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AISB event Bulletin Item

CFP: Knowledge Discovery Ubiquitous Data Streams - Workshop in ECML/PKDD 2007

Knowledge Discovery from Ubiquitous Data Streams
Workshop in conjunction with ECML-PKDD 2007

17 September 2007 - Warsaw, Poland

Web Page:

Important Dates
* Paper submission deadline: June 30th, 2007
* Notification of acceptance/rejection: July 21st, 2007
* Camera-ready deadline: July 28th, 2007

The goal of this workshop is to promote an interdisciplinary forum for
researchers who deal with sequential learning, anytime learning,
real-time learning, online learning, etc. from ubiquitous and
distributed data streams. Distributed Learning from Data Streams is a
recent and increasing research area with challenging applications and
contributions from fields like Data Bases, Data Mining, Machine
Learning, and Visualization.  

Advances in miniaturization and sensor technology lead to
sensor networks, collecting detailed spatio-temporal data about the
environment. How to learn from these distributed continuous streaming
data? Which are the main characteristics of a learning algorithm
acting in sensor networks? What are the relevant issues, challenges,
and research opportunities?  Which emerging applications?  The goal of
this workshop is to convene researchers (from both academia and
industry) who deal with decision rules, decision trees, association
rules, clustering, filtering, preprocessing, post processing, feature
selection, visualization techniques, etc. from distributed data
streams and related themes. Special emphasis in constrained algorithms
designed to handle limited bandwidth, limited computing and storage
capabilities, limited battery power, and specific
network-communication protocols.

A data stream is an ordered sequence of instances that can be read only once 
or a small number of times using limited computing and storage capabilities. 
Topics include but are not restricted to:

* Distributed Data Stream Models
* Learning in Ubiquitous environments
* Learning from Sensor Networks
* Learning from Social Networks
* Clustering from Distributed Data Streams
* Decision Trees from Distributed Data Streams
* Association Rules from Data Streams
* Visualisation Techniques for Distributed Data Streams
* Incremental on-line Learning Algorithms
* Single-Pass and Scalable Algorithms
* Real-Time and Real-World Applications using Stream data
* Adaptive mining techniques in data streams
* Resource-aware distributed data stream mining
* Theoretical frameworks for distributed data stream mining

Submitting Information
* Papers should be in PDF format
* Papers should be at most 10 pages long
* All papers should be formatted in the LNCS style of the Springer
* Papers should be submitted electronically by email to the program chairs: