Large-scale graph processing and applications
Tipo de documentoTexto en actas de congreso
Fecha de publicación2017-09-10
EditorBarcelona Supercomputing Center
Condiciones de accesoAcceso abierto
In the Big Data era, graph processing has been widely used to represent complex system structure, capture data dependency and uncover relationship insights. Due to the ever-growing graph scale and algorithm complexity, several distributed graph processing frameworks have attracted many interests from both academia and industry. In this talk, I will investigate how to achieve the trade-off between performance and cost for large scale graph processing on the Cloud. System-aware and machine learning models are developed to predict the performance of distributed graph processing tasks. Consequently, cost-efficient resource provisioning strategies could be recommended by selecting a certain number of VMs with specified capability subject to the predefined resource price and user preference. At the end of this talk, I will briefly introduce our recent projects on urban computing, disease simulation and social network analytics based on graph processing and real world data.
CitaciónZengxiang Li, S. Large-scale graph processing and applications. A: 3rd Severo Ochoa Research Seminar Lectures at BSC, Barcelona, 2016-2017. "Book of abstracts". Barcelona: Barcelona Supercomputing Center, 2017, p. 35-36.