[Computational-biology] [BI2011] Extended Call for Papers (now due on 24/12/2010)

[TULIP SHARE]

BI2011: Extended Call for Papers

On authors=92 demand from several research groups, BI2011 announces an
extended deadline for submissions: 24 December, 2010 (Friday)

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The International Workshop on Behavior Informatics (BI2011)
URL: http://datamining.it.uts.edu.au/bi/bi2011/,
Submission System: http://www.easychair.org/conferences/?conf=3Dbi2011

Held in conjunction with
The 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining
(PAKDD2011)
URL: http://pakdd2011.pakdd.org/
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Important Dates
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Paper Submission Deadline (extended): 24 December, 2010 (Friday)
Author Notification: 21 January, 2011 (Friday)
Camera-Ready Deadline: 18 February, 2011 (Friday)

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Workshop Scope
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Deep and quantitative behavior analysis such as in social network cannot be
supported by traditional methodologies and techniques in behavioral
sciences. This leads to the emergence of inter-disciplinary Behavior
Representation, Modeling, Analysis and Management (namely Behavior
Informatics). The International Workshop on Behavior Informatics (BI2011)
provides an international forum for researchers and industry practitioners
to share their ideas, original research results, as well as potential
challenges and prospects encountered in Behavior Informatics.

The BI2011 workshop welcomes theoretical work and applied disseminations on
the categories which will include but are not limited to the following:

Behavior modeling: formalizing behaviors, relationships, impact and
networks.

Impact-oriented behavior mining: behaviors associated with high impacts are
of particular importance, while impact-oriented behaviors are often sparse,
rare and imbalanced isolated in business and data; identify impact-oriented
behavior patterns involves different pattern types and computational
challenges.

Analysis of behavior social networks handling challenging issues such as
convergence and divergence of behavior, and the evolution and emergence of
hidden groups and communities.
Extracting discriminative behavior patterns from high-dimensional,
high-frequency, high-density, and huge amount of data.

Large intra-class variance between behaviors: Due to the highly overlapped
nature of behavior data, it is extremely difficult to build a robust
behavior model which is tolerant for one behavior category while
differentiate amongst other categories.

Behavior data processing from transactional space to behavior feature space=
:
Customer demographic and transactional data is generally privacy-oriented,
distributed and not organized in terms of behavior but entity relationships=