Connectionists: KDD 2014 Call for Papers

[Polo Chau]

* * * KDD 2014 Call for Papers * * *
August 24-27, New York City, USA
http://www.kdd.org/kdd2014/

Submissions are solicited for the 20th ACM SIGKDD Conference on
Knowledge Discovery and Data Mining (KDD), an interdisciplinary
conference that brings together researchers and practitioners from all
aspects of data mining, knowledge discovery, and large-scale data
analytics. The conference is a highly selective meeting that includes
oral and poster presentations of refereed papers as well as panel
discussions and invited talks by the leading academic and industrial
experts. This year, we have a special theme, Data Mining for Social
Good, which will highlight how the work of data analytics researchers
and practitioners in contributing towards social good as well as how
these high impact, social problems provide a rich set of challenges for
KDD researchers to work on. KDD 2014 will be held August 24-27 2014 in
New York, USA. The conference will start with workshops and tutorials
on August 24, followed by the main conference (August 25-27).


----- Key dates -----
 * Abstract submission    : Thursday, February 13, 2014, 11:59pm PST
 * Full paper submission  : Friday, February 21, 2014, 11:59pm PST
 * Acceptance notification: Monday, May 12, 2014


----- Submission website -----
https://cmt.research.microsoft.com/KDD2014/


----- Reviewing -----
As per KDD tradition, reviews are not double-blind, and
author names and affiliations should be listed. Authors should consult
the conference website at http://www.kdd.org/kdd2014/ for full
details regarding paper preparation and submission guidelines.


----- Evaluation Criteria -----
Submitted papers will be assessed based on their novelty, technical
quality, potential impact, and clarity. For papers that rely heavily on
empirical evaluations, the experimental methods and results should be
clear, well executed, and repeatable. Authors are strongly encouraged
to make data and code publicly available whenever possible.


----- Dual Submission Policy -----
Papers submitted to KDD should be original work and substantively
different from papers that have been previously published or are under
review in a journal or another conference/workshop. Accepted papers
will be published in the conference proceedings by ACM and also appear
in the ACM Digital Library.


----- Submission Instructions -----
All submissions will be made electronically, in PDF format. Papers are
limited to 10 pages, including references, diagrams, and appendices.
The format is the standard double-column ACM Tighter Alternate Style
(http://www.acm.org/sigs/publications/proceedings-templates). Please
refer to the complete submission and formatting instructions on the
conference website for further details (http://www.kdd.org/kdd2014/).


----- Open Access -----
Accepted KDD papers will be made freely available via the ACM Digital
Library platform two weeks before the conference. The free access will
end on the first day of the next KDD conference. This free availability
period will not only facilitate easy access to the proceedings by
conference attendees, but also enable the community at large to
experience the excitement of learning about the latest developments
being presented at the KDD conference.


----- Tracks -----
KDD is a dual track conference hosting both a research track and an
industry & government track. These two tracks are quite distinct in
scope and evaluation criteria. Papers can therefore only be submitted
to one track and will only be reviewed in the track the paper is
submitted to. Authors are encouraged to carefully read the following
and choose an appropriate track for their submissions.


----- Research Track -----
We invite submission of papers describing innovative research on all
aspects of knowledge discovery and data mining. Papers emphasizing
theoretical foundations are particularly encouraged, as are novel
modeling and algorithmic approaches to specific data mining problems in
scientific, business, medical, and engineering applications. Visionary
papers on new and emerging topics are also welcome. Authors are
explicitly discouraged from submitting papers that contain only
incremental results and that do not provide significant advances over
existing approaches. Application oriented papers that make innovative
technical contributions to research are also welcome.

Papers are solicited in all areas of data mining, knowledge discovery,
and large-scale data analytics, including, but not limited to:

 * Algorithms: 
   Graph and link mining, rule and pattern mining, web mining,
   dimensionality reduction and manifold learning, combinatorial
   optimization, relational and structured learning, matrix and tensor
   methods, classification and regression methods, semi-supervised
   learning, and unsupervised learning and clustering.

 * Applications: 
   innovative applications that use data mining, including systems for
   social network analysis, recommender systems, mining sequences,
   time series analysis, online advertising, bioinformatics, systems
   biology, text/web analysis, mining temporal and spatial data, and
   multimedia processing.

 * Big Data: 
   Efficient and distributed data mining platforms and algorithms,
   systems for large-scale data analytics of textual and graph data,
   large-scale machine learning systems, distributed computing (cloud,
   map-reduce, MPI), large-scale optimization, and novel statistical
   techniques for big data.

 * Data mining for social good: 
   Novel algorithms and applications of data mining to societal
   problems is especially encouraged. (For deployment of existing
   algorithms consider the Industry/Govt. track.) Topics include:
   public policy, sustainability, climate change, medicine and health,
   education, transportation, biodiversity and energy.

 * Foundations of data mining: 
   Data mining methodology, data mining model selection,
   visualization, asymptotic analysis, information theory, and
   security and privacy.


----- Industry and Government Track -----
We invite submissions describing implementations of data
mining/analytics/big data/data science systems in industry, government,
or non-profit settings. Our primary emphasis is on papers that advance
the understanding of, and show how to deal with, practical issues
related to deploying analytics technologies. This track also highlights
new research challenges motivated by analytics and data mining
applications in the real world. These applications can be in any field
including, but not limited to e-commerce, medicine, healthcare,
defense, public policy, engineering, law, manufacturing,
telecommunications, and government. This year, we are highlighting a
special theme at KDD, highlighting data science efforts for social
good. We highly encourage submissions that are focused on that theme,
and describe data science work being done in areas such as education,
sustainability, healthcare, community development, and public safety.

Submitted papers will go through a competitive peer review process. The
Industry & Government track is distinct from the Research Track in that
submissions solve real-world problems and focus on systems that are
deployed or are in the process of being deployed.  Submissions must
clearly identify one of the following three areas they fall into:
"deployed", "discovery", or "emerging".

The criteria for submissions in each category is as follows:

* Deployed: 
  Must describe deployment of a system that solves a non-trivial
  real-world problem. The focus should be on describing the problem,
  its significance, decisions and tradeoffs made when making design
  choices for the solution, deployment challenges and lessons learned.

* Discovery: 
  Must include results that are discoveries with demonstrable value to
  an industry or government organization. This discovered knowledge
  must be "externally validated" as interesting and useful; it can not
  simply be a model that has better performance on some traditional
  evaluation metrics such as accuracy or area under the curve. A new
  scientific discovery enabled by the use of data mining techniques is
  an example of what this category will include.

* Emerging: 
  Submissions do not have to be deployed but must have clear
  applications to Industry/Government to distinguish them from KDD
  research papers. They may also provide insight into issues and
  factors that affect the successful use and deployment of Data Mining
  and Analytics. Papers that describe enabling infrastructure for
  large-scale deployment of Data Mining and analytics techniques also
  fall in this category.


On behalf of the KDD 2014 organizers,

Jure Leskovec (Stanford University)
Wei Wang (UCLA)
 Research Program Co-Chairs
 research-chairs at kdd2014.org

Rayid Ghani (Univerisity of Chicago, Edgeflip)
Prem Melville (Social Alpha)
Brian Dalessandro (Dstillery)
Paul Bradley (MethodCare)
 Industry and Government Program Co-Chairs
 industry-chairs at kdd2014.org

-----
KDD 2014 Publicity Chair:
Polo Chau
Assistant Professor
College of Computing
Georgia Tech

polo at gatech.edu
http://www.cc.gatech.edu/~dchau/