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T. Lammarsch, W. Aigner, S. Miksch, A. Bertone, A. Rind:
"Mind the Time: Unleashing Temporal Aspects in Pattern Discovery";
Computers & Graphics, Special Issue on Visual Analytics (2014), 38; 38 - 50.

Databases handling time-oriented data. State-of-the-art methods are capable of preserving the temporal order of events as well as the temporal intervals in between. The temporal characteristics of the events themselves, however, can likely lead to numerous uninteresting patterns found by current approaches. We present a new definition of the temporal characteristics of events and enhance related work for pattern finding by utilizing temporal relations, like meets, starts, or during, instead of just intervals between events. These prerequisites result in a new procedure for Temporal Data Mining that preserves and mines additional time-oriented information. Our procedure is supported by an interactive visual interface for exploring the patterns. Furthermore, we illustrate the effciency of our procedure presenting an benchmark of the procedure´s run-time behavior. A usage scenario shows how the procedure can provide new insights.

Data Mining, Interactive Visualization, KDD, Pattern Finding, Temporal Data Mining, Time-Oriented Data, Visual Analytics

http://dx.doi.org/10.1016/j.cag.2013.10.007

http://publik.tuwien.ac.at/files/PubDat_220406.pdf

Project Head Silvia Miksch:
HypoVis: Modellierung von Hypothesen mit Visual Analytics Methoden zur Analyse der Vergangenheit und der Vorhersage der Zukunft