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Special Session 2:

Special Session on Big Data Representation and Processing in Data Science

Goal: The ultimate long-term goal of this special session is to uncover the underlying computing paradigms, models, or theories that achieve high “velocity” processing of large “volume” and “variety” of data without the compromise of “veracity” and “value”.

Big data are everywhere! This special session focuses on learning from the practices of real world engineering applications, in particular for multimodal data such as biomedical, video, and some traditional textual data coupled with metadata. Multimodal data are data of multiple modes or “variety,” that is, they contain heterogeneous signals and/or come from heterogeneous sources. Participants should propose solutions and approaches to the analysis and/or design of data with heterogeneity in the context of big data. We invite papers dealing interdisciplinary problems on big data analytics and design for multimodal data. Specifically, we are soliciting the following:

• Solutions and applications for heterogeneous data at scale, e.g. as seen in biology/chemistry (DNA sequencing, microscopy, mass spectrometry data, etc.), physics (astrological, geological data, etc.), machine vision (videos, 3D data, etc.), etc.
• For more traditional research on single-mode data, such as text and images, there should some combination with other sources of information/knowledge, e.g. metadata.

Related Topics for Papers May Include the Following:

• Knowledge management and mining
• Big data analytics: data visualization, statistical and exploratory analytics
• Quantitative and qualitative uncertainty
• Approximation: approximate retrieval, reasoning and proof
• Parallel computing theory in Big Data, e.g., MapReduce applications for Data Science
• Granular and rough computing for heterogeneous data

Important Dates

Deadline for paper submission: Aug. 25, 2014
Notification of paper acceptance: Sept. 1, 2014


Asmi Shah, PhD (MGH Visiting Scientist, Harvard Medical School) asmi.capri@gmail.com
Brendan Jou (Electrical Engineering, Columbia University) bjou@ee.columbia.edu

Program Committee

Wesley Chu, PhD (Computer Science, UCLA) wwc@cs.ucla.edu
Liangliang Cao, PhD (Research Staff Member, IBM T. J. Watson Research) liangliang.cao@us.ibm.com
Tomoyuki Higuchi, Ph.D (The Institute of Statistical Mathematics) higuchi@ism.ac.jp
Howard Ho, PhD (Manager, IBM Almaden Research Center) ctho@us.ibm.com
Urban Liebel, PhD (CTO, Acquifer AG, Karlsruhe Institute of Technology) u.liebel@acquifer.de
Shusaku Tsumoto, MD, PhD (Medicine, Shimane University) tsumoto@computer.org
S. Felix Wu, PhD (Computer Science, UC Davis) wu@cs.ucdavis.edu
Ying Xie, PhD (Computer Science, Kennesaw State University) yxie2@kennesaw.edu
Justin Zhan, PhD (Computer Science, North Carolina A&T State University) zzchan@ncat.edu

Session Chair

T. Y. Lin, PhD (Computer Science, San Jose State University) tylin@cs.sjsu.edu


IEEE BigData 2014 Web Site http://cci.drexel.edu/bigdata/bigdata2014



Last update: 20 June 2014