IEEE International Workshop on Mining Evolving and Streaming Data

To be held in conjunction with

 

 

Introduction

Topics

Important Dates

Accepted Papers

Workshop Chairs

Program Committee

Paper Submission

Introduction

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 of Interest

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. 

 

Important Dates

Due date for papers submission: August 6, 2006
Notification of paper acceptance to authors: September 8, 2006
Camera-ready of accepted papers: September 29, 2006
Workshop day: December 18, 2006

Accepted Papers

[S8203] Hui Zhang and Han-tao Song, "Fuzzy Related Classification Approach Based on Semantic Measurement for Web Document"

[S8205] Clifton Phua, Ross Gayler, Kate Smith-Miles, and Vincent Lee, "Communal Detection of Implicit Personal Identity Streams"

[S8206] Yi Fang, Auroop Ganguly, Nagendra Singh, Veeraraghavan Vijayaraj, Neal Feierabend, and David Potere, "Online change detection: Monitoring land cover from remotely sensed data"

[S8207] Anita Dani and Janusz Getta, "Modelling Evaluation of Continuous Queries on Sliding Windows"

[S8208] Dimitris Tasoulis, Niall Adams, and David Hand, "Unsupervised Clustering In Streaming Data"

[S8209] Frank Klawonn and Plamen Angelov, "Evolving Extended Naive Bayes Classifiers"

[S8210] Brett Gillick, Shonali Krishnaswamy, and Arkady Zaslavsky, "Adaptive Resource-Aware Visualisation Framework for Ubiquitous Data Stream Mining"

[DM342] Shicong Meng, "Mining and Predicting Duplication over Peer-to-Peer Query Streams"

[DM501] Shuang Wu, Chunyu Yang, and Jie Zhou, "Clustering-training for Data Stream Mining"

[DM503] Willie Ng and Manoranjan Dash, "An Evaluation of Progressive Sampling for Imbalanced Data Sets"

[DM508] Manoranjan Dash and Willie Ng, "Efficient Reservoir Sampling for Transactional Data Streams"

[DM583] Barna Saha and Pabitra Mitra, "Fast Incremental Minimum-Cut Based Algorithm for Graph Clustering"

[DM678] Hua-Fu Li, Chin-Chuan Ho, Fang-Fei Kuo, and Suh-Yin Lee, "A New Algorithm for Maintaining Closed Frequent Itemsets in Data Streams by Incremental Updates"

[DM849] Hua-Fu Li, Chin-Chung Ho, and Suh-Yin Lee, "Incremental Mining of Sequential Patterns over a Stream Sliding Window"

[DM936] Chunyu Yang and Jie Zhou, "HClustream: A Novel Approach for Clustering Evolving Heterogeneous Data Stream"

Workshop Chairs 

Mohamed Medhat Gaber

Monash Univesisty, Australia

Haixun Wang

IBM T. J. Watson Research Center, US

Philip S. Yu

IBM T. J. Watson Research Center, US

Jian Pei

Simon Fraser University, Canada

Program Committee

Jesús S. Aguilar-Ruiz, University of Pablo de Olavide, Spain
Yijian Bai, UCLA, US

Lei Chen, Hong Kong University of Science and Technology, Hong Kong
Zhiyuan Chen, University of Maryland, Baltimore County, US
Yun Chi, NEC Research, US
Vladimir Estivill-Castro, Griffith University, Australia
Jianlin Feng, UIUC, US
Francisco Ferrer, University of Seville, Spain
Joăo Gama, University of Porto, Portugal 
Auroop Ganguly, ORNL, US

Weihong Han, National University of Defense Technology, China

Yan Jia, National University of Defense Technology, China
Mehmed M. Kantardzic, University of Louisville, US
Eamonn Keogh, University of California - Riverside, US

Ralf Klinkenberg, University of Dortmund, Germany
Shonali Krishnaswamy, Monash University, Australia
Seng Wai Loke, La Trobe University, Australia
Xiaofeng Meng, Renmin University, China
Kok-Leong Ong, Deakin University, Australia
Josep Roure, Carnegie Mellon University, US
Mohamed B. Senousy, Sadat Academy, Egypt
Simeon J Simoff, University of Technology, Sydney, Australia
Jianyong Wang, Tsinghua University, China

Wei Wang, Fudan University, China
Qian Weining, Fudan University, China

Yuqing Wu, Indiana University, US
Ying Yang, Monash University, Australia

Jeffrey Xu Yu, Chinese University of Hong Kong, Hong Kong
Arkady Zaslavsky, Monash University, Australia
Shuigeng Zhou, Fudan University, China

Paper Submission

The paper submission is now open. Please click here to submit your paper. The submission guidelines could be found at: http://www.comp.hkbu.edu.hk/iwi06/icdm/?index=download

     

Disclaimer