[Comp-neuro] CFP: Knowledge Discovery from Sensor Data (SensorKDD-2010) - KDD 2010 Workshop

Pedro Pereira Rodrigues pprodrigues at liaad.up.pt
Wed Mar 24 23:42:35 CET 2010

** Apologies for cross-posting **


4th International Workshop on
Knowledge Discovery from Sensor Data

at the
16th ACM SIGKDD Conference on
Knowledge Discovery and Data Mining (KDD)

July 25, 2010, Washington, DC, USA

URL: http://www.ornl.gov/sci/knowledgediscovery/SensorKDD-2010/

The International Workshop on Knowledge Discovery from Sensor Data
(SensorKDD-Workshop) has established itself as the premier workshop in
the general areas of knowledge discovery from sensor data and data
streams. The 4th SensorKDD-Workshop (SensorKDD-2010) provides a forum
for presentation of original research and application results, as well
as exchange and dissemination of computational challenges and experience
in handling and mining massive volumes of disparate, dynamic, and
geographically distributed data.

The main motivation for this workshop stems from the increasing need for
a forum to exchange ideas and recent research results, and to facilitate
collaboration and dialog between academia, government, and industrial
stakeholders. This is clearly reflected in the successful organization
of the previous three SensorKDD workshops.

The workshop covers all aspects of knowledge discovery from sensor data,
including algorithms, applications, case studies, and software and
systems. Besides the technical program, SensorKDD-2010 will feature
SensorKDD Workshop contest.

We solicit high quality papers in the general areas of knowledge
discovery from sensor data and data streams. Topics of interest include
(but are not limited to):

* Predictive analysis from geographically distributed and heterogeneous data
* Computationally efficient approaches for mining unusual patterns,
specifically, anomalies, outliers, extremes, nonlinear processes, and
change, from massive and disparate space-time data
* Real-time analysis of dynamic and distributed data, including
streaming and event-based data
* Mining from continuous streams of time-changing data and mining from
ubiquitous data
* Efficient algorithms to detect deviations from the normal in real-time
* Resource-aware algorithms for distributed mining
* Monitoring and surveillance based on a single or multiple sensor feeds
* Coordinated offline discovery and online analysis with feedback loops
* Combination of knowledge discovery and decision scientific processes
* Facilitation of faster and reliable tactical and strategic decisions
* Distributed data stream models
* Theoretical frameworks for distributed stream mining
* Success stories, especially about end-to-end solutions, for national
or global priorities
* Real-world problem design and knowledge discovery requirements

Workshop Paper Submission:

All papers will be peer reviewed. If accepted, at least one of the
authors must attend the workshop to present the work in order for the
paper to be included in the IEEE Digital Library. Selected accepted
papers will be recommended for submission to special issues of journals.

The submitted papers should be in English and must not exceed 9 pages
using the standard ACM format found at
http://www.acm.org/sigs/publications/proceedings-templates in PDF format
and submitted as an email attachment to SensorKDD at gmail.com.

Important Dates:
    Paper Submission: 			May 4, 2010
    Notification of Acceptance: 	June 4, 2010
    Camera Ready Paper Due: 		June 30, 2010

Workshop Organizers:

Dr. Olufemi A. Omitaomu (omitaomuoa at ornl.gov)
Oak Ridge National Laboratory, TN, USA.

Dr. Auroop R. Ganguly (gangulyar at ornl.gov)
Oak Ridge National Laboratory, TN, USA.

Prof. Joao Gama (jgama at fep.up.pt)
University of Porto, Portugal.

Dr. Ranga Raju Vatsavai (Vatsavairr at ornl.gov)
Oak Ridge National Laboratory, TN, USA.

Prof. Mohamed Medhat Gaber (Mohamed.Gaber at port.ac.uk)
University of Portsmouth, UK.

Prof. Nitesh V. Chawla (nchawla at cse.nd.edu)
University of Notre Dame, IN, USA.

Program Committee Members:

The complete list of program committee members can be found at

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