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BigGraphs 2016 : Third IEEE Big Data Workshop on High Performance Big Graph Data Management, Analysis, and Mining

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Link: http://www.biggraphs.org/
 
When Dec 5, 2016 - Dec 8, 2016
Where Washington, D.C.
Submission Deadline Oct 20, 2016
Notification Due Nov 6, 2016
Final Version Due Nov 15, 2016
Categories    big data   data mining   high performance computing   machine leraning
 

Call For Papers

The Third International Workshop on High Performance
Big Graph Data Management, Analysis, and Mining (BigGraphs 2016)

To be held in conjunction with IEEE BigData 2016
Dec 5-8, 2016, Washington, D.C., USA.

Website:
http://www.biggraphs.org

Important Dates:
Oct 20, 2016: Submission deadline
Nov 6, 2016: Notification of paper acceptance to authors
Nov 15, 2016: Camera-ready submissions due

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 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

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 2016
conference proceedings. Papers must be submitted online through the workshop submissions
page (http://wi-lab.com/cyberchair/2016/bigdata16/scripts/submit.php?subarea=S19)
by 11.59 pm PDT (Pacific Daylight Time) on October 20, 2016.

Workshop Organizers:
Nesreen Ahmed
Intel Labs
nesreen.k.ahmed@intel.com

Mohammad Al Hasan
Indiana University-Purdue University Indianapolis
alhasan@cs.iupui.edu

Kamesh Madduri
The Pennsylvania State University
madduri@cse.psu.edu

Program Committee:
Nesreen Ahmed (Intel Labs)
Mohammad Al Hasan (Indiana University - Purdue University)
Ariful Azad (Lawrence Berkeley National Laboratory)
Sanjukta Bhowmick (University of Nebraska at Omaha)
Mehmet Deveci (Sandia National Laboratories)
Nick Duffield (Texas A&M University)
Assefaw Gebremedhin (Washington State University)
Rong Zhou (Palo Alto Research Center)
Oded Green (Georgia Institute of Technology)
Irena Holubova (Charles University)
Kamesh Madduri (The Pennsylvania State University)
Ali Pinar (Sandia National Laboratories)
Ryan Rossi (Palo Alto Research Center)
George Slota (Rensselaer Polytechnic Institute)
Ted Willke (Intel Labs)
Yinglong Xia (Huawei Research America)
Narayanan Sundaram (Intel Labs)

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