[Ieee_vis] The 3rd IEEE Workshop on Interactive Visual Text Analytics at Vis 2013: Call for Participation

Michelle X Zhou mzhou at us.ibm.com
Fri Jul 26 21:42:44 CEST 2013




The 3rd IEEE Workshop on Interactive Visual Text Analytics:
Integrated Analysis of Heterogeneous Data and Ubiquitous Text Analytics
www.textvis.org

IEEE VIS 2013
Atlanta, Georgia
October 2013


Workshop Goals

Much  research  has  been  reported on visual text analytics for plain text
documents  viewed  in  traditional analytic settings.  In this workshop, we
would   like   to  push  the  boundary  of  visual  text  analytics  toward
heterogeneous  textual  data  (text  associated  with other data types) and
ubiquitous  text  analytics.    First, we would like to use the workshop to
collect  various  use  cases  about  heterogeneous data and ubiquitous text
analytics.   From   the  use  cases,  we  hope  to  better  understand  the
requirements  of  heterogeneous  textual  data  analysis from a task-driven
perspective.    Although   there   is  some  work  on  visual  analysis  of
heterogeneous  textual  data,  there  is  not  a clear understanding of the
typical  tasks that people would like to achieve in analyzing heterogeneous
textual  data.  Moreover, how will different tasks influence the design and
development  of both text analytics and visualization technologies? We thus
would  like to leverage the power of the crowd at the workshop to examine a
number  of  use  cases  and  draft a taxonomy that characterizes the design
dimensions of the space and can also be used to guide the future design and
development.  Second,  based  on  the  use cases, we would like to use this
workshop  to  examine  how to best leverage state-of-the-art text analytics
and   traditional   data   mining  techniques  in  conjunction  with  novel
interactive  visual  analytics  to address the challenges manifested by the
collected use cases.



Paper Submission

We  invite  2-4 page position paper submissions that address topics related
to  interactive  visual  analysis  of  heterogeneous textual data or visual
analysis of text data on mobile devices or other ubiquitous scenarios, with
a requirement that every submission must clearly state one or more concrete
use  cases,  including  the  tasks to be achieved, the two or more types of
data  to  be  analyzed, and data analytic methods used.  Topics of interest
include but are not limited to:

 · 		 Task  taxonomy of analysis of heterogeneous textual data or
ubiquitous
    text analytics
 · 		 Visual  metaphors  for  heterogeneous  textual data or
ubiquitous text
    analytics
 ·		 Coordinated visualizations of textual and non-textual data
 · 		 Perception  and  cognition  in  heterogeneous  data
visualization  or
    ubiquitous text analytics
 ·		 Mobile visual text analytics and mobile visual interaction
 · 		 Systems,  languages,  and architectures for heterogeneous
textual data
    analysis
 ·		 Collaborative analysis of heterogeneous textual data
 ·		 Real-time visualization of streaming heterogeneous textual
data
 ·		 Opinion summarization from heterogeneous textual data
 · 		 Visual  event identification and prediction from heterogeneous
textual
    data
 · 		 Uncertainty  in  interactive  heterogeneous  textual  data
analysis or
    ubiquitous text analytics
 · 		 Industry-specific  applications  of  visual  analysis of
heterogeneous
    textual data (e.g. Retail, Healthcare, Government, etc.)
 · 		 Studies  and  evaluation  of  heterogeneous textual data
visualization
    techniques, systems, metrics, and bench-marks
 · 		 Datasets  and  tasks for visual text analysis of heterogeneous
textual
    data


Important dates

 August 31st: Position paper due
 September 14th: Notification
 October 1st: Final version of papers due
 October 14th: Visual text analytics workshop

Please submit your position papers by email to: textvis at us.ibm.com



Organizers

Chris Collins, University of Ontario Institute of Technology
Eser Kandogan, IBM Research, Almaden
Shixia Liu, Microsoft Research Asia
Michelle Zhou, IBM Research, Almaden
Chad Steed, Oakridge National Lab

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