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MLBD 2020 : Machine Learning for Big Data track of the 34th International ECMS Conference on Modelling and Simulation

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Link: http://www.scs-europe.net/conf/ecms2020/mlbd.html
 
When Jun 9, 2020 - Jun 12, 2020
Where Wildau near Berlin, Germany
Submission Deadline Feb 24, 2020
Notification Due Mar 30, 2020
Final Version Due Apr 24, 2020
Categories    machine learning   data mining   data science
 

Call For Papers

This Track’s area of interest is on the Volume and Velocity dimensions of Big Data. Volume, referring to the size of the data, Velocity, referring to the data that is generated rapidly and needs to be analysed in real-time. The workshop accepts papers on the application of Machine Learning and Data Mining Algorithms on large, complex or data generated in real-time. Applications are for example Simulation monitoring, Time Series Analysis, Network Intrusion Detection, Health Monitoring, Trend Detection in Twitter, Financial Monitoring, etc. The conference track also encourages the submission of papers that introduce new techniques, algorithms, systems and workflows, for large quantities of data, data streams and/or Time Series Analysis.

Track-Chair:
Dr. Frederic Stahl, (German Research Center for Artificial Intelligence (DFKI), University of Reading UK)
Track-Co-Chairs:
Professor Dr. Mohamed Gaber (Birmingham City University, UK)
Dr. Marwan Hassani (Eindhoven University of Technology, Netherlands)

Topics of interest include but are not limited to:

* Data Mining and Machine Learning algorithms, models and techniques.
* Data Mining and Machine Learning applications.
* Data Mining and Machine Learning on simulation data.
* Data Mining of Big Data streams
* Data Mining of large quantities of data
* Explainable Data Mining models
* Scalability of Data Mining techniques
* Real-time data stream analytics of simulation data
* Data stream analytics systems
* Concept Drift Detection techniques
* Outlier Detection
* Signal Processing and Analytics
* Visualisation of real-time data streams
* Analytics of IoT data streams

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