DSBD 2015 - Special Session on Data Stream Classification and Big Data Analytics (DSBD2015)

Technological developments of computers technology and its recent applications provide access to new types of data, which have not been considered in classical machine learning and pattern recognition algorithms. Two particularly interesting characteristics of such data sets include their huge size and streaming nature. Big data sets are now collected in many fields as, e.g., finance, business, medical systems, internet, biology and many other scientific research. Such data exceeds the processing capacity of conventional systems but there is a need for their real-time analysis and discovering unknown patterns. As standard methods cannot handle the complex structure and size of massive data, fulfill computational cost-effective requirements, these topics receive a growing interest both from academia and commercial companies. Moreover, data sets rapidly increase their size as they are often generated in a form of incoming streams. Sensor networks, monitoring, traffic management, telecommunication, or web log analysis are examples of such applications where machines working in dynamic environments continuously generate data. Compared to static environments the processing of data streams implies new requirements for algorithms, such as constraints on memory usage, restricted learning and testing time, and one scan of incoming instances. Another important aspect of learning classifiers from 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 learning algorithms. Furthermore learning classifiers and other predictive models from massive and stream data intersects with other related problems as handling class distributions, detecting rare cases or semi-supervised learning.
To address the research topics concerning both data stream classification and massive data analytics issues we organize a Special Session co-located within the framework of the 9th Computer Recognition Systems (CORES) 2015 Conference, which will be held from 25 to 27 May 2015 in Wroclaw, Poland. The main aim of this session is to gather researchers interested in these issues and to demonstrate results in these and related areas of mining such difficult data. Original and high-quality novel research dedicated to both theory and applications of big and stream data processing algorithms are invited.
Scope of the Special Session
The topics of this Special Session are concentrated on (but not limited to) following themes:
Incremental online learning algorithms
Detection and adaptation to the presence of the concept drift
One-class classification and novelty detection
Imbalanced classification
Discovery, detection and classification of complex patterns in massive or evolving data
Semi-supervised approaches and active learning paradigms
Scaling-up learning algorithms
Near real-time analysis of massive data
Integration and fusion of heterogeneous data structures and streams
Distributed and parallel computing systems for big data analytics
Problem of privacy in big and stream data
Applications to real-life problems from medicine, bioinformatics, multimedia, sensors, social networks and related domains
Special Session Organisers
Bartosz Krawczyk, Wrocław University of Technology, bartosz.krawczyk-AT-pwr.wroc.pl
Jerzy Stefanowski, Poznań University of Technology, jerzy.stefanowski-AT-cs.put.poznan.pl
Michał Woźniak, Wrocław University of Technology, michal.wozniak-AT-pwr.wroc.pl