ISPM 2014 - Special Issue of Information Sciences on Processing and Mining Complex Stream Data

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 We solicit original and unpublished contributions in all topics covering these data mining tasks.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
IMPORTANT DATES
Submissions of manuscripts due: February 28, 2013
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: Apr 01 2014
GUEST EDITORS
Jerzy Stefanowski Poznań University of Technology, Poland
Alfredo Cuzzocrea ICAR-CNR and University of Calabria, Italy
Dominik Ślęzak University of Warsaw & Infobright Inc., Poland
SUBMISSION FORMAT
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/
SUBMISSION GUIDELINES
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/
CONTACT
For more information, please contact:
Jerzy Stefanowski at Jerzy.Stefanowski-AT-cs.put.poznan.pl
Alfredo Cuzzocrea at cuzzocrea-AT-si.deis.unical.it
Dominik Ślęzak at slezak-AT-mimuw.edu.pl