Call for Papers

Special Issue of IEEE Transactions on Knowledge and Data Engineering on

“Mining Large Uncertain and Probabilistic Databases”

Guest Editors: Reynold Cheng, Michael Chau, Minos Garofalakis, and Jeffrey Xu Yu

 
Homepage: http://i.cs.hku.hk/~ckcheng/tkde-si/cfp.html

pdf: http://www.computer.org/portal/cms_docs_transactions/transactions/tkde/CFP/cfp_tkde_mlupd.pdf

 

Introduction

Recent years have witnessed the emergence of novel database applications in various non-traditional domains, including location-based services, sensor networks, RFID systems, and biological and biometric databases. Traditionally, data mining has been widely used to reveal interesting patterns in the vast amounts of data generated by such applications. However, for most of these emerging domains, data is often riddled with uncertainty, arising, for instance, from inherent measurement inaccuracies, sampling and curation errors, and network latencies, or even from intentional “blurring” of the data (to preserve anonymity). Such forms of data uncertainty have to be handled carefully, or else the results of long and tedious data analyses could be inaccurate or even incorrect.
The goal of this special issue is to collect and distil the knowledge from experts in developing mining and data processing methods that are “uncertainty-aware”. We welcome papers that develop appropriate uncertainty models for data-mining tools and/or investigate efficient complex data-analysis techniques for large probabilistic and uncertain databases. We also seek paper submissions that extend classical mining and data-analysis algorithms for uncertain and probabilistic data to provide statistical guarantees over the results. In general, topics of interest for this special issue include (but are not limited to) the following areas:

  • Models and structures for uncertain/probabilistic information in data mining and complex data analysis;
  • Clustering spatially- and temporally-uncertain data;
  • Association rule mining and classification of uncertain data;
  • Machine learning aspects in uncertain data processing;
  • Incorporating data uncertainty models into traditional data-analysis algorithms;
  • Mining moving-object trajectories and biological data with noise;
  • Optimization of data-analysis queries and mining applications over uncertain/probabilistic databases;
  • Identification and similarity matching of objects with uncertainty; and
  • Efficient mining and analysis of uncertain/probabilistic data streams.

 

Submission

Prospective authors should prepare manuscripts according to the Information for Authors as published in recent issues of the journal or at http://www.computer.org/tkde/. Note that mandatory over-length page charges and color charges will apply. Manuscripts should be submitted through the online IEEE manuscript submission system at https://mc.manuscriptcentral.com/tkde-cs/.

 

Timeline

 

Paper submission due: April 1, 2009
Completion of first round reviews: June 14, 2009
Revised manuscripts due:  August 9, 2009
Final acceptance notification: November 1, 2009
Publication date (tentative): May 2010

                                       

Guest Editors

Reynold Cheng

Department of Computer Science

The University of Hong Kong

Email: ckcheng @ cs hku hk

 

Michael Chau

School of Business

The University of Hong Kong

Email: mchau @ business hku hk

 

Minos Garofalakis

Department of Electronic & Computer Engineering

Technical University of Crete

Email: minos @ softnet tuc gr

 

Jeffrey Xu Yu

Dept. of Systems Engineering & Engineering Management

The Chinese University of Hong Kong

Email: yu @ se cuhk edu hk