DM-WSN'06 is the First International Workshop Data Mining and Wireless
Sensor Networks (DM-WSN), which is organized in conjunction with
the IEEE International Conference on Data Mining, ICDM'06,
Hong Kong, December 18 - 22, 2006.
Due to recent advancement in electronics industry, Wireless Sensor Networks (WSNs) are
used for various applications such as security, agriculture and environmental
monitoring. WSN may contain hundreds of tiny, low-cost, battery-powered devices that
monitor physical attributes (humidity, temperature, and light) and that self-organize
into networks that can make autonomous decisions (turn on/off actuators), and are part
of a larger distributed management and control system (e.g. irrigation system). As
each node is a data source, a sensor network can possibly generate large sets of
data, which are ideal candidates for data mining techniques. However, sensor networks
are constrained in their ability to communicate their data to a centralized processing
server where data mining would normally take place. Sensors are limited in
terms of available energy for transmission, computational power, memory, and
communications bandwidth. Distributed data mining (DDM) methods provide
solutions to these constraints by placing aspects of the data mining process such
as data sampling, aggregation, and modeling on individual sensors, as well as
clusters of sensors. These activities and placement in the sensor network vary by
the type of data mining being undertaken such as classification, prediction, time
series analysis, clustering, and anomaly detection. The focus of this workshop is
to share the lessons learned from previous successful applications of DDM for WSNs
and to discuss new theories to distribute the data mining process over large
sensor networks. We are particularly interested in approaches that have solved
global network data mining problems through localized and distributed computation.
This workshop will bring together researchers and practitioners from
academia and industry. The workshop objectives are as follows:
- Collect and disseminate lessons learned from prior applications of DDM to
sensor networks - business, science, engineering, medicine, and other disciplines.
- Present and discuss new theoretical results, innovative ideas, and preliminary
studies on DDM that allow knowledge discovery to scale to sensor networks of
- Overcome the individual sensor constraints of available energy for transmission,
computational power, memory, and communications bandwidth, so as to more efficiently
undertake the data mining process on the sensor network.
- Power consumption characteristics of distributed data mining algorithms and developing data mining algorithms that minimize power consumption.
- DDM methods that overcome sensor limitations such as available energy for transmission, computational power, memory, and communications bandwidth.
- Efficient, scalable and distributed algorithms for large-scale DDM tasks such as classification, prediction, link analysis, time series analysis, clustering, and anomaly detection.
- DDM methods that distribute aspects of the data mining process such as data selection, sampling, cleaning, reduction, transformation, integration and aggregation, as well as model development, validation and deployment.
- Theory and application of:
- Distributed Principal Component Analysis (PCA) and Independent Component Analysis (ICA)
- Distributed machine learning (neural networks, support vector machines, decisions trees and rules, genetic algorithms)
- Distributed statistical regression methods
- Distributed Bayesian learning (belief networks, decision networks)
- Distributed clustering methods (distributed k-Means, dynamic neural networks)
- Agent based approaches to DDM.
- Incremental, exploratory and interactive mining
- Visual data mining
- Theoretical foundations in DM and WSNs; extensions of computational learning theory to sensor networks
- Mining of data streams
- Comparisons of in-network DDM versus traditional server side DM
- Privacy sensitive data mining
- Successful applications of DM for WSN in business, science, engineering, medicine, and other disciplines with particular attention to lessons learned.
Technical Program Committee (TPC) - (confirmed)
|Abidi, S.S. Raza||Dalhousie University, Canada|
|Bontempi, Gianluca||Universite Libre de Bruxelles, Belgium|
|Chen, Lei||Hong Kong Uni. of Science & Tech., Hong Kong|
|Chou, Cheng-fu||National Taiwan University, Taiwan|
|Graham, Peter||University of Manitoba, Canada|
|Hammad, Moustafa A.||University of Calgary, Canada|
|Jamil, Hasan M.||Wayne State University|
|Kemke, Christel||University of Manitoba, Canada|
|Leung, Carson K.||University of Manitoba, Canada|
|Pan, Jeffrey J.||Hong Kong Uni. of Sci. & Tech., Hong Kong|
|Peng, Wen-Chih||National Chiao Tung University, Taiwan|
|Sander, Joerg||University of Alberta, Canada|
|Talbi, El-Ghazali||LIFL, Université de Lille, France|
|Tan, Pang-Ning||Michigan State University|
|Yang, Laurence T.||St. Francis Xavier University, Canada|
|Yin, Jie||Hong Kong Uni. of Science & Tech., Hong Kong|
|Zaki, Mohammed||Rensselaer Polytechnic Institute|
|Submission deadline||July 30, 2006
|Authors Notification||September 08, 2006
|Final Manuscript||September 29, 2006
|Workshop Day||December 18, 2006
IEEE Computer Society format, (4+1 extra) pages, check the
on ICDM'06 website
for more details.
We would like to submit extended version of submitted papers for publication in a special issue of an