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RealStream 2013 : Real-World Challenges for Data Stream Mining Workshop


When Sep 27, 2013 - Sep 27, 2013
Where Prague, Czech Republic
Submission Deadline Jul 5, 2013
Notification Due Jul 26, 2013
Final Version Due Aug 9, 2013
Categories    data mining   machine learning   data stream   applications

Call For Papers


Real-World Challenges for Data Stream Mining
Workshop-Discussion at ECMLPKDD 2013

September 27th, 2013, Prague, Czech Republic

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Data streams, online learning and adaptation to concept drift have become important research topics during the last decade. Data arrives in a stream in real time and needs to be mined in real time. In spite of the popularity of the research, truly autonomous, self-maintaining, adaptive data mining systems are rarely reported.
This workshop will provide a forum for researchers and practitioners to discuss real-world challenges for data stream mining, identify gaps between data streams research and meaningful applications, and define new application-relevant research directions for data stream mining.

The focus of this workshop is on presentations and discussions rather than on full written articles. Only extended abstracts (up to 4 pages in Springer LNCS format) are required as a submission and will be published in the online proceedings. The submission of works-in-progress, industrial experiences, as well as the presentation of works already published elsewhere is strongly encouraged. Well articulated position papers are welcome.

We invite contributions focusing on real world challenges for data stream mining. Topics include, but are not limited to:
1.Challenges and lessons learned from mining real-world data streams
2.Dealing with realistic data and workflows
- End user participation to varying degrees
- Interactive user feedback for adaptive learning
- Reliability / correctness of feedback
- Availability and delay of feedback
3.Integrating expert knowledge into data stream models
- What to ask of an expert?
- When to ask? How to set the priorities?
4.Moving from data stream algorithms towards data stream tools
- Online data preparation and pre-processing
- Improving usability and trust
- Developing autonomous, self-diagnosing data stream tools
5.Scalability of data stream mining systems

July 5, 2013: Extended abstract submission
July 26, 2013: Notification of acceptance
August 9, 2013: Camera-ready
September 27, 2013: Workshop date

Workshop organizers
Georg Krempl, KMD, Otto-von-Guericke-University Magdeburg, Germany
Indre Zliobaite, Aalto University, Finland
Yin Wang, Facebook, USA
George Forman, HP Labs, USA

Program Committee
Albert Bifet, Yahoo! Research, Spain
Joao Gama, LIAAD - INESC Porto, University Porto
T. Ryan Hoens, SAS Institute, USA
Petr Kadlec, Evonik Industries, Germany
Vincent Lemaire, Orange Labs, France
Fabian Moerchen, Amazon, USA
Mykola Pechenizkiy, TU Eindhoven, The Netherlands
Myra Spiliopoulou, KMD, Otto-von-Guericke-University Magdeburg, Germany
Alexey Tsymbal, Siemens, Germany
(to be finalized)

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