The systems we are interested in modeling typically evolve in time and have clear non-stationary properties, demanding for the ability of handling regime shifts in the obtained models. Moreover, due to their dynamic nature, these systems produce a continuous stream of data, and thus the models need to be able to adapt to this recent information as time goes by.
The goal of this research line is to develop Data Mining techniques to extract useful knowledge from data that reflects a world in movement:
- Time and space. The objects of analysis exist in time and space. Often they are able to move.
- Dynamic environment. These objects exist in a dynamic and unstable environment, evolving incrementally over time.
- Information processing capability. The objects are endowed with information processing capabilities.
- Locality. The objects never see the global picture - they know only their local spatio-temporal environment.
- Real-Time. The models have to evolve incrementally in correspondence with the evolving environment.
- Distributed. The object will be able to exchange information with other objects, thus forming a truly distributed environment.
Current Projects
- MAESTRA - Learning from Massive, Incompletely annotated, and Structured Data
- Knowledge Discovery from Ubiquitous Data Streams (PTDC/EIA/098355/2008)
Current Activities:
- Organization of ECMLPKDD 2015, Porto
- Data Streams Track @ ACM SAC 2013/14/15
Members:
- João Gama (Coordinator)
Citations @ googlle scholar
Publications @ DBLP - João Moreira, Carlos Ferreira, Pedro Pereira Rodrigues, Raquel Sebastião, João Duarte, Vânia Almeida, Alexandre Carvalho
- Marcia Oliveira, Luis Matias, Mário Cordeiro, Hadi Fanaee, Rui Sarmento,
Thesis
- Carlos Ferreira; Probabilistic Sequential Pattern Mining; 2014
- Raquel Sebastião; Learning from Data Streams: Synopsis and Change Detection; 2014
- Petr Kosina; Decision Rule Learning for Evolving Data Streams; 2013
- Elena Ikonomovska; Algorithms For Learning Regression Trees and Ensembles on Evolving Data, JSI, October 2012
- Pedro Rodrigues; Learning from Ubiquitous Data Streams: Clustering Data and Data Sources, FC-UP, 2010 Best phd academic work [2011] Conet/ewsn phd dissertation award
