graphs. Some of the available graph processing frameworks exploit generic data processing models, 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 – from the perspective of the adopted programming model – designed to process large-scale graphs with the goal of assisting software developers/designers in choosing the most adequate tool. }, added-at = {2020-05-06T23:23:03.000+0200}, author = {Corbellini, Alejandro and Godoy, Daniela and Mateos, Cristian and Schiaffino, Silvia and Zunino, Alejandro}, biburl = {https://www.bibsonomy.org/bibtex/28c4c5dfb7501f2a2d68dc600d66addb2/azunino}, doi = {10.1080/03772063.2020.1754139}, eprint = {https://doi.org/10.1080/03772063.2020.1754139}, interhash = {994c3c28ba13688ef0489b2b27fb3a95}, intrahash = {8c4c5dfb7501f2a2d68dc600d66addb2}, journal = {IETE Journal of Research}, keywords = {Graph Processing bigdata distributed myown web}, number = 0, pages = {1-9}, publisher = {Taylor & Francis}, timestamp = {2020-05-06T23:23:03.000+0200}, title = {An Analysis of Distributed Programming Models and Frameworks for Large-scale Graph Processing}, url = {https://doi.org/10.1080/03772063.2020.1754139 }, volume = 0, year = 2020 }