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- Date: Thu, 21 Jun 2007 21:44:44 +0100
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*** Apologies for cross-posting *** Knowledge Discovery from Ubiquitous Data Streams ================================= Workshop in conjunction with ECML-PKDD 2007 17 September 2007 - Warsaw, Poland Web Page: http://www.niaad.liacc.up.pt/~iwkduds/ Important Dates =========== * Paper submission deadline: June 30th, 2007 * Notification of acceptance/rejection: July 21st, 2007 * Camera-ready deadline: July 28th, 2007 Goals ==== 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. Motivation ========== 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. Topics ==== 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: jgama@fep.up.pt Mohamed.Gaber@csiro.au
Received on Friday, 22 June 2007 14:49:39 UTC