IEEE International Workshop on Mining Evolving and Streaming Data
To be held in conjunction with
Recently, applications that require analysis of streaming and evolving data become of special significance to be developed and addressed by researchers and practitioners.
Advances in both hardware and software technologies coupled with high-speed data generation have led to the area of data streams. Streaming data is ubiquitous and there is a real need to store, query and analyze such rapid large volumes of data. Examples of data streams include (but are not limited to): data generated from wireless sensor networks, web logs and clickstreams, ATM transactions, search engines and phone call records. Traditional data mining techniques are infeasible for analyzing this sort of data. Owing to the importance of applications of this area, mining data streams has attracted great attention over the last few years.
Many applications deal with data of changing characteristics. For instance, managing objects that move in space has applications in traffic control, law enforcement, homeland security, urban planning, etc. As another example, one distinguishing trait setting data streams apart from disk-stored data is that streaming data usually exhibits time-changing data characteristics. As most decision making tasks rely on the up-to-dateness of their supporting data, the evolving nature of the data creates tremendous complexity for many mining algorithms. On the other hand, users are often interested in changes embodied by the data. Thus, how to make mining algorithms more effective and efficient in view of changing data characteristics has become a major challenge in a wide range of application domains. These include applications in network monitoring, biosurveillance, Web data mining, clustering and classification of data of changing distributions, etc.
This workshop aims at gathering data mining researchers to demonstrate their recent research results in the area. Papers that address mining evolving and streaming data techniques, systems and applications are welcome. We also encourage position and on-going research papers.
Topics include (but are not limited to):
· Clustering, classification and frequent patterns from data streams
· Building accurate models for evolving data
· Techniques of detecting changes in evolving data
· Quantification of changes in evolving data
· Applications of detecting changes of evolving data
· Clustering and classification of data of changing distributions.
· Visualization of data streams and stream mining results.
· Analysis of data streams in sensor networks.
· Real-world applications of data stream mining.
· Data stream mining systems.
· Resource-constrained data stream mining techniques.
· Theoretical frameworks for stream mining.
· Interactive stream mining techniques and systems.
· Onboard data analysis.
· Adaptive stream mining techniques.
Due date for papers submission: August 6, 2006
[S8203] Hui Zhang and Han-tao Song, "Fuzzy Related Classification
Approach Based on Semantic Measurement for Web Document"
Jesús S. Aguilar-Ruiz, University of Pablo de Olavide, Spain
Lei Chen, Hong Kong University of Science and Technology, Hong Kong
Weihong Han, National University of Defense Technology, China
Yan Jia, National University of Defense Technology, China
Ralf Klinkenberg, University of Dortmund, Germany
Wei Wang, Fudan University, China
Yuqing Wu, Indiana University, US
Jeffrey Xu Yu, Chinese University of Hong Kong, Hong Kong
The paper submission is now open. Please click
here to submit your paper. The submission guidelines could be found