@article{lammarsch_mind_2014, title = {Mind the {Time}: {Unleashing} {Temporal} {Aspects} in {Pattern} {Discovery}}, volume = {38}, url = {http://publik.tuwien.ac.at/files/PubDat_220406.pdf}, doi = {10/f3szvj}, abstract = {Temporal Data Mining is a core concept of Knowledge Discovery in 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.}, journal = {Computers \& Graphics}, author = {Lammarsch, Tim and Aigner, Wolfgang and Bertone, Alessio and Miksch, Silvia and Rind, Alexander}, editor = {Jorge, Joaquim and Schuman, Heidrun and Pohl, Margit and Schulz, Hans-Jörg}, year = {2014}, note = {{\textless}br /{\textgreater} Projekt: KAVA-Time}, keywords = {FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Institut für Creative Media Technologies, KDD, Pattern Finding, Time-Oriented Data, Visual Computing, Wiss. Beitrag, best, data mining, interactive visualization, peer-reviewed, temporal data mining, visual analytics}, pages = {38--50}, }