The next, 17th ICAISC will take place in Zakopane in June 10-14, 2018,

Committee on Informatics of the Polish Academy of Sciences

Workshop: Data Stream Mining (SDM 2017)

The complex data frequently analysed in the literature are known under the name stream data. Stream data refers to the data that flows into and out the system like streams. Stream data is characterized by huge volumes of continuous data, possibly infinite, containing multidimensional features, fast changing and requiring fast, real-time responses. Moreover, the characteristics of the data stream can vary over time and the evolving pattern needs to be captured.

Data stream mining became recently a very challenging task in the data mining community. It can provide solution of problems in different fields such as engineering, healthcare, social networks, triafic analysys, detecting of the credit card fraud.

List of topics

Papers must present original work or review the state-of-the-art in the following non-exhaustive list of topics:

  • Incremental learning
  • Real-time algorithms
  • Adaptive learning
  • Big data analysis
  • Time series forecasting
  • Data streams classification
  • Data streams clustering
  • Concept drift detection
  • Ensemble methods
  • Application of data stream algorithms
  • Data streams management systems
All the submissions will be peer-reviewed with the same criteria used for other contributed papers.


Piotr Duda, Częstochowa University of Technology, Poland

Maciej Jaworski, Częstochowa University of Technology, Poland

Technical Program Committee

  • Cesare Alippi (Politecnico di Milano)
  • Plamen Angelov (Lancaster University)
  • Albert Bifet (University of Waikato)
  • Giacomo Boracchi (Politecnico di Milano)
  • Mohamed Gaber (Birmingham City University)
  • Joao Gama (University of Porto)
  • Bernhard Pfahringer (University of Waikato)
  • Manuel Roveri (Politecnico di Milano)
  • Jerzy Stefanowski (Poznan University of Technology)
  • Shiliang Sun (East China Normal University)