VAKD 2010 - IEEE ICDM Workshop on Visual Analytics and Knowledge Discovery ? VAKD '10

Website datamining.it.uts.edu.au/icdm10/index.php/workshops | Edit Freely

Category VAKD 2010

Deadline: July 23, 2010 | Date: December 13, 2010

Venue/Country: Sydney, Australia

Updated: 2010-06-04 19:32:22 (GMT+9)

Call For Papers - CFP

held in conjunction with

ICDM 2010: The 10th IEEE International Conference on Data Mining

December 13-17, 2010, Sydney, Australia

Workshop Description

Visual Analytics is a relatively new multidisciplinary field that combines various research areas including knowledge discovery, data analysis, visualization, human-computer interaction, data management, geo-spatial and temporal data processing and statistics. The goal of Visual Analytics is to derive insight from massive, dynamic, ambiguous, and often conflicting data; detect the expected and discover the unexpected; provide timely, defensible, and understandable assessments; and communicate the assessment effectively for action. An integration of the increasing processing power of computers with the efficient pattern recognition abilities and domain knowledge of human analysts is a challenging and promising road in dealing with large amounts of complex data. It will be also a major driving force for solutions for information overload in many research and commercial areas.

The objective of this workshop is to bring together researchers and practitioners that are developing and applying the state-of-the-art in visual analytics; to provide a forum for presentation and discussion of the newest both mature and greenhouse ideas, research and developments in visual analytics and supporting disciplines, and to identify the short- and long-term research directions in the field and preferences of the potential end users.

We solicit papers that will introduce new research results, present forward-looking positional statements, or define relevant research challenges.

Topics of interest include, but are not limited to:

Visual analytics process models

Complexity, efficiency and scalability of visual analytics techniques

Incorporation of domain knowledge in visual analytics

Algorithmic animation methods for visual data mining

Cognitive aspects of information visualization in data mining

Multi-modal technologies for visual analytics

Interactivity in visual analytics

Visual languages in visual analytics

Visual representation of discovered knowledge

Efficient data processing algorithms for visual computing

Metrics and evaluation methods for visual analytics

Generic visualisation architectures

Methods for visualising semantic content

Visual analytics of integrated data sets, including text, graph and digital media data

Collaborative visual analytics, including high-end virtual environments

Visual data abstraction

Visual analysis of large graphs and networks

Visual exploration of data warehouses

Integrated visualisation of raw data and analysis results

Perceptual and cognitive factors visual analytics

Interaction paradigms and human factors in visual analytics

Important Dates

23 July 2010 at 23:59 UTC-11 Paper/challenge submissions

20 September 2010 Notifications of acceptance

11 October 2010 Camera ready papers

13 December 2010 Workshop in Sydney, Australia

Invited Talks

The invited talks provide an overview of Visual Analytics, define its scope and challenges, and present reference Visual Analytics techniques and systems.

Visual Analytics Challenge

You are invited to work the VAST 2008-2010 challenges, and use those datasets, to illustrate your KDD/VA research. A distinct advantage to you in using these datasets is that we will be able to compare and contrast approaches taken by the Visual Analytics community with yours and examine the possibilities for synergies between the two communities. We will provide additional guidance into the adjusted tasks to make the challenge interesting to the KDD community.

We will present examples of the VAST 2008, 2009, and 2010 challenge solutions at the workshop, as a springboard to follow-on discussion.

KDD-09 specific instructions for submissions

VAST 2008 challenge information

Datasets

IEEE VAST 2009 Challenge

IEEE VAST 2010 Challenge

Paper Submission

Submissions have to be 10 pages or less in IEEE 2-column format submitted electronically via Easy Chair.

We strongly encourage (but do not strictly require) all contributors to use at least some of the challenge tasks described below to demonstrate the methods and concepts proposed in the contributions. This will support the discussion by making the position papers more concrete by providing a common problem for all, as well as serve as uniform benchmark data set for the workshop submissions.

In addition to original contributions we will consider papers based on recently published outstanding works, given that the original papers are adequately cited and the status is clearly stated in the contribution.

Proceedings

All submitted papers will be reviewed for quality and originality by the Program Committee. Based on this review, the papers will be accepted for oral and/or poster presentations, or rejected. The review process will not be double-blind (i.e., the reviewers can see your identity, you do not have to anonymize your paper).

Papers will be selected by the program committee through a peer-review process and they will be presented in oral and/or poster sessions in the workshop. Selected papers will be invited to be published in a special journal issue or proceedings after the workshop, along with the conclusions of the workshop.

Organizers

Simeon Simoff

Professor of Information Technology, Head of School

School of Computing and Mathematics,

University of Western Sydney, NSW 1797

Australia

s.simoff [at] uws.edu.au

Pak Chung Wong

Chief scientist and project manager

Pacific Northwest National Laboratory PNNL

P.O. Box 999, J4-32

Richland, WA 99352

USA

pak.wong [at] pnl.gov

Mike Sips

Research Scientist

GFZ German Research Centre for Geosciences

Section 1.3, Earth System Modelling

Telegrafenberg, A20 303

14473 Potsdam

Germany

sips [at] gfz-potsdam.de

Arturas Mazeika

Research Scientist

Max-Planck-Institut Informatik

Department 5: Databases and Information Systems

Campus E 1 4

66123 Saarbruecken

Germany

amazeika [at] mpi-inf.mpg.de

Program Committee (not complete)

Gennady Andrienko, Fraunhofer Institute IAIS, Germany

Alessio Bertone, Donau-Universitaet Krems, Austria

Michael Boehlen, University of Zuerich, Switzerland

Urska Cvek, LSU Shreveport, USA

William S. Cleveland, Purdue Univerity, USA

Joachim Giesen, Friedrich-Schiller-Universitaet Jena, Germany

Maolin Huang, University of Technology, Australia

Otto Huisman, ITC, University of Twente, The Netherlands

Jimmy Johansson, Linkoeping University, Sweden

Anne Kao, The Boeing Company, USA

Paul Kennedy, University of Technology, Australia

Quang Vinh Nguyen, University of Western Sydney, Australia

Thomas Nocke, Potsdam Institute for Climate Impact Research, Germany

Panagiotis Papapetrou, Aalto University, Finland

Kai Puolamaki, Aalto University, Finland

Anthony Robinson, Penn State, USA

Joern Schneidewind, Telefonica-o2, Germany

Tobias Schreck, Technische Universitaet Darmstadt, Germany

Sponsors

VisMaster, a European FP7 Coordination Action Project focused on Visual Analytics

National Visualization and Analytics Center (NVAC)

Department of Homeland Security (DHS)

European Archive