3rd IEEE Big Data Workshop on High Performance Big Graph Data Management, Analysis, and Mining, Washington, Vereinigte Staaten

Abbrevation

BigGraphs

Link

http://www.biggraphs.org

City

Washington

Country

United States

Deadline Paper

2016-10-20

Start Date

2016-12-05

End Date

2016-12-08

Abstract

Modern Big Data increasingly appears in the form of complex graphs and networks. Examples include the physical Internet, the world wide web, online social networks, phone networks, and biological networks. In addition to their massive sizes, these graphs are dynamic, noisy, and sometimes transient. They also conform to all five Vs (Volume, Velocity, Variety, Value and Veracity) that define Big Data. However, many graph–related problems are computationally difficult, and thus big graph data brings unique challenges, as well as numerous opportunities for researchers, to solve various problems that are significant to our communities. This workshop aims to bring together researchers from different paradigms solving big graph problems under a unified platform for sharing their work and exchanging ideas. We are soliciting novel and original research contributions related to big graph data management, analysis, and mining (algorithms, software systems, applications, best practices, performance). Significant work–in–progress papers are also encouraged. Papers can be from any of the following areas, including but not limited to: * Parallel algorithms for big graph analysis on HPC systems * Heterogeneous CPU–GPU solutions to solve big graph problems * Extreme–scale computing for large graph, tensor, and network problems * Sampling and summarization of large graphs * Graph algorithms for large–scale scientific computing problems * Graph clustering, partitioning, and classification methods * Scalable graph topology measurement: diameter approximation, eigenvalues, triangle and graphlet counting * Parallel algorithms for computing graph kernels * Inference on large graph data * Graph evolution and dynamic graph models * Graph streams * Graph databases, novel querying and indexing strategies for RDF data * Novel applications of big graph problems in bioinformatics, health care, security, and social networks * New software systems and runtime systems for big graph data mining