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