- From: Joćo Gama <jgama@liacc.up.pt>
- Date: Mon, 19 Apr 2004 16:08:57 +0100
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CALL FOR PAPERS First International Workshop on Knowledge Discovery in Data Streams 24 September 2004, Pisa, Italy http://www.lsi.us.es/~aguilar/ecml2004/ in conjunction with ECML/PKDD 2004: The 15th European Conference on Machine Learning (ECML) and The 8th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD), http://ecmlpkdd.isti.cnr.it/ MOTIVATION Databases are growing incessantly and many sources produce data continuously. In many cases, we need to extract some sort of knowledge from this continuous stream of data. Examples include customer click streams, telephone records, large sets of web pages, multimedia data, scientific data, and sets of retail chain transactions. These sources are called data streams. The goal of this workshop is to convene researchers who deal with decision rules, decision trees, association rules, clustering, filtering, preprocessing, post processing, feature selection, visualization techniques, etc. from data streams and related themes. We are looking for all possible contributions related to inductive learning from data streams. The rapid growth in information science and technology in general and the complexity and volume of data in particular have introduced new challenges for the research community. Databases are growing incessantly and many sources produce data continuously. In most of real world applications, the process generating the data is not strictly stationary. In many cases, we need to extract some sort of knowledge from this continuous stream of data. Examples include customer click streams, telephone records, large sets of web pages, multimedia data, scientific data, and sets of retail chain transactions. These sources are called data streams. Learning from data streams are incremental tasks that requires incremental algorithms that take drift into account. The goal of this workshop is to convene researchers who deal with decision rules, decision trees, association rules, clustering, filtering, preprocessing, post processing, feature selection, visualization techniques, etc. from data streams and related themes. Research works presenting theoretical results, basic research, perspective solutions and practical developments are welcome, provided that they address the topic of the workshop. Position papers are also welcome and encouraged. Topics of Interest Topics include (but are not restricted to): * Data Stream Models * Clustering from Data Streams * Decision Trees from Data Streams * Association Rules from Data Streams * Decision Rules from Data Streams * Feature Selection from Data Streams * Visualization Techniques for Data Streams * Incremental on-line Learning Algorithms * Mining spatio-temporal data streams * Scalable Algorithms * Real-Time Applications * Real-World Applications Important Dates Submission deadline: June 14, 2004 Notification of acceptance: July 5, 2004 Camera-ready copies due: July 12, 2004 Chairs * Joao Gama, University of Porto, Portugal * Jesus S. Aguilar-Ruiz, University of Seville, Spain Program Commitee * Usama Fayyad, DMX Group, US * Philip S. Yu, IBM Watson Research Center, US * Minos Garofalakis, Bell Labs, US * Hillol Kargupta, University of Maryland, Baltimore County, US * Jiong Yang, University of Illinois at Urbana Champaign, US * Wei Wang, University of North Carolina, Chapel Hill, US * S. Muthukrishnan, Rutgers University and AT&T Research, US * Jeffrey S. Vitter, Purdue University, US * Venkatesh Ganti, Microsoft Research, US * Nick Koudas, AT&T Research, US * Pedro Domingos, University of Washington, US. * Geoff Hulten, Microsoft Research, US * Luis Torgo, LIACC, Univ. of Porto * Divesh Srivastava, AT&T Research, US * Nina Mishra, Stanford University, US * Sudipto Guha, University of Pennsylvania, US * Rosa Meo, University of Torino, Italy * Josep Roure i Alcobe, Polytechnic of Catalunya, Esp * Joao Gama, University of Porto, Portugal * Jesus S. Aguilar-Ruiz, University of Seville, Spain
Received on Monday, 19 April 2004 11:29:05 UTC