| |||||||||||||||
BigGraphs 2015 : The Second International Workshop on High Performance Big Graph Data Management, Analysis, and Mining (BigGraphs 2015) | |||||||||||||||
Link: http://www.biggraphs.org | |||||||||||||||
| |||||||||||||||
Call For Papers | |||||||||||||||
[Apologies if you received multiple copies of this message]
The Second International Workshop on High Performance Big Graph Data Management, Analysis, and Mining (BigGraphs 2015) To be held in conjunction with IEEE BigData 2015 Oct 29--Nov 1, 2015, Santa Clara, CA, USA. Website: http://www.biggraphs.org Important Dates: Sep 5, 2015: Due date for workshop papers submission Sep 25, 2015: Notification of paper acceptance to authors Oct 5, 2015: Camera-ready of accepted papers Call for papers: 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 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 Submissions must be at most 8 pages long, including all figures, tables, and references. They must be formatted according to the style files used by the IEEE BigData 2015 conference proceedings. Papers must be submitted online through the workshop submission page (https://wi-lab.com/cyberchair/2015/bigdata15/scripts/submit.php?subarea=S06) by 11.59 pm PDT (Pacific Daylight Time) on September 5, 2015. Workshop Organizers: Mohammad Al Hasan Department of Computer and Information Science Indiana University-Purdue University Indianapolis Indianapolis, IN 46202 alhasan@cs.iupui.edu Kamesh Madduri Department of Computer Science and Engineering The Pennsylvania State University University Park, PA 16802 madduri@cse.psu.edu Fengguang Song Department of Computer and Information Science Indiana University-Purdue University Indianapolis Indianapolis, IN 46202 fgsong@cs.iupui.edu Program Committee: Leman Akoglu (Stony Brook University) Medha Atre (University of Pennsylvania) Juan Colmenares (Samsung Research America) Oded Green (Georgia Institute of Technology) Mahantesh Halappanavar (Pacific Northwest National Laboratory) Mohammad Al Hasan (Indiana University Purdue University) Kamesh Madduri (The Pennsylvania State University) Erik Saule (University of North Carolina at Charlotte) Fengguang Song (Indiana University Purdue University) Chen Tian (Huawei Technologies USA) Stanimire Tomov (University of Tennessee Knoxville) Mohammed J. Zaki (Rensselaer Polytechnic Institute) |
|