The capabilities to generate and collect data and information have seen an explosive growth and overwhelm traditional methods of data analysis such as spreadsheets, ad-hoc queries, or simple visualizations. Exploring trends, patterns, and relationships are particularly important when dealing with large amounts of data. The human perceptual system is highly sophisticated and specifically suited to spot visual patterns. For this reason, visualization is successfully applied in aiding these tasks. But facing the huge volumes of data to be analysed today, applying purely visual techniques only is often not sufficient. Time is an important data dimension that is common across many application domains. However, support for the analysis of time-oriented data and information is weak. The main reason is that time - in contrast to other quantitative data dimensions that are usually „flat” - has an inherent structure and distinct characteristics which increase its complexity dramatically and demand specialised methods in order to support proper analysis and visualisation. Especially, the problems imposed by the combination of multiple, heterogeneous data sources in real world scenarios push current techniques to their limits. Aim To fill these gaps within DisCo*, we aim to develop novel Visual Analytics methods to visually as well as computationally analyse multivariate, time-oriented data and information to discover new and unexpected trends, patterns, and relationships. The main goals of the intertwined visual and analytical methods are to ensure high usability and good control of the integrated mining techniques by applying intuitive visualizations and visual interfaces. * ... in Latin „disco” (inf. discere) means „I learn”


Duration 01/03/2007 - 30/05/2010
Funding FFG
Program FIT-IT Visual Computing
Project members
Peter Klinka
Mag. Michael Smuc


Bertone, A.; Lammarsch, T.; Turic, T.; Aigner, W.; Miksch, S.; Gärtner, J. (2010). MuTIny: A Multi-Time Interval Pattern Discovery Approach To Preserve The Temporal Information In Between. European Conference on Data Mining (ECDM'10)

Smuc, M.; Mayr, E.; Lammarsch, T.; Aigner, W.; Miksch, S.; Gärtner, J. (2009). To Score or Not to Score? Tripling Insights for Participatory Design. IEEE Computer Society Press, IEEE Computer Graphics and Applications

Bertone, A. (2007). A Matter of Time: Machine Learning and Temporal Data Mining . DUK

Bertone, A. (2007). Evaluation of SAX functionalities . DUK

Lammarsch, T. (2007). Toolkits für die Visualisierung. DUK


MuTIny: A Multi-time Interval Pattern Discovery Approach To Preserve The Temporal Information In Between

ECDM 2010 , 29/07/2010

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