In this work we present a cloud-based distributed processing framework designed for large-scale temporal graphs. By using computing resources in the cloud this ...
Abstract—Large-scale temporal graphs can serve as a model in many application scenarios. Recently, due to the popularity.
Jun 24, 2014 · In this work we present a cloud-based distributed processing framework designed for large- scale temporal graphs. By using computing resources ...
Bibliographic details on Towards Cloud-Based Distributed Scaleable Processing over Large-Scale Temporal Graphs.
2016. Towards Cloud-based Distributed Scaleable Processing over Large-scale Temporal Graphs. M Steinbauer, G Kotsis. 2014 IEEE 23rd International WETICE ...
This necessitates the use of distributed systems to scale to large graphs [27], [41]. Time is an increasingly common graph feature in a variety of domains ...
Missing: Scaleable | Show results with:Scaleable
ABSTRACT. Graph models have a long standing history as models for real world structures and processes. In recent research two important dimensions of graphs ...
Apr 11, 2016 · In this work a distributed computing framework designed for storing and processing of large-scale temporal graphs is presented. For this system ...
This work presents a cloud-based distributed processing framework extending the Pregel paradigm for large-scale temporal graphs that is scalable and ...
Missing: Scaleable | Show results with:Scaleable
Mar 7, 2024 · graph processing techniques in cloud computing and guides future research efforts towards more efficient and scalable graph processing in cloud.
Missing: Scaleable | Show results with:Scaleable