First
International Workshop on
Knowledge Discovery in Data Streams
20 September 2004, Pisa, Italy
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
- Temporal, spatial, and spatio-temporal data mining
- Scalable Algorithms
- Real-Time Applications
- Real-World Applications
Important Dates
Submission deadline: June 21, 2004 (extended)
Notification of acceptance: July 15, 2004 (extended)
Camera-ready copies due: July 26, 2004
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