The First 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:
Sept 6, 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 submitted online through the official submission website (https://wi-lab.com/cyberchair/2014/bigdata14/scripts/submit.php?subarea=S3) by 11:59 pm PDT (Pacific Daylight Time) on September 6, 2014.

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
Computer Science and Engineering Department
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


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)