Call for Contributions
Information Sciences
Special Issue on Processing and Mining Complex Stream Data

The paper submission deadline prolonged to March 18, 2013
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Data mining and machine learning have shown tremendous methodological development and have been applied to various real-world problems. Nevertheless, many of current approaches assume processing static and simple (usually tabular) forms of data. On the other hand, modern applications and the rapid grows of information technologies give access to massive, complex and dynamic data.

Massive data are generated every day in many fields. These are often machine-generared data produced, e.g., by sensor or monitoring systems. Enormous amount of data overhelm current computer systems with respect to storing, processing and analysing them under acceptable space and time constraints. Moreover these data are no longer of a standard form but they could be represented in more complex structures offering richer descriptions of real-world objects. Both massive and complex data characteristics require new scalable algorithmic solutions allowing for data summarizing, sampling and approximating. New architectures for efficient managing such data and quering them are also necessary as well.

This need is particularly relevant in the emerging data stream mining domain, where large volumes of data records are generated continuously. The amounts of data arriving at a high rate, often with dynamically changing characteristics, require real-time or near-real-time analysis and introduce constraints over the available amount of memory. Another important aspect of mining data streams refers to changes in the data distributions and target concepts over time. Detecting theses changes and adapting classifiers to concept drifts becomes one of the challenges for new scalable algorithms.
The aim of this special issue is to discuss the current state of research and latest results concerning mining large, complex and evolving stream data. We solicit original and unpublished contributions in all topics covering these data mining tasks. Papers should present new results in the following (non-exhaustive) list of topics:
* Scalability in processing massive data volumes
* Handling machine-generated data streams
* Approximate processing and approximate queries
* Near-real-time analytics of massive and stream data
* Discovering complex patterns in data, including multi-labeled classification and structured, complex decisions
* Classification, clustering and frequent patterns from data streams
* Detecting and adapting to changes and concept drifts in evolving data streams
* Ensemble learning in changing environments
* Efficient algorithms for mining data streams in ubiquitous environments
* Handling uncertainty in mining stream data
* Cleaning algorithms for data stream mining
* Adaptive, complex learning from rare and imbalanced data
* Architectures of data repositories for learning in complex and dynamic environments
* Data stream mining and processing over cloud infrastructures
* Applications requiring mining massive, complex and stream data

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IMPORTANT DATES
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Submissions of manuscripts due: March 18, 2013 (moved from February 28)
Author notification: May 26, 2013
Submission of revised manuscripts July 8, 2013
Final decisions September 28, 2013
Submission of final versions due: October 21, 2013
Intended publication date First half of 2014
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GUEST EDITORS
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Jerzy Stefanowski Poznań University of Technology, Poland
Alfredo Cuzzocrea ICAR-CNR and University of Calabria, Italy
Dominik Ślęzak University of Warsaw & Infobright Inc., Poland


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SUBMISSION FORMAT
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The submitted papers must be written in English and describe original research which is not published nor currently under review by other journals or conferences. Author guidelines for preparation of manuscript can be found at {http://www.elsevier.com/locate/ins/}
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SUBMISSION GUIDELINES
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All manuscripts and any supplementary material should be submitted through Elsevier Editorial System (EES). The authors must select as “Special Issue: Min. Compl. and Stream D.” when they reach the “Article Type” step in the submission process. The EES Web site for INS Information Sciences is available at: http://ees.elsevier.com/ins/

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CONTACT
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For more information, please contact:

Jerzy Stefanowski at Jerzy.Stefanowski@cs.put.poznan.pl (main guest editor)
Alfredo Cuzzocrea at cuzzocrea@si.deis.unical.it
Dominik Ślęzak at slezak@mimuw.edu.pl
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