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[Computational-biology] CFP: International Workshop on High Performance Big Graph Data Management, Analysis, and Mining

bigGraph Data via comp-bio%40net.bio.net (by biggraphdata from gmail.com)
Thu Jul 31 08:19:50 EST 2014


== International Workshop on High Performance Big Graph Data Management,
Analysis, and Mining ==

To be held in conjunction with IEEE BigData’14 Oct 27-Oct 30, 2014,
Washington DC, USA.
(http://cs.iupui.edu/~fgsong/biggraphsworkshop)

Important Dates:
  Aug 30, 2014: Due date for workshop papers submission
  Sept 20, 2014: Notification of paper acceptance to authors
  Oct 5, 2014: Camera-ready of accepted papers


The First International Workshop on “High Performance Big Graph Data
Management, Analysis, and Mining” will be held in conjunction with IEEE
BigData'14 in Washington DC, USA in October 2014.

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.

Big graph problems are currently solved using several complementary
paradigms. The most popular approach is perhaps by exploiting parallelism,
through specialized algorithms for supercomputers, shared-memory multicore
and manycore systems, and heterogeneous CPU-GPU systems. However, since
real-world graphs are sparse and highly irregular, there are very few
parallel implementations that can actually deliver high performance. The
major challenges to scaling and efficiency include irregular data
dependencies, poor locality, and high synchronization costs of current
approaches. In addition to parallelism, researchers are developing
approximation algorithms that use sampling for compressing and summarizing
graph data. Streaming algorithms are also being considered for scenarios
where the rate of updates is too fast to process the entire graph in a
single pass. Further, out-of-core algorithms are necessary for massive
graphs that do not fit in the main memory of a typical system. Researchers
can use graph-based solutions for solving problems from many diverse
disciplines, including routing and transportation, social networks,
bioinformatics, computational science, health care, security and
intelligence analysis.

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

Papers should be at maximum 8 pages long, formatted using the style of
Big-Data 2014 conference proceedings. Paper in PDF format can be sent to
any of the program organizers by email by 11:59 pm PDT (Pacific Daylight
Time) August 30, 2014 on the paper submission deadline.

Workshop Organizers:
  Mohammad Al Hasan
  Department of Computer and Information Science
  Indiana University - Purdue University
  Indianapolis, IN 46202
  alhasan from cs.iupui.edu

  Kamesh Madduri
  Computer Science and Engineering Department
  Pennsylvania State University
  University Park, PA 16802
  madduri from cse.psu.edu

  Fengguang Song
  Department of Computer and Information Science
  Indiana University - Purdue University
  Indianapolis, IN 46202
  fgsong from cs.iupui.edu


Technical Program Committee:
  Medha Atre (University of Pennsylvania)
  Mohammad Al Hasan (Indiana University Purdue University)
  Kamesh Madduri (Pennsylvania State University)
  Xia Ning (NEC Laboratories)
  Saeed Salem (North Dakota State University)
  Fengguang Song (Indiana University Purdue University)
  Guangming Tan (Chinese Academy of Sciences, China)
  Chen Tian (Futurewei Technologies USA)
  Stanimire Tomov (University of Tennessee Knoxville)
  Jeff Vetter (Oak Ridge National Laboratory, Georgia Tech)
  Daniel Waddington (Samsung Research America)
  Mohammed J. Zaki (Rensselaer Polytechnic Institute)



Thanks,
Mohammad Hasan, Kamesh Madduri, Fengguang Song


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