IPMU 2016 Special Session

Computational Aspects of Data Aggregation and Complex Data Fusion

About the IPMU 2016 Conference

16th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems – IPMU 2016

June 20-24, 2016, Eindhoven, The Netherlands

The IPMU conference is organized every two years with the focus of bringing together scientists working on methods for the management of uncertainty and aggregation for the exchange of ideas between theoreticians and practitioners in these and related areas.

The proceedings of IPMU-2016 will be published in Communications in Computer and Information Science (CCIS) with Springer. Papers must be prepared in the LNCS/CCIS one-column page format. For more details please refer to the Instructions for the authors.

We would like to invite the researchers to submit their contributions to the special session entitled Computational Aspects of Data Aggregation and Complex Data Fusion.

Update#1 (2016-02-03). Paper submission is now closed.

Update#2 (2016-03-15). The special session will be included in the Final Program of IPMU 2016.

Description of the Special Session

Classically, the theory of aggregation focuses on mathematical aspects of data aggregation. This includes the construction and analysis of fusion functions that act on elements in the real line (means, t-norms, t-conorms, fuzzy implications), on chains, as well as other lattices.

This session is focused on data fusion tools for aggregating more complex objects – mostly their computational aspects, as in such a framework algorithms of interest are much more challenging. Special focus is on combining information from heterogeneous sources, big data aggregation, and computationally demanding complex data types.


Topics include, but are not limited to:
  • complex data fusion, including, but not limited to:
    • multidimensional real vectors
    • character strings (DNA sequences, etc.)
    • directional and spatial data
    • trees and other types of graphs
    • intervals and fuzzy quantities
    • rankings
    • time series
  • big data aggregation, parallel algorithms
  • algorithmic aspects of classical aggregation functions
  • record linkage between heterogeneous data sets
  • learning aggregation functions from data
  • applications:
    • bioinformatics
    • decision making
    • data analysis
    • clustering
    • computational statistics
    • bibliometrics
    • pattern recognition
    • plagiarism detection
    • automated spelling correction


Please feel free to send any questions or comments to the organizers.
  • Marek Gagolewski
    Systems Research Institute, Polish Academy of Sciences
    Warsaw, Poland
  • Bernard De Baets
    KERMIT, Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University
    Ghent, Belgium
  • Gleb Beliakov
    School of Information Technology
    Melbourne, Australia