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Call for Papers

Special Issue ofIEEE Transactions on Knowledge and Data EngineeringonMining Large Uncertain and Probabilistic Databases

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

Special Issue URL: http://www.cs.hku.hk/~ckcheng/tkde-si/cfp.htmlIntroduction

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 datagenerated by such applications. However, for most of these emerging domains, data is often riddled withuncertainty, arising, for instance, from inherent measurement inaccuracies, sampling and curation errors, andnetwork 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 inaccurateor 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 uncertaintymodels 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-analysisalgorithms for uncertain and probabilistic data to provide statistical guarantees over the results. In general, topicsof 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.

SubmissionProspective authors should prepare manuscripts according to the Information for Authors as published in recentissues of the journal or at http://www.computer.org/tkde/. Note that mandatory over-length page charges andcolor charges will apply. Manuscripts should be submitted through the online IEEE manuscript submission

system at https://mc.manuscriptcentral.com/tkde-cs/.

Timeline

Deadline for paper submission: April 1, 2009

Completion of first round review: June 14, 2009Revised manuscripts due: August 9, 2009

Final decision notification: November 1, 2009Publication date (tentative): May 2010

Guest Editors

Reynold ChengDepartment of Computer Science

The University of Hong KongEmail: ckcheng @ cs hku hk

Minos GarofalakisDepartment of Electronic & Computer Engineering

Technical University of CreteEmail: minos @ softnet tuc gr

Michael ChauSchool of Business

The University of Hong KongEmail: mchau @ business hku hk

Jeffrey Xu YuDept. of Systems Engineering & Engineering Management

The Chinese University of Hong KongEmail: yu @ se cuhk edu hk