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 MEMuRY, a new procedure for Temporal Data Mining that preserves and mines additional time-oriented information. Our procedure is supported by SAPPERLOT, an interactive visual interface for exploring the patterns. Furthermore, we illustrate the efficiency of our procedure presenting a benchmark of the procedure's run-time behavior. A usage scenario shows how the procedure can provide new insights.
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